From 9f95895ec91ff5897baed8acb9f328afc56516f6 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:07:14 +0100 Subject: [PATCH 01/45] Add files via upload --- rrt_algorithms/utilities/fuzy_logic.py | 61 +++++ .../utilities/obstacle_generation1.py | 64 +++++ .../utilities/obstacle_generation2.py | 65 +++++ rrt_algorithms/utilities/plotting.py | 212 ++++++++-------- rrt_algorithms/utilities/plotting0.py | 235 +++++++++++++++++ rrt_algorithms/utilities/plotting1.py | 239 ++++++++++++++++++ rrt_algorithms/utilities/plotting2.py | 239 ++++++++++++++++++ 7 files changed, 1016 insertions(+), 99 deletions(-) create mode 100644 rrt_algorithms/utilities/fuzy_logic.py create mode 100644 rrt_algorithms/utilities/obstacle_generation1.py create mode 100644 rrt_algorithms/utilities/obstacle_generation2.py create mode 100644 rrt_algorithms/utilities/plotting0.py create mode 100644 rrt_algorithms/utilities/plotting1.py create mode 100644 rrt_algorithms/utilities/plotting2.py diff --git a/rrt_algorithms/utilities/fuzy_logic.py b/rrt_algorithms/utilities/fuzy_logic.py new file mode 100644 index 0000000..d78576a --- /dev/null +++ b/rrt_algorithms/utilities/fuzy_logic.py @@ -0,0 +1,61 @@ +import numpy as np +import skfuzzy as fuzz +from skfuzzy import control as ctrl + +class FuzzyController: + def __init__(self): + # Define fuzzy variables + self.dist_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'dist_to_goal') + self.dist_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'dist_to_obstacle') + + self.to_goal_gain = ctrl.Consequent(np.arange(0, 2.1, 0.1), 'to_goal_gain') + self.speed_gain = ctrl.Consequent(np.arange(0, 2.1, 0.1), 'speed_gain') + self.obstacle_gain = ctrl.Consequent(np.arange(0, 2.1, 0.1), 'obstacle_gain') + + # Membership functions for distance to goal + self.dist_to_goal['close'] = fuzz.trimf(self.dist_to_goal.universe, [0, 0, 30]) + self.dist_to_goal['medium'] = fuzz.trimf(self.dist_to_goal.universe, [20, 50, 80]) + self.dist_to_goal['far'] = fuzz.trimf(self.dist_to_goal.universe, [70, 100, 100]) + + # Membership functions for distance to obstacles + self.dist_to_obstacle['close'] = fuzz.trimf(self.dist_to_obstacle.universe, [0, 0, 30]) + self.dist_to_obstacle['medium'] = fuzz.trimf(self.dist_to_obstacle.universe, [20, 50, 80]) + self.dist_to_obstacle['far'] = fuzz.trimf(self.dist_to_obstacle.universe, [70, 100, 100]) + + # Membership functions for gain adjustments + self.to_goal_gain['low'] = fuzz.trimf(self.to_goal_gain.universe, [0, 0, 1]) + self.to_goal_gain['medium'] = fuzz.trimf(self.to_goal_gain.universe, [0.5, 1.0, 1.5]) + self.to_goal_gain['high'] = fuzz.trimf(self.to_goal_gain.universe, [1.0, 2.0, 2.0]) + + self.speed_gain['low'] = fuzz.trimf(self.speed_gain.universe, [0, 0, 1]) + self.speed_gain['medium'] = fuzz.trimf(self.speed_gain.universe, [0.5, 1.0, 1.5]) + self.speed_gain['high'] = fuzz.trimf(self.speed_gain.universe, [1.0, 2.0, 2.0]) + + self.obstacle_gain['low'] = fuzz.trimf(self.obstacle_gain.universe, [0, 0, 1]) + self.obstacle_gain['medium'] = fuzz.trimf(self.obstacle_gain.universe, [0.5, 1.0, 1.5]) + self.obstacle_gain['high'] = fuzz.trimf(self.obstacle_gain.universe, [1.0, 2.0, 2.0]) + + # Define fuzzy rules + self.rule1 = ctrl.Rule(self.dist_to_goal['close'] & self.dist_to_obstacle['close'], + [self.to_goal_gain['low'], self.speed_gain['medium'], self.obstacle_gain['high']]) + self.rule2 = ctrl.Rule(self.dist_to_goal['medium'] & self.dist_to_obstacle['medium'], + [self.to_goal_gain['medium'], self.speed_gain['medium'], self.obstacle_gain['medium']]) + self.rule3 = ctrl.Rule(self.dist_to_goal['far'] & self.dist_to_obstacle['far'], + [self.to_goal_gain['high'], self.speed_gain['high'], self.obstacle_gain['low']]) + self.rule4 = ctrl.Rule(self.dist_to_goal['far'] & self.dist_to_obstacle['close'], + [self.to_goal_gain['medium'], self.speed_gain['low'], self.obstacle_gain['high']]) + self.rule5 = ctrl.Rule(self.dist_to_goal['close'] & self.dist_to_obstacle['far'], + [self.to_goal_gain['high'], self.speed_gain['high'], self.obstacle_gain['low']]) + + self.control_system = ctrl.ControlSystem([self.rule1, self.rule2, self.rule3, self.rule4, self.rule5]) + self.simulation = ctrl.ControlSystemSimulation(self.control_system) + + def compute_gains(self, dist_to_goal, dist_to_obstacle): + self.simulation.input['dist_to_goal'] = dist_to_goal + self.simulation.input['dist_to_obstacle'] = dist_to_obstacle + self.simulation.compute() + return (self.simulation.output['to_goal_gain'], + self.simulation.output['speed_gain'], + self.simulation.output['obstacle_gain']) + + diff --git a/rrt_algorithms/utilities/obstacle_generation1.py b/rrt_algorithms/utilities/obstacle_generation1.py new file mode 100644 index 0000000..9acb30a --- /dev/null +++ b/rrt_algorithms/utilities/obstacle_generation1.py @@ -0,0 +1,64 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +from __future__ import annotations + +import random +import uuid + +import numpy as np + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from rrt_algorithms.search_space.search_space1 import SearchSpace + + + +def generate_random_cylindrical_obstacles(X: SearchSpace, start, end, n): + """ + Generates n random cylindrical obstacles without disrupting world connectivity. + It also respects start and end points so that they don't lie inside an obstacle. + """ + i = 0 + obstacles = [] + while i < n: + scollision = True + fcollision = True + + x_center = random.uniform(X.dimension_lengths[0][0], X.dimension_lengths[0][1]) + y_center = random.uniform(X.dimension_lengths[1][0], X.dimension_lengths[1][1]) + z_min = random.uniform(X.dimension_lengths[2][0], X.dimension_lengths[2][1] - 10) # Ensure some height + z_max = z_min + random.uniform(5, 15) # Give a random height between 5 and 15 + radius = random.uniform(2, 10) # Random radius between 2 and 10 + + if np.linalg.norm([start[0] - x_center, start[1] - y_center]) > radius: + scollision = False + if np.linalg.norm([end[0] - x_center, end[1] - y_center]) > radius: + fcollision = False + + obstacle = (x_center, y_center, z_min, z_max, radius) + + if scollision or fcollision: + continue + + obstacles.append(obstacle) + X.obs.insert(uuid.uuid4().int, X.compute_aabb(obstacle), X.compute_aabb(obstacle)) + i += 1 + + return obstacles + + + +def obstacle_generator(obstacles): + """ + Add obstacles to r-tree + :param obstacles: list of obstacles + """ + for obstacle in obstacles: + yield (uuid.uuid4().int, obstacle, obstacle) diff --git a/rrt_algorithms/utilities/obstacle_generation2.py b/rrt_algorithms/utilities/obstacle_generation2.py new file mode 100644 index 0000000..e790dd9 --- /dev/null +++ b/rrt_algorithms/utilities/obstacle_generation2.py @@ -0,0 +1,65 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +from __future__ import annotations + +import random +import uuid + +import numpy as np + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from rrt_algorithms.search_space.search_space2 import SearchSpace + + +def generate_random_cylindrical_obstacles(X: SearchSpace, start: Tuple[float, float, float], end: Tuple[float, float, float], n: int) -> List[Tuple[float, float, float, float, float]]: + """ + Generates n random cylindrical obstacles without disrupting world connectivity. + It also respects start and end points so that they don't lie inside an obstacle. + """ + obstacles = [] + i = 0 + while i < n: + scollision = True + fcollision = True + + x_center = random.uniform(X.dimension_lengths[0][0], X.dimension_lengths[0][1]) + y_center = random.uniform(X.dimension_lengths[1][0], X.dimension_lengths[1][1]) + z_min = random.uniform(X.dimension_lengths[2][0], X.dimension_lengths[2][1] - 10) # Ensure some height + z_max = z_min + random.uniform(5, 15) # Give a random height between 5 and 15 + radius = random.uniform(2, 10) # Random radius between 2 and 10 + + if np.linalg.norm([start[0] - x_center, start[1] - y_center]) > radius: + scollision = False + if np.linalg.norm([end[0] - x_center, end[1] - y_center]) > radius: + fcollision = False + + obstacle = (x_center, y_center, z_min, z_max, radius) + + if scollision or fcollision: + continue + + obstacles.append(obstacle) + X.obstacles.append(obstacle) # Add to the SearchSpace's obstacles list + i += 1 + + # Rebuild the BVH with the new obstacles + X.build_bvh() + + return obstacles + + +def obstacle_generator(obstacles): + """ + Add obstacles to r-tree + :param obstacles: list of obstacles + """ + for obstacle in obstacles: + yield (uuid.uuid4().int, obstacle, obstacle) diff --git a/rrt_algorithms/utilities/plotting.py b/rrt_algorithms/utilities/plotting.py index 86e149e..87eafcb 100644 --- a/rrt_algorithms/utilities/plotting.py +++ b/rrt_algorithms/utilities/plotting.py @@ -1,13 +1,10 @@ -# This file is subject to the terms and conditions defined in -# file 'LICENSE', which is part of this source code package. from pathlib import Path - import plotly as py from plotly import graph_objs as go +import numpy as np colors = ['darkblue', 'teal'] - class Plot(object): def __init__(self, filename): """ @@ -20,8 +17,7 @@ def __init__(self, filename): self.filename = str(self.filename) self.data = [] self.layout = {'title': 'Plot', - 'showlegend': False - } + 'showlegend': False} self.fig = {'data': self.data, 'layout': self.layout} @@ -76,52 +72,111 @@ def plot_tree_3d(self, trees): ) self.data.append(trace) - def plot_obstacles(self, X, O): + def plot_obstacles(self, X, obstacles): + """ + Plot obstacles in the search space. + :param X: Search Space + :param obstacles: list of obstacles (cylindrical in this case) + """ + if X.dimensions == 3: # plot in 3D + for obstacle in obstacles: + x_center, y_center, z_min, z_max, radius = obstacle + + # Create the mesh grid for the cylindrical surface + z = np.linspace(z_min, z_max, 50) + theta = np.linspace(0, 2 * np.pi, 50) + theta_grid, z_grid = np.meshgrid(theta, z) + x_grid = x_center + radius * np.cos(theta_grid) + y_grid = y_center + radius * np.sin(theta_grid) + + # Plot the cylindrical surface + obs = go.Surface( + x=x_grid, + y=y_grid, + z=z_grid, + colorscale='Viridis', + opacity=0.7 + ) + self.data.append(obs) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def draw(self, auto_open=True): + """ + Render the plot to a file + """ + py.offline.plot(self.fig, filename=self.filename, auto_open=auto_open) + def plot_start(self, X, x_init): """ - Plot obstacles + Plot starting point :param X: Search Space - :param O: list of obstacles + :param x_init: starting location """ if X.dimensions == 2: # plot in 2D - self.layout['shapes'] = [] - for O_i in O: - # noinspection PyUnresolvedReferences - self.layout['shapes'].append( - { - 'type': 'rect', - 'x0': O_i[0], - 'y0': O_i[1], - 'x1': O_i[2], - 'y1': O_i[3], - 'line': { - 'color': 'purple', - 'width': 4, - }, - 'fillcolor': 'purple', - 'opacity': 0.70 - }, - ) + trace = go.Scatter( + x=[x_init[0]], + y=[x_init[1]], + marker=dict( + color="orange", + size=10 + ), + mode="markers" + ) + self.data.append(trace) elif X.dimensions == 3: # plot in 3D - for O_i in O: - obs = go.Mesh3d( - x=[O_i[0], O_i[0], O_i[3], O_i[3], O_i[0], O_i[0], O_i[3], O_i[3]], - y=[O_i[1], O_i[4], O_i[4], O_i[1], O_i[1], O_i[4], O_i[4], O_i[1]], - z=[O_i[2], O_i[2], O_i[2], O_i[2], O_i[5], O_i[5], O_i[5], O_i[5]], - i=[7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2], - j=[3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3], - k=[0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6], - color='purple', - opacity=0.70 - ) - self.data.append(obs) + trace = go.Scatter3d( + x=[x_init[0]], + y=[x_init[1]], + z=[x_init[2]], + marker=dict( + color="orange", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_goal(self, X, x_goal, color="green"): + """ + Plot goal point + :param X: Search Space + :param x_goal: goal location + :param color: color of the goal point + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_goal[0]], + y=[x_goal[1]], + marker=dict( + color=color, + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_goal[0]], + y=[x_goal[1]], + z=[x_goal[2]], + marker=dict( + color=color, + size=10 + ), + mode="markers" + ) + self.data.append(trace) else: # can't plot in higher dimensions print("Cannot plot in > 3 dimensions") - def plot_path(self, X, path): + def plot_path(self, X, path, color='red'): """ Plot path through Search Space :param X: Search Space :param path: path through space given as a sequence of points + :param color: color of the path to be plotted """ if X.dimensions == 2: # plot in 2D x, y = [], [] @@ -132,7 +187,7 @@ def plot_path(self, X, path): x=x, y=y, line=dict( - color="red", + color=color, width=4 ), mode="lines" @@ -150,86 +205,45 @@ def plot_path(self, X, path): y=y, z=z, line=dict( - color="red", + color=color, width=4 ), mode="lines" ) self.data.append(trace) - else: # can't plot in higher dimensions + else: print("Cannot plot in > 3 dimensions") - def plot_start(self, X, x_init): - """ - Plot starting point - :param X: Search Space - :param x_init: starting location - """ - if X.dimensions == 2: # plot in 2D - trace = go.Scatter( - x=[x_init[0]], - y=[x_init[1]], - line=dict( - color="orange", - width=10 - ), - mode="markers" - ) - self.data.append(trace) - elif X.dimensions == 3: # plot in 3D - trace = go.Scatter3d( - x=[x_init[0]], - y=[x_init[1]], - z=[x_init[2]], - line=dict( - color="orange", - width=10 - ), - mode="markers" - ) - - self.data.append(trace) - else: # can't plot in higher dimensions - print("Cannot plot in > 3 dimensions") - - def plot_goal(self, X, x_goal): + def plot_intermediate_goal(self, X, x_intermediate): """ - Plot goal point + Plot intermediate goal point :param X: Search Space - :param x_goal: goal location + :param x_intermediate: intermediate goal location """ if X.dimensions == 2: # plot in 2D trace = go.Scatter( - x=[x_goal[0]], - y=[x_goal[1]], - line=dict( - color="green", - width=10 + x=[x_intermediate[0]], + y=[x_intermediate[1]], + marker=dict( + color="pink", + size=10 ), mode="markers" ) - self.data.append(trace) elif X.dimensions == 3: # plot in 3D trace = go.Scatter3d( - x=[x_goal[0]], - y=[x_goal[1]], - z=[x_goal[2]], - line=dict( - color="green", - width=10 + x=[x_intermediate[0]], + y=[x_intermediate[1]], + z=[x_intermediate[2]], + marker=dict( + color="pink", + size=10 ), mode="markers" ) - self.data.append(trace) else: # can't plot in higher dimensions - print("Cannot plot in > 3 dimensions") - - def draw(self, auto_open=True): - """ - Render the plot to a file - """ - py.offline.plot(self.fig, filename=self.filename, auto_open=auto_open) + print("Cannot plot in > 3 dimensions") diff --git a/rrt_algorithms/utilities/plotting0.py b/rrt_algorithms/utilities/plotting0.py new file mode 100644 index 0000000..86e149e --- /dev/null +++ b/rrt_algorithms/utilities/plotting0.py @@ -0,0 +1,235 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +from pathlib import Path + +import plotly as py +from plotly import graph_objs as go + +colors = ['darkblue', 'teal'] + + +class Plot(object): + def __init__(self, filename): + """ + Create a plot + :param filename: filename + """ + self.filename = Path(__file__).parent / "../../output/visualizations/" / f"{filename}.html" + if not self.filename.parent.exists(): + self.filename.parent.mkdir(parents=True, exist_ok=True) + self.filename = str(self.filename) + self.data = [] + self.layout = {'title': 'Plot', + 'showlegend': False + } + + self.fig = {'data': self.data, + 'layout': self.layout} + + def plot_tree(self, X, trees): + """ + Plot tree + :param X: Search Space + :param trees: list of trees + """ + if X.dimensions == 2: # plot in 2D + self.plot_tree_2d(trees) + elif X.dimensions == 3: # plot in 3D + self.plot_tree_3d(trees) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_tree_2d(self, trees): + """ + Plot 2D trees + :param trees: trees to plot + """ + for i, tree in enumerate(trees): + for start, end in tree.E.items(): + if end is not None: + trace = go.Scatter( + x=[start[0], end[0]], + y=[start[1], end[1]], + line=dict( + color=colors[i] + ), + mode="lines" + ) + self.data.append(trace) + + def plot_tree_3d(self, trees): + """ + Plot 3D trees + :param trees: trees to plot + """ + for i, tree in enumerate(trees): + for start, end in tree.E.items(): + if end is not None: + trace = go.Scatter3d( + x=[start[0], end[0]], + y=[start[1], end[1]], + z=[start[2], end[2]], + line=dict( + color=colors[i] + ), + mode="lines" + ) + self.data.append(trace) + + def plot_obstacles(self, X, O): + """ + Plot obstacles + :param X: Search Space + :param O: list of obstacles + """ + if X.dimensions == 2: # plot in 2D + self.layout['shapes'] = [] + for O_i in O: + # noinspection PyUnresolvedReferences + self.layout['shapes'].append( + { + 'type': 'rect', + 'x0': O_i[0], + 'y0': O_i[1], + 'x1': O_i[2], + 'y1': O_i[3], + 'line': { + 'color': 'purple', + 'width': 4, + }, + 'fillcolor': 'purple', + 'opacity': 0.70 + }, + ) + elif X.dimensions == 3: # plot in 3D + for O_i in O: + obs = go.Mesh3d( + x=[O_i[0], O_i[0], O_i[3], O_i[3], O_i[0], O_i[0], O_i[3], O_i[3]], + y=[O_i[1], O_i[4], O_i[4], O_i[1], O_i[1], O_i[4], O_i[4], O_i[1]], + z=[O_i[2], O_i[2], O_i[2], O_i[2], O_i[5], O_i[5], O_i[5], O_i[5]], + i=[7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2], + j=[3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3], + k=[0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6], + color='purple', + opacity=0.70 + ) + self.data.append(obs) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_path(self, X, path): + """ + Plot path through Search Space + :param X: Search Space + :param path: path through space given as a sequence of points + """ + if X.dimensions == 2: # plot in 2D + x, y = [], [] + for i in path: + x.append(i[0]) + y.append(i[1]) + trace = go.Scatter( + x=x, + y=y, + line=dict( + color="red", + width=4 + ), + mode="lines" + ) + + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + x, y, z = [], [], [] + for i in path: + x.append(i[0]) + y.append(i[1]) + z.append(i[2]) + trace = go.Scatter3d( + x=x, + y=y, + z=z, + line=dict( + color="red", + width=4 + ), + mode="lines" + ) + + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_start(self, X, x_init): + """ + Plot starting point + :param X: Search Space + :param x_init: starting location + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_init[0]], + y=[x_init[1]], + line=dict( + color="orange", + width=10 + ), + mode="markers" + ) + + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_init[0]], + y=[x_init[1]], + z=[x_init[2]], + line=dict( + color="orange", + width=10 + ), + mode="markers" + ) + + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_goal(self, X, x_goal): + """ + Plot goal point + :param X: Search Space + :param x_goal: goal location + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_goal[0]], + y=[x_goal[1]], + line=dict( + color="green", + width=10 + ), + mode="markers" + ) + + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_goal[0]], + y=[x_goal[1]], + z=[x_goal[2]], + line=dict( + color="green", + width=10 + ), + mode="markers" + ) + + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def draw(self, auto_open=True): + """ + Render the plot to a file + """ + py.offline.plot(self.fig, filename=self.filename, auto_open=auto_open) diff --git a/rrt_algorithms/utilities/plotting1.py b/rrt_algorithms/utilities/plotting1.py new file mode 100644 index 0000000..ffb485b --- /dev/null +++ b/rrt_algorithms/utilities/plotting1.py @@ -0,0 +1,239 @@ +from pathlib import Path +import plotly as py +from plotly import graph_objs as go +import numpy as np + +colors = ['darkblue', 'teal'] + +class Plot(object): + def __init__(self, filename): + """ + Create a plot + :param filename: filename + """ + self.filename = Path(__file__).parent / "../../output/visualizations/" / f"{filename}.html" + if not self.filename.parent.exists(): + self.filename.parent.mkdir(parents=True, exist_ok=True) + self.filename = str(self.filename) + self.data = [] + self.layout = {'title': 'Plot', + 'showlegend': False} + + self.fig = {'data': self.data, + 'layout': self.layout} + + def plot_tree(self, X, trees): + """ + Plot tree + :param X: Search Space + :param trees: list of trees + """ + if X.dimensions == 2: # plot in 2D + self.plot_tree_2d(trees) + elif X.dimensions == 3: # plot in 3D + self.plot_tree_3d(trees) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_tree_2d(self, trees): + """ + Plot 2D trees + :param trees: trees to plot + """ + for i, tree in enumerate(trees): + for start, end in tree.E.items(): + if end is not None: + trace = go.Scatter( + x=[start[0], end[0]], + y=[start[1], end[1]], + line=dict( + color=colors[i] + ), + mode="lines" + ) + self.data.append(trace) + + def plot_tree_3d(self, trees): + """ + Plot 3D trees + :param trees: trees to plot + """ + for i, tree in enumerate(trees): + for start, end in tree.E.items(): + if end is not None: + trace = go.Scatter3d( + x=[start[0], end[0]], + y=[start[1], end[1]], + z=[start[2], end[2]], + line=dict( + color=colors[i] + ), + mode="lines" + ) + self.data.append(trace) + + def plot_obstacles(self, X, obstacles): + if X.dimensions == 3: # Plot in 3D + for obstacle in obstacles: + x_center, y_center, z_min, z_max, radius = obstacle + + z = np.linspace(z_min, z_max, 50) + theta = np.linspace(0, 2 * np.pi, 50) + theta_grid, z_grid = np.meshgrid(theta, z) + x_grid = x_center + radius * np.cos(theta_grid) + y_grid = y_center + radius * np.sin(theta_grid) + + obs = go.Surface( + x=x_grid, + y=y_grid, + z=z_grid, + colorscale='Viridis', + opacity=0.7 + ) + self.data.append(obs) + def draw(self, auto_open=True): + """ + Render the plot to a file + """ + py.offline.plot(self.fig, filename=self.filename, auto_open=auto_open) + def plot_start(self, X, x_init): + """ + Plot starting point + :param X: Search Space + :param x_init: starting location + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_init[0]], + y=[x_init[1]], + marker=dict( + color="orange", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_init[0]], + y=[x_init[1]], + z=[x_init[2]], + marker=dict( + color="orange", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_goal(self, X, x_goal, color="green"): + """ + Plot goal point + :param X: Search Space + :param x_goal: goal location + :param color: color of the goal point + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_goal[0]], + y=[x_goal[1]], + marker=dict( + color=color, + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_goal[0]], + y=[x_goal[1]], + z=[x_goal[2]], + marker=dict( + color=color, + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_path(self, X, path, color='red'): + """ + Plot path through Search Space + :param X: Search Space + :param path: path through space given as a sequence of points + :param color: color of the path to be plotted + """ + if X.dimensions == 2: # plot in 2D + x, y = [], [] + for i in path: + x.append(i[0]) + y.append(i[1]) + trace = go.Scatter( + x=x, + y=y, + line=dict( + color=color, + width=4 + ), + mode="lines" + ) + + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + x, y, z = [], [], [] + for i in path: + x.append(i[0]) + y.append(i[1]) + z.append(i[2]) + trace = go.Scatter3d( + x=x, + y=y, + z=z, + line=dict( + color=color, + width=4 + ), + mode="lines" + ) + + self.data.append(trace) + else: + print("Cannot plot in > 3 dimensions") + + + def plot_intermediate_goal(self, X, x_intermediate): + """ + Plot intermediate goal point + :param X: Search Space + :param x_intermediate: intermediate goal location + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_intermediate[0]], + y=[x_intermediate[1]], + marker=dict( + color="pink", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_intermediate[0]], + y=[x_intermediate[1]], + z=[x_intermediate[2]], + marker=dict( + color="pink", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") diff --git a/rrt_algorithms/utilities/plotting2.py b/rrt_algorithms/utilities/plotting2.py new file mode 100644 index 0000000..ffb485b --- /dev/null +++ b/rrt_algorithms/utilities/plotting2.py @@ -0,0 +1,239 @@ +from pathlib import Path +import plotly as py +from plotly import graph_objs as go +import numpy as np + +colors = ['darkblue', 'teal'] + +class Plot(object): + def __init__(self, filename): + """ + Create a plot + :param filename: filename + """ + self.filename = Path(__file__).parent / "../../output/visualizations/" / f"{filename}.html" + if not self.filename.parent.exists(): + self.filename.parent.mkdir(parents=True, exist_ok=True) + self.filename = str(self.filename) + self.data = [] + self.layout = {'title': 'Plot', + 'showlegend': False} + + self.fig = {'data': self.data, + 'layout': self.layout} + + def plot_tree(self, X, trees): + """ + Plot tree + :param X: Search Space + :param trees: list of trees + """ + if X.dimensions == 2: # plot in 2D + self.plot_tree_2d(trees) + elif X.dimensions == 3: # plot in 3D + self.plot_tree_3d(trees) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_tree_2d(self, trees): + """ + Plot 2D trees + :param trees: trees to plot + """ + for i, tree in enumerate(trees): + for start, end in tree.E.items(): + if end is not None: + trace = go.Scatter( + x=[start[0], end[0]], + y=[start[1], end[1]], + line=dict( + color=colors[i] + ), + mode="lines" + ) + self.data.append(trace) + + def plot_tree_3d(self, trees): + """ + Plot 3D trees + :param trees: trees to plot + """ + for i, tree in enumerate(trees): + for start, end in tree.E.items(): + if end is not None: + trace = go.Scatter3d( + x=[start[0], end[0]], + y=[start[1], end[1]], + z=[start[2], end[2]], + line=dict( + color=colors[i] + ), + mode="lines" + ) + self.data.append(trace) + + def plot_obstacles(self, X, obstacles): + if X.dimensions == 3: # Plot in 3D + for obstacle in obstacles: + x_center, y_center, z_min, z_max, radius = obstacle + + z = np.linspace(z_min, z_max, 50) + theta = np.linspace(0, 2 * np.pi, 50) + theta_grid, z_grid = np.meshgrid(theta, z) + x_grid = x_center + radius * np.cos(theta_grid) + y_grid = y_center + radius * np.sin(theta_grid) + + obs = go.Surface( + x=x_grid, + y=y_grid, + z=z_grid, + colorscale='Viridis', + opacity=0.7 + ) + self.data.append(obs) + def draw(self, auto_open=True): + """ + Render the plot to a file + """ + py.offline.plot(self.fig, filename=self.filename, auto_open=auto_open) + def plot_start(self, X, x_init): + """ + Plot starting point + :param X: Search Space + :param x_init: starting location + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_init[0]], + y=[x_init[1]], + marker=dict( + color="orange", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_init[0]], + y=[x_init[1]], + z=[x_init[2]], + marker=dict( + color="orange", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_goal(self, X, x_goal, color="green"): + """ + Plot goal point + :param X: Search Space + :param x_goal: goal location + :param color: color of the goal point + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_goal[0]], + y=[x_goal[1]], + marker=dict( + color=color, + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_goal[0]], + y=[x_goal[1]], + z=[x_goal[2]], + marker=dict( + color=color, + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") + + def plot_path(self, X, path, color='red'): + """ + Plot path through Search Space + :param X: Search Space + :param path: path through space given as a sequence of points + :param color: color of the path to be plotted + """ + if X.dimensions == 2: # plot in 2D + x, y = [], [] + for i in path: + x.append(i[0]) + y.append(i[1]) + trace = go.Scatter( + x=x, + y=y, + line=dict( + color=color, + width=4 + ), + mode="lines" + ) + + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + x, y, z = [], [], [] + for i in path: + x.append(i[0]) + y.append(i[1]) + z.append(i[2]) + trace = go.Scatter3d( + x=x, + y=y, + z=z, + line=dict( + color=color, + width=4 + ), + mode="lines" + ) + + self.data.append(trace) + else: + print("Cannot plot in > 3 dimensions") + + + def plot_intermediate_goal(self, X, x_intermediate): + """ + Plot intermediate goal point + :param X: Search Space + :param x_intermediate: intermediate goal location + """ + if X.dimensions == 2: # plot in 2D + trace = go.Scatter( + x=[x_intermediate[0]], + y=[x_intermediate[1]], + marker=dict( + color="pink", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + elif X.dimensions == 3: # plot in 3D + trace = go.Scatter3d( + x=[x_intermediate[0]], + y=[x_intermediate[1]], + z=[x_intermediate[2]], + marker=dict( + color="pink", + size=10 + ), + mode="markers" + ) + self.data.append(trace) + else: # can't plot in higher dimensions + print("Cannot plot in > 3 dimensions") From 46c0ac3352ef22e7ac1da0c6009366cb9d8affce Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:07:49 +0100 Subject: [PATCH 02/45] Add files via upload --- rrt_algorithms/search_space/search_space.py | 110 ++++++++-------- rrt_algorithms/search_space/search_space0.py | 77 +++++++++++ rrt_algorithms/search_space/search_space1.py | 116 ++++++++++++++++ rrt_algorithms/search_space/search_space2.py | 131 +++++++++++++++++++ 4 files changed, 381 insertions(+), 53 deletions(-) create mode 100644 rrt_algorithms/search_space/search_space0.py create mode 100644 rrt_algorithms/search_space/search_space1.py create mode 100644 rrt_algorithms/search_space/search_space2.py diff --git a/rrt_algorithms/search_space/search_space.py b/rrt_algorithms/search_space/search_space.py index 16c3d42..10cd2ee 100644 --- a/rrt_algorithms/search_space/search_space.py +++ b/rrt_algorithms/search_space/search_space.py @@ -7,71 +7,75 @@ from rrt_algorithms.utilities.geometry import es_points_along_line from rrt_algorithms.utilities.obstacle_generation import obstacle_generator +class SearchSpace: + def __init__(self, dimension_lengths, obstacles=None): + self.dimensions = len(dimension_lengths) + self.dimension_lengths = dimension_lengths + self.obstacles = obstacles if obstacles is not None else [] -class SearchSpace(object): - def __init__(self, dimension_lengths, O=None): + def obstacle_free(self, point): """ - Initialize Search Space - :param dimension_lengths: range of each dimension - :param O: list of obstacles + Check if the point is free from obstacles. + :param point: Tuple (x, y, z) + :return: True if the point is free of obstacles, False otherwise. """ - # sanity check - if len(dimension_lengths) < 2: - raise Exception("Must have at least 2 dimensions") - self.dimensions = len(dimension_lengths) # number of dimensions - # sanity checks - if any(len(i) != 2 for i in dimension_lengths): - raise Exception("Dimensions can only have a start and end") - if any(i[0] >= i[1] for i in dimension_lengths): - raise Exception("Dimension start must be less than dimension end") - self.dimension_lengths = dimension_lengths # length of each dimension - p = index.Property() - p.dimension = self.dimensions - if O is None: - self.obs = index.Index(interleaved=True, properties=p) - else: - # r-tree representation of obstacles - # sanity check - if any(len(o) / 2 != len(dimension_lengths) for o in O): - raise Exception("Obstacle has incorrect dimension definition") - if any(o[i] >= o[int(i + len(o) / 2)] for o in O for i in range(int(len(o) / 2))): - raise Exception("Obstacle start must be less than obstacle end") - self.obs = index.Index(obstacle_generator(O), interleaved=True, properties=p) + for obstacle in self.obstacles: + x_center, y_center, z_min, z_max, radius = obstacle + + # Check if the point is within the cylinder's height bounds + if z_min <= point[2] <= z_max: + # Check if the point is within the radius of the cylinder + distance_to_axis = np.sqrt((point[0] - x_center) ** 2 + (point[1] - y_center) ** 2) + if distance_to_axis <= radius: + return False # The point is inside the obstacle + return True # The point is free of obstacles - def obstacle_free(self, x): + def collision_free(self, x_a, x_b, r): """ - Check if a location resides inside of an obstacle - :param x: location to check - :return: True if not inside an obstacle, False otherwise + Check if a line segment between x_a and x_b is free from obstacles. + :param x_a: Starting point of the line segment. + :param x_b: Ending point of the line segment. + :param r: Resolution for sampling along the line. + :return: True if the line segment does not intersect any obstacles, False otherwise. """ - return self.obs.count(x) == 0 + # Generate points along the line segment between x_a and x_b + points = self.es_points_along_line(x_a, x_b, r) + for point in points: + if not self.obstacle_free(point): + return False # Collision detected + return True # No collision - def sample_free(self): + + + def es_points_along_line(self, x_a, x_b, r): """ - Sample a location within X_free - :return: random location within X_free + Generate points along a line segment between x_a and x_b. + :param x_a: Starting point of the line segment. + :param x_b: Ending point of the line segment. + :param r: Resolution for sampling points. + :return: List of points along the line segment. """ - while True: # sample until not inside of an obstacle - x = self.sample() - if self.obstacle_free(x): - return x + distance = np.linalg.norm(np.array(x_b) - np.array(x_a)) + num_points = int(distance / r) + 1 + return [tuple(np.array(x_a) + t * (np.array(x_b) - np.array(x_a))) for t in np.linspace(0, 1, num_points)] + - def collision_free(self, start, end, r): + + def sample(self): """ - Check if a line segment intersects an obstacle - :param start: starting point of line - :param end: ending point of line - :param r: resolution of points to sample along edge when checking for collisions - :return: True if line segment does not intersect an obstacle, False otherwise + Return a random location within the search space dimensions. + :return: A tuple representing a random location within the search space. """ - points = es_points_along_line(start, end, r) - coll_free = all(map(self.obstacle_free, points)) - return coll_free + return tuple(np.random.uniform(low, high) for low, high in self.dimension_lengths) - def sample(self): + def sample_free(self): """ - Return a random location within X - :return: random location within X (not necessarily X_free) + Sample a random location within the search space that is free from obstacles. + :return: A tuple representing a random location within the search space that is obstacle-free. """ - x = np.random.uniform(self.dimension_lengths[:, 0], self.dimension_lengths[:, 1]) - return tuple(x) + while True: + point = self.sample() + if self.obstacle_free(point): + return point + + diff --git a/rrt_algorithms/search_space/search_space0.py b/rrt_algorithms/search_space/search_space0.py new file mode 100644 index 0000000..16c3d42 --- /dev/null +++ b/rrt_algorithms/search_space/search_space0.py @@ -0,0 +1,77 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. + +import numpy as np +from rtree import index + +from rrt_algorithms.utilities.geometry import es_points_along_line +from rrt_algorithms.utilities.obstacle_generation import obstacle_generator + + +class SearchSpace(object): + def __init__(self, dimension_lengths, O=None): + """ + Initialize Search Space + :param dimension_lengths: range of each dimension + :param O: list of obstacles + """ + # sanity check + if len(dimension_lengths) < 2: + raise Exception("Must have at least 2 dimensions") + self.dimensions = len(dimension_lengths) # number of dimensions + # sanity checks + if any(len(i) != 2 for i in dimension_lengths): + raise Exception("Dimensions can only have a start and end") + if any(i[0] >= i[1] for i in dimension_lengths): + raise Exception("Dimension start must be less than dimension end") + self.dimension_lengths = dimension_lengths # length of each dimension + p = index.Property() + p.dimension = self.dimensions + if O is None: + self.obs = index.Index(interleaved=True, properties=p) + else: + # r-tree representation of obstacles + # sanity check + if any(len(o) / 2 != len(dimension_lengths) for o in O): + raise Exception("Obstacle has incorrect dimension definition") + if any(o[i] >= o[int(i + len(o) / 2)] for o in O for i in range(int(len(o) / 2))): + raise Exception("Obstacle start must be less than obstacle end") + self.obs = index.Index(obstacle_generator(O), interleaved=True, properties=p) + + def obstacle_free(self, x): + """ + Check if a location resides inside of an obstacle + :param x: location to check + :return: True if not inside an obstacle, False otherwise + """ + return self.obs.count(x) == 0 + + def sample_free(self): + """ + Sample a location within X_free + :return: random location within X_free + """ + while True: # sample until not inside of an obstacle + x = self.sample() + if self.obstacle_free(x): + return x + + def collision_free(self, start, end, r): + """ + Check if a line segment intersects an obstacle + :param start: starting point of line + :param end: ending point of line + :param r: resolution of points to sample along edge when checking for collisions + :return: True if line segment does not intersect an obstacle, False otherwise + """ + points = es_points_along_line(start, end, r) + coll_free = all(map(self.obstacle_free, points)) + return coll_free + + def sample(self): + """ + Return a random location within X + :return: random location within X (not necessarily X_free) + """ + x = np.random.uniform(self.dimension_lengths[:, 0], self.dimension_lengths[:, 1]) + return tuple(x) diff --git a/rrt_algorithms/search_space/search_space1.py b/rrt_algorithms/search_space/search_space1.py new file mode 100644 index 0000000..fc4194d --- /dev/null +++ b/rrt_algorithms/search_space/search_space1.py @@ -0,0 +1,116 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import numpy as np +from rtree import index + +from rrt_algorithms.utilities.geometry import es_points_along_line +from rrt_algorithms.utilities.obstacle_generation1 import obstacle_generator + +from rtree import index + +class SearchSpace: + def __init__(self, dimension_lengths, obstacles=None): + self.dimension_lengths = dimension_lengths + self.obstacles = obstacles if obstacles is not None else [] + self.dimensions = len(dimension_lengths) # Add this line to define the number of dimensions + self.build_bvh() + self.build_obs_index() + + def build_bvh(self): + # Create a BVH tree for fast collision detection + p = index.Property() + p.dimension = 3 # Set dimension to 3 for 3D bounding boxes + self.bvh = index.Index(properties=p) + for i, obstacle in enumerate(self.obstacles): + aabb = self.compute_aabb(obstacle) + self.bvh.insert(i, aabb) + + def build_obs_index(self): + # Create an R-tree index for obstacles + p = index.Property() + p.dimension = 3 # Set dimension to 3 for 3D obstacles + self.obs = index.Index(properties=p) + for i, obstacle in enumerate(self.obstacles): + aabb = self.compute_aabb(obstacle) + self.obs.insert(i, aabb) + + def compute_aabb(self, obstacle): + # Compute the Axis-Aligned Bounding Box (AABB) for the obstacle + x_center, y_center, z_min, z_max, radius = obstacle + x_min = x_center - radius + x_max = x_center + radius + y_min = y_center - radius + y_max = y_center + radius + z_min = z_min # Lower z-bound + z_max = z_max # Upper z-bound + return (x_min, y_min, z_min, x_max, y_max, z_max) + + def sample(self): + """ + Return a random location within the search space dimensions. + :return: A tuple representing a random location within the search space. + """ + return tuple(np.random.uniform(low, high) for low, high in self.dimension_lengths) + def collision_free(self, start, end, r): + direction = np.array(end) - np.array(start) + length = np.linalg.norm(direction) + direction = direction / length + steps = int(length / r) + for i in range(steps + 1): + point = np.array(start) + i * r * direction + if not self.obstacle_free(point): + return False + return True + + + + def is_point_in_obstacle(self, point, obstacle): + # Detailed check if the point is inside the actual obstacle + x_center, y_center, z_min, z_max, radius = obstacle + dist = np.linalg.norm(np.array(point[:2]) - np.array([x_center, y_center])) + return dist <= radius and z_min <= point[2] <= z_max + + + + def es_points_along_line(self, x_a, x_b, r): + """ + Generate points along a line segment between x_a and x_b. + :param x_a: Starting point of the line segment. + :param x_b: Ending point of the line segment. + :param r: Resolution for sampling points. + :return: List of points along the line segment. + """ + distance = np.linalg.norm(np.array(x_b) - np.array(x_a)) + num_points = int(distance / r) + 1 + return [tuple(np.array(x_a) + t * (np.array(x_b) - np.array(x_a))) for t in np.linspace(0, 1, num_points)] + + + + + def obstacle_free(self, point): + """ + Check if a point is free from obstacles. + :param point: A tuple representing a point in the search space. + :return: True if the point is free from obstacles, False otherwise. + """ + nearby_obstacles = list(self.bvh.intersection((point[0], point[1], point[2], point[0], point[1], point[2]))) + for obs_idx in nearby_obstacles: + if self.is_point_in_obstacle(point, self.obstacles[obs_idx]): + return False + return True + + def sample_free(self): + """ + Sample a random location within the search space that is free from obstacles. + :return: A tuple representing a random location within the search space that is obstacle-free. + """ + while True: + point = self.sample() + if self.obstacle_free(point): + return point diff --git a/rrt_algorithms/search_space/search_space2.py b/rrt_algorithms/search_space/search_space2.py new file mode 100644 index 0000000..f776cd1 --- /dev/null +++ b/rrt_algorithms/search_space/search_space2.py @@ -0,0 +1,131 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#The drone is modeled as a sphere with a 10 cm radius (0.1 meters). This radius is added to the radius of each obstacle during collision detection. +#Expanded Obstacles: By expanding each obstacle's radius during collision checks, we ensure that the drone is considered in the obstacle avoidance logic, avoiding potential collisions. +#all_obstacles as a combination of the obstacles list (static obstacles) and Obstacles (randomly generated obstacles). +import numpy as np +from rtree import index + +from rrt_algorithms.utilities.geometry import es_points_along_line +from rrt_algorithms.utilities.obstacle_generation2 import obstacle_generator + +from rtree import index + +class SearchSpace: + def __init__(self, dimension_lengths, obstacles=None): + self.dimension_lengths = dimension_lengths + self.obstacles = obstacles if obstacles is not None else [] + self.dimensions = len(dimension_lengths) + self.drone_radius = 0.10 # Drone radius in meters (10 cm for a 20 cm diameter drone) + self.build_bvh() + + def build_bvh(self): + p = index.Property() + p.dimension = 3 # Set dimension to 3 for 3D bounding boxes + self.bvh = index.Index(properties=p) + for i, obstacle in enumerate(self.obstacles): + aabb = self.compute_aabb(obstacle) + self.bvh.insert(i, aabb) + + def compute_aabb(self, obstacle): + x_center, y_center, z_min, z_max, radius = obstacle + radius += self.drone_radius # Expand the obstacle's radius by the drone's radius + x_min = x_center - radius + x_max = x_center + radius + y_min = y_center - radius + y_max = y_center + radius + z_min = z_min # Lower z-bound + z_max = z_max # Upper z-bound + return (x_min, y_min, z_min, x_max, y_max, z_max) + + def obstacle_free(self, point): + # Quick check using BVH and AABB + nearby_obstacles = list(self.bvh.intersection((point[0], point[1], point[2], point[0], point[1], point[2]))) + for obs_idx in nearby_obstacles: + if self.is_point_in_obstacle(point, self.obstacles[obs_idx]): + return False + return True + + def is_point_in_obstacle(self, point, obstacle): + # Detailed check if the point is inside the actual obstacle + x_center, y_center, z_min, z_max, radius = obstacle + radius += self.drone_radius # Expand the obstacle's radius by the drone's radius + dist = np.linalg.norm(np.array(point[:2]) - np.array([x_center, y_center])) + return dist <= radius and z_min <= point[2] <= z_max + + def collision_free(self, start, end, r): + # Check the line segment using the BVH + start = start[:5] + end = end[:5] + direction = np.array(end) - np.array(start) + length = np.linalg.norm(direction) + direction = direction / length + steps = int(length / r) + for i in range(steps + 1): + point = np.array(start) + i * r * direction + if not self.obstacle_free(point): + return False + return True + + def build_obs_index(self): + # Create an R-tree index for obstacles + p = index.Property() + p.dimension = 3 # Set dimension to 3 for 3D obstacles + self.obs = index.Index(properties=p) + for i, obstacle in enumerate(self.obstacles): + aabb = self.compute_aabb(obstacle) + self.obs.insert(i, aabb) + + + + def sample(self): + """ + Return a random location within the search space dimensions. + :return: A tuple representing a random location within the search space. + """ + return tuple(np.random.uniform(low, high) for low, high in self.dimension_lengths) + def collision_free(self, start, end, r): + direction = np.array(end) - np.array(start) + length = np.linalg.norm(direction) + direction = direction / length + steps = int(length / r) + for i in range(steps + 1): + point = np.array(start) + i * r * direction + if not self.obstacle_free(point): + return False + return True + + + + + + + def es_points_along_line(self, x_a, x_b, r): + """ + Generate points along a line segment between x_a and x_b. + :param x_a: Starting point of the line segment. + :param x_b: Ending point of the line segment. + :param r: Resolution for sampling points. + :return: List of points along the line segment. + """ + distance = np.linalg.norm(np.array(x_b) - np.array(x_a)) + num_points = int(distance / r) + 1 + return [tuple(np.array(x_a) + t * (np.array(x_b) - np.array(x_a))) for t in np.linspace(0, 1, num_points)] + + + + + + def sample_free(self): + """ + Sample a random location within the search space that is free from obstacles. + :return: A tuple representing a random location within the search space that is obstacle-free. + """ + while True: + point = self.sample() + if self.obstacle_free(point): + return point From 36fe4b7d63e64a18647f40fc57276bbecaabad38 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:09:06 +0100 Subject: [PATCH 03/45] Add files via upload --- rrt_algorithms/rrt/rrt.py | 3 + rrt_algorithms/rrt/rrt_base 1.py | 186 +++++++++++++++++++++++++++++++ rrt_algorithms/rrt/rrt_base.py | 9 ++ 3 files changed, 198 insertions(+) create mode 100644 rrt_algorithms/rrt/rrt_base 1.py diff --git a/rrt_algorithms/rrt/rrt.py b/rrt_algorithms/rrt/rrt.py index 34fa05a..c295202 100644 --- a/rrt_algorithms/rrt/rrt.py +++ b/rrt_algorithms/rrt/rrt.py @@ -25,7 +25,9 @@ def rrt_search(self): self.add_edge(0, self.x_init, None) while True: + print(f"q: {self.q}") x_new, x_nearest = self.new_and_near(0, self.q) + print(f"x_new: {x_new}, x_nearest: {x_nearest}") if x_new is None: continue @@ -36,3 +38,4 @@ def rrt_search(self): solution = self.check_solution() if solution[0]: return solution[1] + diff --git a/rrt_algorithms/rrt/rrt_base 1.py b/rrt_algorithms/rrt/rrt_base 1.py new file mode 100644 index 0000000..eaa19e9 --- /dev/null +++ b/rrt_algorithms/rrt/rrt_base 1.py @@ -0,0 +1,186 @@ +import random + +import numpy as np +import sys +sys.path.append('/home/dell/rrt-algorithms') + + +from rrt_algorithms.search_space.search_space1 import SearchSpace +from rrt_algorithms.rrt.tree import Tree +from rrt_algorithms.utilities.geometry import steer + + +class RRTBase(object): + def __init__(self, X, q, x_init, x_goal, max_samples, r, prc=0.01): + """ + Template RRT planner + :param X: Search Space + :param q: length of edges added to tree + :param x_init: tuple, initial location + :param x_goal: tuple, goal location + :param max_samples: max number of samples to take + :param r: resolution of points to sample along edge when checking for collisions + :param prc: probability of checking whether there is a solution + """ + self.X = X + self.samples_taken = 0 + self.max_samples = max_samples + self.q = q + self.r = r + self.prc = prc + self.x_init = x_init + self.x_goal = x_goal + self.trees = [] # list of all trees + self.add_tree() # add initial tree + + def add_tree(self): + """ + Create an empty tree and add to trees + """ + self.trees.append(Tree(self.X)) + + def add_vertex(self, tree, v): + """ + Add vertex to corresponding tree + :param tree: int, tree to which to add vertex + :param v: tuple, vertex to add + """ + self.trees[tree].V.insert(0, v + v, v) + self.trees[tree].V_count += 1 # increment number of vertices in tree + self.samples_taken += 1 # increment number of samples taken + + def add_edge(self, tree, child, parent): + """ + Add edge to corresponding tree + :param tree: int, tree to which to add vertex + :param child: tuple, child vertex + :param parent: tuple, parent vertex + """ + self.trees[tree].E[child] = parent + + def nearby(self, tree, x, n): + """ + Return nearby vertices + :param tree: int, tree being searched + :param x: tuple, vertex around which searching + :param n: int, max number of neighbors to return + :return: list of nearby vertices + """ + return self.trees[tree].V.nearest(x, num_results=n, objects="raw") + + def get_nearest(self, tree, x): + """ + Return vertex nearest to x + :param tree: int, tree being searched + :param x: tuple, vertex around which searching + :return: tuple, nearest vertex to x + """ + return next(self.nearby(tree, x, 1)) + + def new_and_near(self, tree, q): + """ + Return a new steered vertex and the vertex in tree that is nearest + :param tree: int, tree being searched + :param q: length of edge when steering + :return: vertex, new steered vertex, vertex, nearest vertex in tree to new vertex + """ + x_rand = self.X.sample_free() + print(f"Random sample: {x_rand}") + + x_nearest = self.get_nearest(tree, x_rand) + print(f"Nearest point: {x_nearest}") + x_new = self.bound_point(steer(x_nearest, x_rand, q)) + print(f"Steered new point: {x_new}") + # check if new point is in X_free and not already in V + if not self.trees[0].V.count(x_new) == 0 or not self.X.obstacle_free(x_new): + print(f"New point {x_new} is invalid or in obstacle.") + return None, None + self.samples_taken += 1 + return x_new, x_nearest + + def connect_to_point(self, tree, x_a, x_b): + """ + Connect vertex x_a in tree to vertex x_b + :param tree: int, tree to which to add edge + :param x_a: tuple, vertex + :param x_b: tuple, vertex + :return: bool, True if able to add edge, False if prohibited by an obstacle + """ + if self.trees[tree].V.count(x_b) == 0 and self.X.collision_free(x_a, x_b, self.r): + self.add_vertex(tree, x_b) + self.add_edge(tree, x_b, x_a) + return True + return False + + def can_connect_to_goal(self, tree): + """ + Check if the goal can be connected to the graph + :param tree: rtree of all Vertices + :return: True if can be added, False otherwise + """ + x_nearest = self.get_nearest(tree, self.x_goal) + if self.x_goal in self.trees[tree].E and x_nearest in self.trees[tree].E[self.x_goal]: + # tree is already connected to goal using nearest vertex + return True + # check if obstacle-free + if self.X.collision_free(x_nearest, self.x_goal, self.r): + return True + return False + + def get_path(self): + """ + Return path through tree from start to goal + :return: path if possible, None otherwise + """ + if self.can_connect_to_goal(0): + print("Can connect to goal") + self.connect_to_goal(0) + return self.reconstruct_path(0, self.x_init, self.x_goal) + print("Could not connect to goal") + return None + + def connect_to_goal(self, tree): + """ + Connect x_goal to graph + (does not check if this should be possible, for that use: can_connect_to_goal) + :param tree: rtree of all Vertices + """ + x_nearest = self.get_nearest(tree, self.x_goal) + self.trees[tree].E[self.x_goal] = x_nearest + + def reconstruct_path(self, tree, x_init, x_goal): + """ + Reconstruct path from start to goal + :param tree: int, tree in which to find path + :param x_init: tuple, starting vertex + :param x_goal: tuple, ending vertex + :return: sequence of vertices from start to goal + """ + path = [x_goal] + current = x_goal + if x_init == x_goal: + return path + while not self.trees[tree].E[current] == x_init: + path.append(self.trees[tree].E[current]) + current = self.trees[tree].E[current] + path.append(x_init) + path.reverse() + return path + + def check_solution(self): + # probabilistically check if solution found + if self.prc and random.random() < self.prc: + print("Checking if can connect to goal at", str(self.samples_taken), "samples") + path = self.get_path() + if path is not None: + return True, path + # check if can connect to goal after generating max_samples + if self.samples_taken >= self.max_samples: + return True, self.get_path() + return False, None + + def bound_point(self, point): + # if point is out-of-bounds, set to bound + point = np.maximum(point, self.X.dimension_lengths[:, 0]) + point = np.minimum(point, self.X.dimension_lengths[:, 1]) + return tuple(point) diff --git a/rrt_algorithms/rrt/rrt_base.py b/rrt_algorithms/rrt/rrt_base.py index 8d147fa..e19d617 100644 --- a/rrt_algorithms/rrt/rrt_base.py +++ b/rrt_algorithms/rrt/rrt_base.py @@ -1,7 +1,11 @@ import random import numpy as np +import sys +sys.path.append('/home/dell/rrt-algorithms') + +from rrt_algorithms.search_space.search_space import SearchSpace from rrt_algorithms.rrt.tree import Tree from rrt_algorithms.utilities.geometry import steer @@ -81,10 +85,15 @@ def new_and_near(self, tree, q): :return: vertex, new steered vertex, vertex, nearest vertex in tree to new vertex """ x_rand = self.X.sample_free() + print(f"Random sample: {x_rand}") + x_nearest = self.get_nearest(tree, x_rand) + print(f"Nearest point: {x_nearest}") x_new = self.bound_point(steer(x_nearest, x_rand, q)) + print(f"Steered new point: {x_new}") # check if new point is in X_free and not already in V if not self.trees[0].V.count(x_new) == 0 or not self.X.obstacle_free(x_new): + print(f"New point {x_new} is invalid or in obstacle.") return None, None self.samples_taken += 1 return x_new, x_nearest From c8aa4710b080e0e5826e3eddc2d77e1830bdd0d7 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:14:27 +0100 Subject: [PATCH 04/45] Create dwa_algorithm --- rrt_algorithms/dwa_algorithm | 1 + 1 file changed, 1 insertion(+) create mode 100644 rrt_algorithms/dwa_algorithm diff --git a/rrt_algorithms/dwa_algorithm b/rrt_algorithms/dwa_algorithm new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/rrt_algorithms/dwa_algorithm @@ -0,0 +1 @@ + From e94b7ef50f65253f6c96ddf2797240b9bbeaf111 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:15:23 +0100 Subject: [PATCH 05/45] Delete rrt_algorithms/dwa_algorithm --- rrt_algorithms/dwa_algorithm | 1 - 1 file changed, 1 deletion(-) delete mode 100644 rrt_algorithms/dwa_algorithm diff --git a/rrt_algorithms/dwa_algorithm b/rrt_algorithms/dwa_algorithm deleted file mode 100644 index 8b13789..0000000 --- a/rrt_algorithms/dwa_algorithm +++ /dev/null @@ -1 +0,0 @@ - From c24d78f6da265e6d0303bed82c773d0547962846 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:17:03 +0100 Subject: [PATCH 06/45] Create dwa_algorithm --- rrt_algorithms/dwa_algorithm | 1 + 1 file changed, 1 insertion(+) create mode 100644 rrt_algorithms/dwa_algorithm diff --git a/rrt_algorithms/dwa_algorithm b/rrt_algorithms/dwa_algorithm new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/rrt_algorithms/dwa_algorithm @@ -0,0 +1 @@ + From afd12273df4aa1ea8c0a609911ddce058b250155 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 10:17:38 +0100 Subject: [PATCH 07/45] Delete rrt_algorithms/dwa_algorithm --- rrt_algorithms/dwa_algorithm | 1 - 1 file changed, 1 deletion(-) delete mode 100644 rrt_algorithms/dwa_algorithm diff --git a/rrt_algorithms/dwa_algorithm b/rrt_algorithms/dwa_algorithm deleted file mode 100644 index 8b13789..0000000 --- a/rrt_algorithms/dwa_algorithm +++ /dev/null @@ -1 +0,0 @@ - From 51c508aa9934feeee9daff0270ecad21da4e8344 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:31:37 +0100 Subject: [PATCH 08/45] Update README.md --- README.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index be2ba5d..f4a3c32 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,15 @@ +## Requirements +pip install scikit-fuzzy + +pip install --upgrade numpy + +pip install tensorflow keras gym + +pip install tensorflow or pip install tensorflow==2.10.0 # rrt -Collection of rrt-based algorithms that scale to n-dimensions: +Collection of rrt-based algorithms in 3-dimensions: - rrt -- rrt* (rrt-star) -- rrt* (bidirectional) -- rrt* (bidriectional, lazy shortening) -- rrt connect + Utilizes [R-trees](https://en.wikipedia.org/wiki/R-tree) to improve performance by avoiding point-wise collision-checking and distance-checking. From 6638d0249b3c0c6c62db13e75044cf4e93956a1d Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:36:43 +0100 Subject: [PATCH 09/45] Create 123 --- dwa_algorithm/123 | 1 + 1 file changed, 1 insertion(+) create mode 100644 dwa_algorithm/123 diff --git a/dwa_algorithm/123 b/dwa_algorithm/123 new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/dwa_algorithm/123 @@ -0,0 +1 @@ + From 2355a93f3033927667c29e6ca43a4fe1b3b90be5 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:37:12 +0100 Subject: [PATCH 10/45] Add files via upload --- dwa_algorithm/DWA.py | 102 +++++++++++ dwa_algorithm/DWA1.py | 108 ++++++++++++ dwa_algorithm/DWA2.py | 164 ++++++++++++++++++ dwa_algorithm/DWA3.py | 164 ++++++++++++++++++ dwa_algorithm/__pycache__/DWA.cpython-38.pyc | Bin 0 -> 3336 bytes dwa_algorithm/__pycache__/DWA1.cpython-38.pyc | Bin 0 -> 2825 bytes dwa_algorithm/__pycache__/DWA2.cpython-38.pyc | Bin 0 -> 4637 bytes dwa_algorithm/__pycache__/DWA3.cpython-38.pyc | Bin 0 -> 4671 bytes 8 files changed, 538 insertions(+) create mode 100644 dwa_algorithm/DWA.py create mode 100644 dwa_algorithm/DWA1.py create mode 100644 dwa_algorithm/DWA2.py create mode 100644 dwa_algorithm/DWA3.py create mode 100644 dwa_algorithm/__pycache__/DWA.cpython-38.pyc create mode 100644 dwa_algorithm/__pycache__/DWA1.cpython-38.pyc create mode 100644 dwa_algorithm/__pycache__/DWA2.cpython-38.pyc create mode 100644 dwa_algorithm/__pycache__/DWA3.cpython-38.pyc diff --git a/dwa_algorithm/DWA.py b/dwa_algorithm/DWA.py new file mode 100644 index 0000000..be3e894 --- /dev/null +++ b/dwa_algorithm/DWA.py @@ -0,0 +1,102 @@ +import numpy as np + +class DWA: + def __init__(self, config): + self.config = config + + def plan(self, current_position, goal_position, X, obstacles): + # Generate the dynamic window based on current speed and robot limitations + dw = self.calc_dynamic_window(current_position) + + # Evaluate trajectories in the dynamic window + best_u = None + min_cost = float('inf') + best_trajectory = [] + + for v, w in dw: + trajectory = self.generate_trajectory(current_position, v, w) + cost = self.calc_cost(trajectory, goal_position, obstacles) + + if cost < min_cost: + min_cost = cost + best_u = (v, w) + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [(v, w) for v in np.arange(v_min, v_max, self.config['v_reso']) for w in np.arange(w_min, w_max, self.config['yaw_rate_reso'])] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) + diff --git a/dwa_algorithm/DWA1.py b/dwa_algorithm/DWA1.py new file mode 100644 index 0000000..8ddc95a --- /dev/null +++ b/dwa_algorithm/DWA1.py @@ -0,0 +1,108 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import numpy as np + +class DWA: + def __init__(self, config): + self.config = config + + def plan(self, start_point, end_point, X, obstacles): + # Generate dynamic window and evaluate possible trajectories + dw = self.calc_dynamic_window(start_point) + best_trajectory = None + min_cost = float('inf') + + for v in np.arange(dw[0], dw[1], self.config['v_reso']): + for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): + trajectory = self.generate_trajectory(start_point, v, y) + if X.collision_free(trajectory[0], trajectory[-1], self.config['dt']): + cost = self.calc_cost(trajectory, end_point, obstacles) + if cost < min_cost: + min_cost = cost + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation, returning individual scalar values instead of a list of tuples + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [v_min, v_max, w_min, w_max] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +""" +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) +""" diff --git a/dwa_algorithm/DWA2.py b/dwa_algorithm/DWA2.py new file mode 100644 index 0000000..5a6e071 --- /dev/null +++ b/dwa_algorithm/DWA2.py @@ -0,0 +1,164 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import sys +import numpy as np +import skfuzzy as fuzz +from skfuzzy import control as ctrl + +# Update the DWA plan method to use the fuzzy controller +class FuzzyController: + def __init__(self): + # Define fuzzy variables + self.distance_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_goal') + self.distance_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_obstacle') + + self.to_goal_gain = ctrl.Consequent(np.arange(0, 11, 1), 'to_goal_gain') + self.speed_gain = ctrl.Consequent(np.arange(0, 11, 1), 'speed_gain') + self.obstacle_gain = ctrl.Consequent(np.arange(0, 11, 1), 'obstacle_gain') + + # Auto-membership function with 3 levels: 'poor', 'average', 'good' + self.distance_to_goal.automf(3) + self.distance_to_obstacle.automf(3) + + self.to_goal_gain.automf(3) + self.speed_gain.automf(3) + self.obstacle_gain.automf(3) + + # Define fuzzy rules to cover all cases + rule1 = ctrl.Rule(self.distance_to_goal['poor'] & self.distance_to_obstacle['good'], + (self.to_goal_gain['good'], self.speed_gain['good'], self.obstacle_gain['poor'])) + rule2 = ctrl.Rule(self.distance_to_goal['average'] & self.distance_to_obstacle['average'], + (self.to_goal_gain['average'], self.speed_gain['average'], self.obstacle_gain['average'])) + rule3 = ctrl.Rule(self.distance_to_goal['good'] & self.distance_to_obstacle['poor'], + (self.to_goal_gain['poor'], self.speed_gain['poor'], self.obstacle_gain['good'])) + + # Create control system + self.control_system = ctrl.ControlSystem([rule1, rule2, rule3]) + self.simulation = ctrl.ControlSystemSimulation(self.control_system) + + def compute_gains(self, dist_to_goal, dist_to_obstacle): + # Set inputs + self.simulation.input['distance_to_goal'] = dist_to_goal + self.simulation.input['distance_to_obstacle'] = dist_to_obstacle + + # Perform fuzzy computation + self.simulation.compute() + + # Ensure the fuzzy system has outputted the necessary gains + return (self.simulation.output.get('to_goal_gain', 0), + self.simulation.output.get('speed_gain', 0), + self.simulation.output.get('obstacle_gain', 0)) + +class DWA: + def __init__(self, config): + self.config = config + self.fuzzy_controller = FuzzyController() + + def plan(self, start_point, end_point, X, obstacles): + # Compute distance to goal and nearest obstacle + dist_to_goal = np.linalg.norm(np.array(start_point[:2]) - np.array(end_point[:2])) + dist_to_obstacle = min([np.linalg.norm(np.array(start_point[:2]) - np.array(obs[:2])) - obs[4] for obs in obstacles]) + + # Get fuzzy-adjusted weights + to_goal_gain, speed_gain, obstacle_gain = self.fuzzy_controller.compute_gains(dist_to_goal, dist_to_obstacle) + # Generate dynamic window and evaluate possible trajectories + dw = self.calc_dynamic_window(start_point) + best_trajectory = None + min_cost = float('inf') + + for v in np.arange(dw[0], dw[1], self.config['v_reso']): + for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): + trajectory = self.generate_trajectory(start_point, v, y) + if X.collision_free(trajectory[0], trajectory[-1], self.config['dt']): + cost = (to_goal_gain * self.calc_to_goal_cost(trajectory[-1], end_point) + + speed_gain * (self.config['max_speed'] - trajectory[-1][3]) + + obstacle_gain * self.calc_obstacle_cost(trajectory, obstacles)) + if cost < min_cost: + min_cost = cost + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation, returning individual scalar values instead of a list of tuples + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [v_min, v_max, w_min, w_max] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +""" +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) +""" diff --git a/dwa_algorithm/DWA3.py b/dwa_algorithm/DWA3.py new file mode 100644 index 0000000..2af0e0a --- /dev/null +++ b/dwa_algorithm/DWA3.py @@ -0,0 +1,164 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import sys +import numpy as np +import skfuzzy as fuzz +from skfuzzy import control as ctrl + +# Update the DWA plan method to use the fuzzy controller +class FuzzyController: + def __init__(self): + # Define fuzzy variables + self.distance_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_goal') + self.distance_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_obstacle') + + self.to_goal_gain = ctrl.Consequent(np.arange(0, 11, 1), 'to_goal_gain') + self.speed_gain = ctrl.Consequent(np.arange(0, 11, 1), 'speed_gain') + self.obstacle_gain = ctrl.Consequent(np.arange(0, 11, 1), 'obstacle_gain') + + # Auto-membership function with 3 levels: 'poor', 'average', 'good' + self.distance_to_goal.automf(3) + self.distance_to_obstacle.automf(3) + + self.to_goal_gain.automf(3) + self.speed_gain.automf(3) + self.obstacle_gain.automf(3) + + # Define fuzzy rules to cover all cases + rule1 = ctrl.Rule(self.distance_to_goal['poor'] & self.distance_to_obstacle['good'], + (self.to_goal_gain['good'], self.speed_gain['good'], self.obstacle_gain['poor'])) + rule2 = ctrl.Rule(self.distance_to_goal['average'] & self.distance_to_obstacle['average'], + (self.to_goal_gain['average'], self.speed_gain['average'], self.obstacle_gain['average'])) + rule3 = ctrl.Rule(self.distance_to_goal['good'] & self.distance_to_obstacle['poor'], + (self.to_goal_gain['poor'], self.speed_gain['poor'], self.obstacle_gain['good'])) + + # Create control system + self.control_system = ctrl.ControlSystem([rule1, rule2, rule3]) + self.simulation = ctrl.ControlSystemSimulation(self.control_system) + + def compute_gains(self, dist_to_goal, dist_to_obstacle): + # Set inputs + self.simulation.input['distance_to_goal'] = dist_to_goal + self.simulation.input['distance_to_obstacle'] = dist_to_obstacle + + # Perform fuzzy computation + self.simulation.compute() + + # Ensure the fuzzy system has outputted the necessary gains + return (self.simulation.output.get('to_goal_gain', 0), + self.simulation.output.get('speed_gain', 0), + self.simulation.output.get('obstacle_gain', 0)) + +class DWA: + def __init__(self, config): + self.config = config + self.fuzzy_controller = FuzzyController() + + def plan(self, start_point, end_point, X, obstacles): + # Compute distance to goal and nearest obstacle + dist_to_goal = np.linalg.norm(np.array(start_point[:2]) - np.array(end_point[:2])) + dist_to_obstacle = min([np.linalg.norm(np.array(start_point[:2]) - np.array(obs[:2])) - obs[4] for obs in obstacles]) + + # Get fuzzy-adjusted weights + to_goal_gain, speed_gain, obstacle_gain = self.fuzzy_controller.compute_gains(dist_to_goal, dist_to_obstacle) + # Generate dynamic window and evaluate possible trajectories + dw = self.calc_dynamic_window(start_point) + best_trajectory = None + min_cost = float('inf') + + for v in np.arange(dw[0], dw[1], self.config['v_reso']): + for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): + trajectory = self.generate_trajectory(start_point, v, y) + if X.collision_free(trajectory[0][:5], trajectory[-1][:5], self.config['dt']): + cost = (to_goal_gain * self.calc_to_goal_cost(trajectory[-1], end_point) + + speed_gain * (self.config['max_speed'] - trajectory[-1][3]) + + obstacle_gain * self.calc_obstacle_cost(trajectory, obstacles)) + if cost < min_cost: + min_cost = cost + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation, returning individual scalar values instead of a list of tuples + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [v_min, v_max, w_min, w_max] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state[:5] # Ensure only the first 5 elements are considered + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +""" +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) +""" diff --git a/dwa_algorithm/__pycache__/DWA.cpython-38.pyc b/dwa_algorithm/__pycache__/DWA.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..66b5a32fff9dba13abe1746ea51713ea87a136f6 GIT binary patch literal 3336 zcmZ`*%a0sK8Lz5-OwZH1>xp+a2_`%gJVCJ|UJ1yt7qe10M0)`Qv}`qJPlW-Pl}_0{X^s_*%$`bD?f zVkp1<;vd5&8;t#n2CJom!5(V5P@aU# z;VlSpb9$=1AV|`r2m+f9H&XFghZYwL5goHAU36SjBkFI^DcK8%d(JCIvdS&EvdVTTN*kk2*(n>3n7Uqess?ckMdg<5$~(GV zdN6=E&{mQT2e!T%h2toYr)fAzqTnP+Wp<*S;W!HmeRUM4u?mYgC{%bDM@6Pib<_M1 zWqC2^D$-AHM8`_SX%S4bJSmba)!k7Rj+aCJE8WaK%Zo4?$2mNH;=7;4c@Z4@-Xuu_ 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17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:38:11 +0100 Subject: [PATCH 12/45] Add files via upload --- rrt_algorithms/dwa_algorithm/DWA.py | 102 +++++++++++ rrt_algorithms/dwa_algorithm/DWA1.py | 108 ++++++++++++ rrt_algorithms/dwa_algorithm/DWA2.py | 164 ++++++++++++++++++ rrt_algorithms/dwa_algorithm/DWA3.py | 164 ++++++++++++++++++ .../__pycache__/DWA.cpython-38.pyc | Bin 0 -> 3336 bytes .../__pycache__/DWA1.cpython-38.pyc | Bin 0 -> 2825 bytes .../__pycache__/DWA2.cpython-38.pyc | Bin 0 -> 4637 bytes .../__pycache__/DWA3.cpython-38.pyc | Bin 0 -> 4671 bytes 8 files changed, 538 insertions(+) create mode 100644 rrt_algorithms/dwa_algorithm/DWA.py create mode 100644 rrt_algorithms/dwa_algorithm/DWA1.py create mode 100644 rrt_algorithms/dwa_algorithm/DWA2.py create mode 100644 rrt_algorithms/dwa_algorithm/DWA3.py create mode 100644 rrt_algorithms/dwa_algorithm/__pycache__/DWA.cpython-38.pyc create mode 100644 rrt_algorithms/dwa_algorithm/__pycache__/DWA1.cpython-38.pyc create mode 100644 rrt_algorithms/dwa_algorithm/__pycache__/DWA2.cpython-38.pyc create mode 100644 rrt_algorithms/dwa_algorithm/__pycache__/DWA3.cpython-38.pyc diff --git a/rrt_algorithms/dwa_algorithm/DWA.py b/rrt_algorithms/dwa_algorithm/DWA.py new file mode 100644 index 0000000..be3e894 --- /dev/null +++ b/rrt_algorithms/dwa_algorithm/DWA.py @@ -0,0 +1,102 @@ +import numpy as np + +class DWA: + def __init__(self, config): + self.config = config + + def plan(self, current_position, goal_position, X, obstacles): + # Generate the dynamic window based on current speed and robot limitations + dw = self.calc_dynamic_window(current_position) + + # Evaluate trajectories in the dynamic window + best_u = None + min_cost = float('inf') + best_trajectory = [] + + for v, w in dw: + trajectory = self.generate_trajectory(current_position, v, w) + cost = self.calc_cost(trajectory, goal_position, obstacles) + + if cost < min_cost: + min_cost = cost + best_u = (v, w) + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [(v, w) for v in np.arange(v_min, v_max, self.config['v_reso']) for w in np.arange(w_min, w_max, self.config['yaw_rate_reso'])] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) + diff --git a/rrt_algorithms/dwa_algorithm/DWA1.py b/rrt_algorithms/dwa_algorithm/DWA1.py new file mode 100644 index 0000000..8ddc95a --- /dev/null +++ b/rrt_algorithms/dwa_algorithm/DWA1.py @@ -0,0 +1,108 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import numpy as np + +class DWA: + def __init__(self, config): + self.config = config + + def plan(self, start_point, end_point, X, obstacles): + # Generate dynamic window and evaluate possible trajectories + dw = self.calc_dynamic_window(start_point) + best_trajectory = None + min_cost = float('inf') + + for v in np.arange(dw[0], dw[1], self.config['v_reso']): + for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): + trajectory = self.generate_trajectory(start_point, v, y) + if X.collision_free(trajectory[0], trajectory[-1], self.config['dt']): + cost = self.calc_cost(trajectory, end_point, obstacles) + if cost < min_cost: + min_cost = cost + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation, returning individual scalar values instead of a list of tuples + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [v_min, v_max, w_min, w_max] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +""" +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) +""" diff --git a/rrt_algorithms/dwa_algorithm/DWA2.py b/rrt_algorithms/dwa_algorithm/DWA2.py new file mode 100644 index 0000000..5a6e071 --- /dev/null +++ b/rrt_algorithms/dwa_algorithm/DWA2.py @@ -0,0 +1,164 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import sys +import numpy as np +import skfuzzy as fuzz +from skfuzzy import control as ctrl + +# Update the DWA plan method to use the fuzzy controller +class FuzzyController: + def __init__(self): + # Define fuzzy variables + self.distance_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_goal') + self.distance_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_obstacle') + + self.to_goal_gain = ctrl.Consequent(np.arange(0, 11, 1), 'to_goal_gain') + self.speed_gain = ctrl.Consequent(np.arange(0, 11, 1), 'speed_gain') + self.obstacle_gain = ctrl.Consequent(np.arange(0, 11, 1), 'obstacle_gain') + + # Auto-membership function with 3 levels: 'poor', 'average', 'good' + self.distance_to_goal.automf(3) + self.distance_to_obstacle.automf(3) + + self.to_goal_gain.automf(3) + self.speed_gain.automf(3) + self.obstacle_gain.automf(3) + + # Define fuzzy rules to cover all cases + rule1 = ctrl.Rule(self.distance_to_goal['poor'] & self.distance_to_obstacle['good'], + (self.to_goal_gain['good'], self.speed_gain['good'], self.obstacle_gain['poor'])) + rule2 = ctrl.Rule(self.distance_to_goal['average'] & self.distance_to_obstacle['average'], + (self.to_goal_gain['average'], self.speed_gain['average'], self.obstacle_gain['average'])) + rule3 = ctrl.Rule(self.distance_to_goal['good'] & self.distance_to_obstacle['poor'], + (self.to_goal_gain['poor'], self.speed_gain['poor'], self.obstacle_gain['good'])) + + # Create control system + self.control_system = ctrl.ControlSystem([rule1, rule2, rule3]) + self.simulation = ctrl.ControlSystemSimulation(self.control_system) + + def compute_gains(self, dist_to_goal, dist_to_obstacle): + # Set inputs + self.simulation.input['distance_to_goal'] = dist_to_goal + self.simulation.input['distance_to_obstacle'] = dist_to_obstacle + + # Perform fuzzy computation + self.simulation.compute() + + # Ensure the fuzzy system has outputted the necessary gains + return (self.simulation.output.get('to_goal_gain', 0), + self.simulation.output.get('speed_gain', 0), + self.simulation.output.get('obstacle_gain', 0)) + +class DWA: + def __init__(self, config): + self.config = config + self.fuzzy_controller = FuzzyController() + + def plan(self, start_point, end_point, X, obstacles): + # Compute distance to goal and nearest obstacle + dist_to_goal = np.linalg.norm(np.array(start_point[:2]) - np.array(end_point[:2])) + dist_to_obstacle = min([np.linalg.norm(np.array(start_point[:2]) - np.array(obs[:2])) - obs[4] for obs in obstacles]) + + # Get fuzzy-adjusted weights + to_goal_gain, speed_gain, obstacle_gain = self.fuzzy_controller.compute_gains(dist_to_goal, dist_to_obstacle) + # Generate dynamic window and evaluate possible trajectories + dw = self.calc_dynamic_window(start_point) + best_trajectory = None + min_cost = float('inf') + + for v in np.arange(dw[0], dw[1], self.config['v_reso']): + for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): + trajectory = self.generate_trajectory(start_point, v, y) + if X.collision_free(trajectory[0], trajectory[-1], self.config['dt']): + cost = (to_goal_gain * self.calc_to_goal_cost(trajectory[-1], end_point) + + speed_gain * (self.config['max_speed'] - trajectory[-1][3]) + + obstacle_gain * self.calc_obstacle_cost(trajectory, obstacles)) + if cost < min_cost: + min_cost = cost + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation, returning individual scalar values instead of a list of tuples + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [v_min, v_max, w_min, w_max] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +""" +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] + goal_position = [10, 10] # Example goal position [x, y] + X = None # The search space; not used in this basic example + obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions + + trajectory = dwa.plan(current_position, goal_position, X, obstacles) + print("Best trajectory:", trajectory) +""" diff --git a/rrt_algorithms/dwa_algorithm/DWA3.py b/rrt_algorithms/dwa_algorithm/DWA3.py new file mode 100644 index 0000000..2af0e0a --- /dev/null +++ b/rrt_algorithms/dwa_algorithm/DWA3.py @@ -0,0 +1,164 @@ +# This file is subject to the terms and conditions defined in +# file 'LICENSE', which is part of this source code package. +#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). +#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. +#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. +#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. +#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. +# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. +import sys +import numpy as np +import skfuzzy as fuzz +from skfuzzy import control as ctrl + +# Update the DWA plan method to use the fuzzy controller +class FuzzyController: + def __init__(self): + # Define fuzzy variables + self.distance_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_goal') + self.distance_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_obstacle') + + self.to_goal_gain = ctrl.Consequent(np.arange(0, 11, 1), 'to_goal_gain') + self.speed_gain = ctrl.Consequent(np.arange(0, 11, 1), 'speed_gain') + self.obstacle_gain = ctrl.Consequent(np.arange(0, 11, 1), 'obstacle_gain') + + # Auto-membership function with 3 levels: 'poor', 'average', 'good' + self.distance_to_goal.automf(3) + self.distance_to_obstacle.automf(3) + + self.to_goal_gain.automf(3) + self.speed_gain.automf(3) + self.obstacle_gain.automf(3) + + # Define fuzzy rules to cover all cases + rule1 = ctrl.Rule(self.distance_to_goal['poor'] & self.distance_to_obstacle['good'], + (self.to_goal_gain['good'], self.speed_gain['good'], self.obstacle_gain['poor'])) + rule2 = ctrl.Rule(self.distance_to_goal['average'] & self.distance_to_obstacle['average'], + (self.to_goal_gain['average'], self.speed_gain['average'], self.obstacle_gain['average'])) + rule3 = ctrl.Rule(self.distance_to_goal['good'] & self.distance_to_obstacle['poor'], + (self.to_goal_gain['poor'], self.speed_gain['poor'], self.obstacle_gain['good'])) + + # Create control system + self.control_system = ctrl.ControlSystem([rule1, rule2, rule3]) + self.simulation = ctrl.ControlSystemSimulation(self.control_system) + + def compute_gains(self, dist_to_goal, dist_to_obstacle): + # Set inputs + self.simulation.input['distance_to_goal'] = dist_to_goal + self.simulation.input['distance_to_obstacle'] = dist_to_obstacle + + # Perform fuzzy computation + self.simulation.compute() + + # Ensure the fuzzy system has outputted the necessary gains + return (self.simulation.output.get('to_goal_gain', 0), + self.simulation.output.get('speed_gain', 0), + self.simulation.output.get('obstacle_gain', 0)) + +class DWA: + def __init__(self, config): + self.config = config + self.fuzzy_controller = FuzzyController() + + def plan(self, start_point, end_point, X, obstacles): + # Compute distance to goal and nearest obstacle + dist_to_goal = np.linalg.norm(np.array(start_point[:2]) - np.array(end_point[:2])) + dist_to_obstacle = min([np.linalg.norm(np.array(start_point[:2]) - np.array(obs[:2])) - obs[4] for obs in obstacles]) + + # Get fuzzy-adjusted weights + to_goal_gain, speed_gain, obstacle_gain = self.fuzzy_controller.compute_gains(dist_to_goal, dist_to_obstacle) + # Generate dynamic window and evaluate possible trajectories + dw = self.calc_dynamic_window(start_point) + best_trajectory = None + min_cost = float('inf') + + for v in np.arange(dw[0], dw[1], self.config['v_reso']): + for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): + trajectory = self.generate_trajectory(start_point, v, y) + if X.collision_free(trajectory[0][:5], trajectory[-1][:5], self.config['dt']): + cost = (to_goal_gain * self.calc_to_goal_cost(trajectory[-1], end_point) + + speed_gain * (self.config['max_speed'] - trajectory[-1][3]) + + obstacle_gain * self.calc_obstacle_cost(trajectory, obstacles)) + if cost < min_cost: + min_cost = cost + best_trajectory = trajectory + + return best_trajectory + + def calc_dynamic_window(self, current_position): + # Dynamic window calculation, returning individual scalar values instead of a list of tuples + v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) + v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) + w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) + w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) + + return [v_min, v_max, w_min, w_max] + + def generate_trajectory(self, current_position, v, w): + # Generate trajectory for a given velocity and yaw rate + trajectory = [current_position] + time = 0 + + while time <= self.config['predict_time']: + next_position = self.motion_model(trajectory[-1], v, w) + trajectory.append(next_position) + time += self.config['dt'] + + return trajectory + + def motion_model(self, state, v, w): + # Update the state [x, y, theta, v, w] given current velocities + x, y, theta, _, _ = state[:5] # Ensure only the first 5 elements are considered + x += v * np.cos(theta) * self.config['dt'] + y += v * np.sin(theta) * self.config['dt'] + theta += w * self.config['dt'] + return [x, y, theta, v, w] + + def calc_cost(self, trajectory, goal_position, obstacles): + # Cost function combining distance to goal, speed, and distance to obstacles + to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) + speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) + obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) + + return to_goal_cost + speed_cost + obstacle_cost + + def calc_to_goal_cost(self, state, goal_position): + # Euclidean distance to the goal + return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) + + def calc_obstacle_cost(self, trajectory, obstacles): + # Calculate the distance to the closest obstacle + min_dist = float('inf') + for point in trajectory: + for obs in obstacles: + dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) + if dist < min_dist: + min_dist = dist + return min_dist + +# Sample usage of DWA with RRT +""" +if __name__ == "__main__": + dwa_params = { + 'max_speed': 1.0, + 'min_speed': 0.0, + 'max_yaw_rate': 40.0 * np.pi / 180.0, + 'max_accel': 0.2, + 'v_reso': 0.01, + 'yaw_rate_reso': 0.1 * np.pi / 180.0, + 'dt': 0.1, + 'predict_time': 3.0, + 'to_goal_cost_gain': 1.0, + 'speed_cost_gain': 1.0, + 'obstacle_cost_gain': 1.0, + } + + dwa = DWA(dwa_params) + current_position = [0, 0, 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dwa_algorithm/DWA3.py | 164 ------------------ dwa_algorithm/__pycache__/DWA.cpython-38.pyc | Bin 3336 -> 0 bytes dwa_algorithm/__pycache__/DWA1.cpython-38.pyc | Bin 2825 -> 0 bytes dwa_algorithm/__pycache__/DWA2.cpython-38.pyc | Bin 4637 -> 0 bytes dwa_algorithm/__pycache__/DWA3.cpython-38.pyc | Bin 4671 -> 0 bytes 9 files changed, 539 deletions(-) delete mode 100644 dwa_algorithm/123 delete mode 100644 dwa_algorithm/DWA.py delete mode 100644 dwa_algorithm/DWA1.py delete mode 100644 dwa_algorithm/DWA2.py delete mode 100644 dwa_algorithm/DWA3.py delete mode 100644 dwa_algorithm/__pycache__/DWA.cpython-38.pyc delete mode 100644 dwa_algorithm/__pycache__/DWA1.cpython-38.pyc delete mode 100644 dwa_algorithm/__pycache__/DWA2.cpython-38.pyc delete mode 100644 dwa_algorithm/__pycache__/DWA3.cpython-38.pyc diff --git a/dwa_algorithm/123 b/dwa_algorithm/123 deleted file mode 100644 index 8b13789..0000000 --- a/dwa_algorithm/123 +++ /dev/null @@ -1 +0,0 @@ - diff --git a/dwa_algorithm/DWA.py b/dwa_algorithm/DWA.py deleted file mode 100644 index be3e894..0000000 --- a/dwa_algorithm/DWA.py +++ /dev/null @@ -1,102 +0,0 @@ -import numpy as np - -class DWA: - def __init__(self, config): - self.config = config - - def plan(self, current_position, goal_position, X, obstacles): - # Generate the dynamic window based on current speed and robot limitations - dw = self.calc_dynamic_window(current_position) - - # Evaluate trajectories in the dynamic window - best_u = None - min_cost = float('inf') - best_trajectory = [] - - for v, w in dw: - trajectory = self.generate_trajectory(current_position, v, w) - cost = self.calc_cost(trajectory, goal_position, obstacles) - - if cost < min_cost: - min_cost = cost - best_u = (v, w) - best_trajectory = trajectory - - return best_trajectory - - def calc_dynamic_window(self, current_position): - # Dynamic window calculation - v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) - v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) - w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) - w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) - - return [(v, w) for v in np.arange(v_min, v_max, self.config['v_reso']) for w in np.arange(w_min, w_max, self.config['yaw_rate_reso'])] - - def generate_trajectory(self, current_position, v, w): - # Generate trajectory for a given velocity and yaw rate - trajectory = [current_position] - time = 0 - - while time <= self.config['predict_time']: - next_position = self.motion_model(trajectory[-1], v, w) - trajectory.append(next_position) - time += self.config['dt'] - - return trajectory - - def motion_model(self, state, v, w): - # Update the state [x, y, theta, v, w] given current velocities - x, y, theta, _, _ = state - x += v * np.cos(theta) * self.config['dt'] - y += v * np.sin(theta) * self.config['dt'] - theta += w * self.config['dt'] - return [x, y, theta, v, w] - - def calc_cost(self, trajectory, goal_position, obstacles): - # Cost function combining distance to goal, speed, and distance to obstacles - to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) - speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) - obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) - - return to_goal_cost + speed_cost + obstacle_cost - - def calc_to_goal_cost(self, state, goal_position): - # Euclidean distance to the goal - return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) - - def calc_obstacle_cost(self, trajectory, obstacles): - # Calculate the distance to the closest obstacle - min_dist = float('inf') - for point in trajectory: - for obs in obstacles: - dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) - if dist < min_dist: - min_dist = dist - return min_dist - -# Sample usage of DWA with RRT -if __name__ == "__main__": - dwa_params = { - 'max_speed': 1.0, - 'min_speed': 0.0, - 'max_yaw_rate': 40.0 * np.pi / 180.0, - 'max_accel': 0.2, - 'v_reso': 0.01, - 'yaw_rate_reso': 0.1 * np.pi / 180.0, - 'dt': 0.1, - 'predict_time': 3.0, - 'to_goal_cost_gain': 1.0, - 'speed_cost_gain': 1.0, - 'obstacle_cost_gain': 1.0, - } - - dwa = DWA(dwa_params) - current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] - goal_position = [10, 10] # Example goal position [x, y] - X = None # The search space; not used in this basic example - obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions - - trajectory = dwa.plan(current_position, goal_position, X, obstacles) - print("Best trajectory:", trajectory) - diff --git a/dwa_algorithm/DWA1.py b/dwa_algorithm/DWA1.py deleted file mode 100644 index 8ddc95a..0000000 --- a/dwa_algorithm/DWA1.py +++ /dev/null @@ -1,108 +0,0 @@ -# This file is subject to the terms and conditions defined in -# file 'LICENSE', which is part of this source code package. -#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). -#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. -#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. -#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. -#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. -# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. -import numpy as np - -class DWA: - def __init__(self, config): - self.config = config - - def plan(self, start_point, end_point, X, obstacles): - # Generate dynamic window and evaluate possible trajectories - dw = self.calc_dynamic_window(start_point) - best_trajectory = None - min_cost = float('inf') - - for v in np.arange(dw[0], dw[1], self.config['v_reso']): - for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): - trajectory = self.generate_trajectory(start_point, v, y) - if X.collision_free(trajectory[0], trajectory[-1], self.config['dt']): - cost = self.calc_cost(trajectory, end_point, obstacles) - if cost < min_cost: - min_cost = cost - best_trajectory = trajectory - - return best_trajectory - - def calc_dynamic_window(self, current_position): - # Dynamic window calculation, returning individual scalar values instead of a list of tuples - v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) - v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) - w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) - w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) - - return [v_min, v_max, w_min, w_max] - - def generate_trajectory(self, current_position, v, w): - # Generate trajectory for a given velocity and yaw rate - trajectory = [current_position] - time = 0 - - while time <= self.config['predict_time']: - next_position = self.motion_model(trajectory[-1], v, w) - trajectory.append(next_position) - time += self.config['dt'] - - return trajectory - - def motion_model(self, state, v, w): - # Update the state [x, y, theta, v, w] given current velocities - x, y, theta, _, _ = state - x += v * np.cos(theta) * self.config['dt'] - y += v * np.sin(theta) * self.config['dt'] - theta += w * self.config['dt'] - return [x, y, theta, v, w] - - def calc_cost(self, trajectory, goal_position, obstacles): - # Cost function combining distance to goal, speed, and distance to obstacles - to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) - speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) - obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) - - return to_goal_cost + speed_cost + obstacle_cost - - def calc_to_goal_cost(self, state, goal_position): - # Euclidean distance to the goal - return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) - - def calc_obstacle_cost(self, trajectory, obstacles): - # Calculate the distance to the closest obstacle - min_dist = float('inf') - for point in trajectory: - for obs in obstacles: - dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) - if dist < min_dist: - min_dist = dist - return min_dist - -# Sample usage of DWA with RRT -""" -if __name__ == "__main__": - dwa_params = { - 'max_speed': 1.0, - 'min_speed': 0.0, - 'max_yaw_rate': 40.0 * np.pi / 180.0, - 'max_accel': 0.2, - 'v_reso': 0.01, - 'yaw_rate_reso': 0.1 * np.pi / 180.0, - 'dt': 0.1, - 'predict_time': 3.0, - 'to_goal_cost_gain': 1.0, - 'speed_cost_gain': 1.0, - 'obstacle_cost_gain': 1.0, - } - - dwa = DWA(dwa_params) - current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] - goal_position = [10, 10] # Example goal position [x, y] - X = None # The search space; not used in this basic example - obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions - - trajectory = dwa.plan(current_position, goal_position, X, obstacles) - print("Best trajectory:", trajectory) -""" diff --git a/dwa_algorithm/DWA2.py b/dwa_algorithm/DWA2.py deleted file mode 100644 index 5a6e071..0000000 --- a/dwa_algorithm/DWA2.py +++ /dev/null @@ -1,164 +0,0 @@ -# This file is subject to the terms and conditions defined in -# file 'LICENSE', which is part of this source code package. -#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). -#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. -#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. -#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. -#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. -# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. -import sys -import numpy as np -import skfuzzy as fuzz -from skfuzzy import control as ctrl - -# Update the DWA plan method to use the fuzzy controller -class FuzzyController: - def __init__(self): - # Define fuzzy variables - self.distance_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_goal') - self.distance_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_obstacle') - - self.to_goal_gain = ctrl.Consequent(np.arange(0, 11, 1), 'to_goal_gain') - self.speed_gain = ctrl.Consequent(np.arange(0, 11, 1), 'speed_gain') - self.obstacle_gain = ctrl.Consequent(np.arange(0, 11, 1), 'obstacle_gain') - - # Auto-membership function with 3 levels: 'poor', 'average', 'good' - self.distance_to_goal.automf(3) - self.distance_to_obstacle.automf(3) - - self.to_goal_gain.automf(3) - self.speed_gain.automf(3) - self.obstacle_gain.automf(3) - - # Define fuzzy rules to cover all cases - rule1 = ctrl.Rule(self.distance_to_goal['poor'] & self.distance_to_obstacle['good'], - (self.to_goal_gain['good'], self.speed_gain['good'], self.obstacle_gain['poor'])) - rule2 = ctrl.Rule(self.distance_to_goal['average'] & self.distance_to_obstacle['average'], - (self.to_goal_gain['average'], self.speed_gain['average'], self.obstacle_gain['average'])) - rule3 = ctrl.Rule(self.distance_to_goal['good'] & self.distance_to_obstacle['poor'], - (self.to_goal_gain['poor'], self.speed_gain['poor'], self.obstacle_gain['good'])) - - # Create control system - self.control_system = ctrl.ControlSystem([rule1, rule2, rule3]) - self.simulation = ctrl.ControlSystemSimulation(self.control_system) - - def compute_gains(self, dist_to_goal, dist_to_obstacle): - # Set inputs - self.simulation.input['distance_to_goal'] = dist_to_goal - self.simulation.input['distance_to_obstacle'] = dist_to_obstacle - - # Perform fuzzy computation - self.simulation.compute() - - # Ensure the fuzzy system has outputted the necessary gains - return (self.simulation.output.get('to_goal_gain', 0), - self.simulation.output.get('speed_gain', 0), - self.simulation.output.get('obstacle_gain', 0)) - -class DWA: - def __init__(self, config): - self.config = config - self.fuzzy_controller = FuzzyController() - - def plan(self, start_point, end_point, X, obstacles): - # Compute distance to goal and nearest obstacle - dist_to_goal = np.linalg.norm(np.array(start_point[:2]) - np.array(end_point[:2])) - dist_to_obstacle = min([np.linalg.norm(np.array(start_point[:2]) - np.array(obs[:2])) - obs[4] for obs in obstacles]) - - # Get fuzzy-adjusted weights - to_goal_gain, speed_gain, obstacle_gain = self.fuzzy_controller.compute_gains(dist_to_goal, dist_to_obstacle) - # Generate dynamic window and evaluate possible trajectories - dw = self.calc_dynamic_window(start_point) - best_trajectory = None - min_cost = float('inf') - - for v in np.arange(dw[0], dw[1], self.config['v_reso']): - for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): - trajectory = self.generate_trajectory(start_point, v, y) - if X.collision_free(trajectory[0], trajectory[-1], self.config['dt']): - cost = (to_goal_gain * self.calc_to_goal_cost(trajectory[-1], end_point) + - speed_gain * (self.config['max_speed'] - trajectory[-1][3]) + - obstacle_gain * self.calc_obstacle_cost(trajectory, obstacles)) - if cost < min_cost: - min_cost = cost - best_trajectory = trajectory - - return best_trajectory - - def calc_dynamic_window(self, current_position): - # Dynamic window calculation, returning individual scalar values instead of a list of tuples - v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) - v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) - w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) - w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) - - return [v_min, v_max, w_min, w_max] - - def generate_trajectory(self, current_position, v, w): - # Generate trajectory for a given velocity and yaw rate - trajectory = [current_position] - time = 0 - - while time <= self.config['predict_time']: - next_position = self.motion_model(trajectory[-1], v, w) - trajectory.append(next_position) - time += self.config['dt'] - - return trajectory - - def motion_model(self, state, v, w): - # Update the state [x, y, theta, v, w] given current velocities - x, y, theta, _, _ = state - x += v * np.cos(theta) * self.config['dt'] - y += v * np.sin(theta) * self.config['dt'] - theta += w * self.config['dt'] - return [x, y, theta, v, w] - - def calc_cost(self, trajectory, goal_position, obstacles): - # Cost function combining distance to goal, speed, and distance to obstacles - to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) - speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) - obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) - - return to_goal_cost + speed_cost + obstacle_cost - - def calc_to_goal_cost(self, state, goal_position): - # Euclidean distance to the goal - return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) - - def calc_obstacle_cost(self, trajectory, obstacles): - # Calculate the distance to the closest obstacle - min_dist = float('inf') - for point in trajectory: - for obs in obstacles: - dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) - if dist < min_dist: - min_dist = dist - return min_dist - -# Sample usage of DWA with RRT -""" -if __name__ == "__main__": - dwa_params = { - 'max_speed': 1.0, - 'min_speed': 0.0, - 'max_yaw_rate': 40.0 * np.pi / 180.0, - 'max_accel': 0.2, - 'v_reso': 0.01, - 'yaw_rate_reso': 0.1 * np.pi / 180.0, - 'dt': 0.1, - 'predict_time': 3.0, - 'to_goal_cost_gain': 1.0, - 'speed_cost_gain': 1.0, - 'obstacle_cost_gain': 1.0, - } - - dwa = DWA(dwa_params) - current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] - goal_position = [10, 10] # Example goal position [x, y] - X = None # The search space; not used in this basic example - obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions - - trajectory = dwa.plan(current_position, goal_position, X, obstacles) - print("Best trajectory:", trajectory) -""" diff --git a/dwa_algorithm/DWA3.py b/dwa_algorithm/DWA3.py deleted file mode 100644 index 2af0e0a..0000000 --- a/dwa_algorithm/DWA3.py +++ /dev/null @@ -1,164 +0,0 @@ -# This file is subject to the terms and conditions defined in -# file 'LICENSE', which is part of this source code package. -#the RRT and DWA algorithms function with the optimized collision checking using Bounding Volume Hierarchies (BVH). -#BVH Implementation: We use an R-tree (a type of BVH) to store axis-aligned bounding boxes (AABB) for each obstacle. This allows us to quickly identify which obstacles might intersect with a given point or path segment. -#Efficient Collision Checking: Instead of checking every obstacle for collision, we first use the BVH to narrow down the list to only those obstacles whose bounding boxes intersect with the point or path segment in question. -#Integration with DWA: The code remains the same in terms of how paths are generated and optimized, but now the collision checking is more efficient, leading to faster overall performance. -#All obstacles (both random and predefined) need to be properly registered in the SearchSpace so that the collision checking functions (obstacle_free and collision_free) are aware of all obstacles. -# the script avoids both static and dynamic obstacles, it starts from orange dot and its final goal is green. -import sys -import numpy as np -import skfuzzy as fuzz -from skfuzzy import control as ctrl - -# Update the DWA plan method to use the fuzzy controller -class FuzzyController: - def __init__(self): - # Define fuzzy variables - self.distance_to_goal = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_goal') - self.distance_to_obstacle = ctrl.Antecedent(np.arange(0, 101, 1), 'distance_to_obstacle') - - self.to_goal_gain = ctrl.Consequent(np.arange(0, 11, 1), 'to_goal_gain') - self.speed_gain = ctrl.Consequent(np.arange(0, 11, 1), 'speed_gain') - self.obstacle_gain = ctrl.Consequent(np.arange(0, 11, 1), 'obstacle_gain') - - # Auto-membership function with 3 levels: 'poor', 'average', 'good' - self.distance_to_goal.automf(3) - self.distance_to_obstacle.automf(3) - - self.to_goal_gain.automf(3) - self.speed_gain.automf(3) - self.obstacle_gain.automf(3) - - # Define fuzzy rules to cover all cases - rule1 = ctrl.Rule(self.distance_to_goal['poor'] & self.distance_to_obstacle['good'], - (self.to_goal_gain['good'], self.speed_gain['good'], self.obstacle_gain['poor'])) - rule2 = ctrl.Rule(self.distance_to_goal['average'] & self.distance_to_obstacle['average'], - (self.to_goal_gain['average'], self.speed_gain['average'], self.obstacle_gain['average'])) - rule3 = ctrl.Rule(self.distance_to_goal['good'] & self.distance_to_obstacle['poor'], - (self.to_goal_gain['poor'], self.speed_gain['poor'], self.obstacle_gain['good'])) - - # Create control system - self.control_system = ctrl.ControlSystem([rule1, rule2, rule3]) - self.simulation = ctrl.ControlSystemSimulation(self.control_system) - - def compute_gains(self, dist_to_goal, dist_to_obstacle): - # Set inputs - self.simulation.input['distance_to_goal'] = dist_to_goal - self.simulation.input['distance_to_obstacle'] = dist_to_obstacle - - # Perform fuzzy computation - self.simulation.compute() - - # Ensure the fuzzy system has outputted the necessary gains - return (self.simulation.output.get('to_goal_gain', 0), - self.simulation.output.get('speed_gain', 0), - self.simulation.output.get('obstacle_gain', 0)) - -class DWA: - def __init__(self, config): - self.config = config - self.fuzzy_controller = FuzzyController() - - def plan(self, start_point, end_point, X, obstacles): - # Compute distance to goal and nearest obstacle - dist_to_goal = np.linalg.norm(np.array(start_point[:2]) - np.array(end_point[:2])) - dist_to_obstacle = min([np.linalg.norm(np.array(start_point[:2]) - np.array(obs[:2])) - obs[4] for obs in obstacles]) - - # Get fuzzy-adjusted weights - to_goal_gain, speed_gain, obstacle_gain = self.fuzzy_controller.compute_gains(dist_to_goal, dist_to_obstacle) - # Generate dynamic window and evaluate possible trajectories - dw = self.calc_dynamic_window(start_point) - best_trajectory = None - min_cost = float('inf') - - for v in np.arange(dw[0], dw[1], self.config['v_reso']): - for y in np.arange(dw[2], dw[3], self.config['yaw_rate_reso']): - trajectory = self.generate_trajectory(start_point, v, y) - if X.collision_free(trajectory[0][:5], trajectory[-1][:5], self.config['dt']): - cost = (to_goal_gain * self.calc_to_goal_cost(trajectory[-1], end_point) + - speed_gain * (self.config['max_speed'] - trajectory[-1][3]) + - obstacle_gain * self.calc_obstacle_cost(trajectory, obstacles)) - if cost < min_cost: - min_cost = cost - best_trajectory = trajectory - - return best_trajectory - - def calc_dynamic_window(self, current_position): - # Dynamic window calculation, returning individual scalar values instead of a list of tuples - v_min = max(self.config['min_speed'], current_position[3] - self.config['max_accel'] * self.config['dt']) - v_max = min(self.config['max_speed'], current_position[3] + self.config['max_accel'] * self.config['dt']) - w_min = max(-self.config['max_yaw_rate'], current_position[4] - self.config['max_accel'] * self.config['dt']) - w_max = min(self.config['max_yaw_rate'], current_position[4] + self.config['max_accel'] * self.config['dt']) - - return [v_min, v_max, w_min, w_max] - - def generate_trajectory(self, current_position, v, w): - # Generate trajectory for a given velocity and yaw rate - trajectory = [current_position] - time = 0 - - while time <= self.config['predict_time']: - next_position = self.motion_model(trajectory[-1], v, w) - trajectory.append(next_position) - time += self.config['dt'] - - return trajectory - - def motion_model(self, state, v, w): - # Update the state [x, y, theta, v, w] given current velocities - x, y, theta, _, _ = state[:5] # Ensure only the first 5 elements are considered - x += v * np.cos(theta) * self.config['dt'] - y += v * np.sin(theta) * self.config['dt'] - theta += w * self.config['dt'] - return [x, y, theta, v, w] - - def calc_cost(self, trajectory, goal_position, obstacles): - # Cost function combining distance to goal, speed, and distance to obstacles - to_goal_cost = self.config['to_goal_cost_gain'] * self.calc_to_goal_cost(trajectory[-1], goal_position) - speed_cost = self.config['speed_cost_gain'] * (self.config['max_speed'] - trajectory[-1][3]) - obstacle_cost = self.config['obstacle_cost_gain'] * self.calc_obstacle_cost(trajectory, obstacles) - - return to_goal_cost + speed_cost + obstacle_cost - - def calc_to_goal_cost(self, state, goal_position): - # Euclidean distance to the goal - return np.linalg.norm([state[0] - goal_position[0], state[1] - goal_position[1]]) - - def calc_obstacle_cost(self, trajectory, obstacles): - # Calculate the distance to the closest obstacle - min_dist = float('inf') - for point in trajectory: - for obs in obstacles: - dist = np.linalg.norm([point[0] - obs[0], point[1] - obs[1]]) - if dist < min_dist: - min_dist = dist - return min_dist - -# Sample usage of DWA with RRT -""" -if __name__ == "__main__": - dwa_params = { - 'max_speed': 1.0, - 'min_speed': 0.0, - 'max_yaw_rate': 40.0 * np.pi / 180.0, - 'max_accel': 0.2, - 'v_reso': 0.01, - 'yaw_rate_reso': 0.1 * np.pi / 180.0, - 'dt': 0.1, - 'predict_time': 3.0, - 'to_goal_cost_gain': 1.0, - 'speed_cost_gain': 1.0, - 'obstacle_cost_gain': 1.0, - } - - dwa = DWA(dwa_params) - current_position = [0, 0, 0, 0, 0] # Example starting position [x, y, theta, v, w] - goal_position = [10, 10] # Example goal position [x, y] - X = None # The search space; not used in this basic example - obstacles = [(5, 5), (7, 8)] # Example obstacles; replace with actual obstacle positions - - trajectory = dwa.plan(current_position, goal_position, X, obstacles) - print("Best trajectory:", trajectory) -""" diff --git a/dwa_algorithm/__pycache__/DWA.cpython-38.pyc b/dwa_algorithm/__pycache__/DWA.cpython-38.pyc deleted file mode 100644 index 66b5a32fff9dba13abe1746ea51713ea87a136f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3336 zcmZ`*%a0sK8Lz5-OwZH1>xp+a2_`%gJVCJ|UJ1yt7qe10M0)`Qv}`qJPlW-Pl}_0{X^s_*%$`bD?f zVkp1<;vd5&8;t#n2CJom!5(V5P@aU# z;VlSpb9$=1AV|`r2m+f9H&XFghZYwL5goHAU36SjBkFI^DcK8%d(JCIvdS&EvdVTTN*kk2*(n>3n7Uqess?ckMdg<5$~(GV zdN6=E&{mQT2e!T%h2toYr)fAzqTnP+Wp<*S;W!HmeRUM4u?mYgC{%bDM@6Pib<_M1 zWqC2^D$-AHM8`_SX%S4bJSmba)!k7Rj+aCJE8WaK%Zo4?$2mNH;=7;4c@Z4@-Xuu_ 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zjYju_{nkk$?DrgrPAQQr{hWSUqD!}c$V{@2GT0O1GIr&rX$fRw29S7$TKX-s6td z0yt~DT`J`bwz;Mhia$%GBp`r}(*HelXU@!>d59tvAcEo&CndmNvwe${wM9w*R&<;F zhBm|@plP)?R7ufE-@)lg1b_Nd%xd&h`coTLX*6bM!=&RI;JEOid8Ab&|AHZkKa;jI zFGy`!&++d+NffSJW3b4IQ;%-VWrV43|vJ z7~$r^8EVI-TT75h3M7&sz5tfSQj?x7OOunIpuM|%sn87F@};8dr!N&E&!|;ZwV_eV z1Y$Bm5VNkrFZK#eKcmY4v!>rzE^$@wYfB{v(6+RvAQa^=30#`&g5z@Z5f0u$ z`2j9rd>_wq_zE0*;hII4p2Hrro-O!+(Jt#X$x1{qz5%#Is&>z&s9{{w>e BDbD}^ From cb179d8edb85d4835e68ea7743732d8b6259c97f Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:38:48 +0100 Subject: [PATCH 14/45] Delete rrt_algorithms/dwa_algorithm/123 --- rrt_algorithms/dwa_algorithm/123 | 1 - 1 file changed, 1 deletion(-) delete mode 100644 rrt_algorithms/dwa_algorithm/123 diff --git a/rrt_algorithms/dwa_algorithm/123 b/rrt_algorithms/dwa_algorithm/123 deleted file mode 100644 index 8b13789..0000000 --- a/rrt_algorithms/dwa_algorithm/123 +++ /dev/null @@ -1 +0,0 @@ - From 965ab97683d8deaf6496399a959a8c4f28588402 Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:39:12 +0100 Subject: [PATCH 15/45] Create 12 --- rrt_algorithms/dqn_algorithm/12 | 1 + 1 file changed, 1 insertion(+) create mode 100644 rrt_algorithms/dqn_algorithm/12 diff --git a/rrt_algorithms/dqn_algorithm/12 b/rrt_algorithms/dqn_algorithm/12 new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/rrt_algorithms/dqn_algorithm/12 @@ -0,0 +1 @@ + From fdea997b6ae2eb9c7fad41402b65182b6cac726c Mon Sep 17 00:00:00 2001 From: Shabnam Sadeghi Esfahlani Date: Sat, 31 Aug 2024 11:39:26 +0100 Subject: [PATCH 16/45] Add files via upload --- rrt_algorithms/dqn_algorithm/DQN.py | 200 ++++++++++++++++++ .../__pycache__/DQN.cpython-38.pyc | Bin 0 -> 6419 bytes 2 files changed, 200 insertions(+) create mode 100644 rrt_algorithms/dqn_algorithm/DQN.py create mode 100644 rrt_algorithms/dqn_algorithm/__pycache__/DQN.cpython-38.pyc diff --git a/rrt_algorithms/dqn_algorithm/DQN.py b/rrt_algorithms/dqn_algorithm/DQN.py new file mode 100644 index 0000000..61f53fe --- /dev/null +++ b/rrt_algorithms/dqn_algorithm/DQN.py @@ -0,0 +1,200 @@ +import numpy as np +import tensorflow as tf +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import Adam +from collections import deque +import random +import sys +import uuid +import numpy as np +import skfuzzy as fuzz +from skfuzzy import control as ctrl +sys.path.append('/home/dell/rrt-algorithms') + +from rrt_algorithms.rrt.rrt import RRT +from rrt_algorithms.search_space.search_space2 import SearchSpace +from rrt_algorithms.utilities.plotting2 import Plot +from rrt_algorithms.dwa_algorithm.DWA3 import DWA +from rrt_algorithms.dwa_algorithm.DWA3 import FuzzyController +from rrt_algorithms.utilities.obstacle_generation2 import generate_random_cylindrical_obstacles +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=2000) + self.gamma = 0.95 # discount rate + self.epsilon = 1.0 # exploration rate + self.epsilon_min = 0.01 + self.epsilon_decay = 0.995 + self.learning_rate = 0.001 + self.model = self._build_model() + + def _build_model(self): + model = Sequential() + model.add(Dense(24, input_dim=6, activation='relu')) # Input_dim should be 6 + model.add(Dense(24, activation='relu')) + model.add(Dense(self.action_size, activation='linear')) + model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate)) + return model + + def remember(self, state, action, reward, next_state, done): + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + if np.random.rand() <= self.epsilon: + return random.randrange(self.action_size) + act_values = self.model.predict(state) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + # Ensure there are enough samples in memory to form a full batch + if len(self.memory) < batch_size: + return + + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = (reward + self.gamma * + np.amax(self.model.predict(next_state)[0])) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + if self.epsilon > self.epsilon_min: + self.epsilon *= self.epsilon_decay + +class DroneEnv: + def __init__(self, X, start, goal, obstacles, dwa_params): + self.X = X + self.start = start + self.goal = goal + self.obstacles = obstacles + self.dwa_params = dwa_params + self.state_size = 6 + self.action_size = 3 + self.agent = DQNAgent(self.state_size, self.action_size) + self.reset() + + def reset(self): + self.state = np.array(self.start + (0.0, 0.0, 0.0)) + return self.state.reshape(1, -1) + + def step(self, action): + velocity_change, yaw_change = self.map_action_to_control(action) + next_state, reward, done = self.perform_action(velocity_change, yaw_change) + return next_state, reward, done + + def train(self, episodes, batch_size=64): # Set your batch size here + for episode in range(episodes): + state = self.reset() + total_reward = 0 + done = False + + while not done: + action = self.agent.act(state) + next_state, reward, done = self.step(action) + total_reward += reward + self.agent.remember(state, action, reward, next_state, done) + state = next_state + + # Replay experiences with the set batch size + self.agent.replay(batch_size) + + print(f"Episode: {episode + 1}/{episodes}, Total Reward: {total_reward}") + + + def compute_reward(self, state, path): + # Distance to the goal + distance_to_goal = np.linalg.norm(np.array(state[:2]) - np.array(self.goal[:2])) + + # Obstacle clearance + obstacle_clearance = self.min_obstacle_clearance(path, self.obstacles) + + # Path smoothness (penalizing sharp turns) + path_smoothness = self.path_smoothness(path) + + # Energy usage (penalizing high energy usage) + energy_usage = self.compute_energy_usage(path, state[3]) # state[3] is the current velocity + + # Calculate the reward + reward = -distance_to_goal # Encourage getting closer to the goal + + # Penalize getting too close to obstacles + if obstacle_clearance < 1.0: # 1.0 is a threshold, you can adjust this + reward -= 100 * (1.0 - obstacle_clearance) + + # Penalize sharp turns in the path + reward -= 10 * path_smoothness + + # Penalize high energy usage + reward -= 0.1 * energy_usage + + # Small positive reward for staying alive (not crashing) + reward += 1.0 + + return reward + + + def perform_action(self, velocity_change, yaw_change): + state = self.state + new_velocity = state[3] + velocity_change + new_yaw_rate = state[4] + yaw_change + + # Ensure the state vector always has 6 elements + state = np.array([state[0], state[1], state[2], new_velocity, new_yaw_rate, state[5]]) # Adjust the last value as needed + + dwa = DWA(self.dwa_params) + local_path = dwa.plan(state, self.goal, self.X, self.obstacles) + + if not local_path: + reward = -100 # Collision penalty + done = True + updated_state = state # Maintain the current state + else: + # Ensure the state from local_path[-1] is extended to 6 elements + updated_state = np.array(local_path[-1]) + if len(updated_state) == 5: + updated_state = np.append(updated_state, state[5]) # Maintain the last element (e.g., energy usage or other feature) + + reward = self.compute_reward(updated_state, local_path) + done = np.linalg.norm(np.array(updated_state[:3]) - np.array(self.goal[:3])) < 1.0 # Check if the goal is reached + + print("State shape after update:", updated_state.shape) + return updated_state.reshape(1, -1), reward, done # This return should be inside the function + + +# Ensure all other parts of the code that handle the state are consistent with the 6-element structure. + + def min_obstacle_clearance(self, path, obstacles): + min_clearance = float('inf') + for point in path: + for obs in obstacles: + clearance = np.linalg.norm(np.array(point[:2]) - np.array(obs[:2])) - obs[4] + if clearance < min_clearance: + min_clearance = clearance + return min_clearance + def path_smoothness(self, path): + total_curvature = 0.0 + for i in range(1, len(path) - 1): + vec1 = np.array(path[i][:3]) - np.array(path[i-1][:3]) # Use the first 3 elements if these are the coordinates + vec2 = np.array(path[i+1][:3]) - np.array(path[i][:3]) + angle = np.arccos(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))) + total_curvature += angle + return total_curvature + + def compute_energy_usage(self, path, velocity): + energy = 0.0 + for i in range(1, len(path)): + distance = np.linalg.norm(np.array(path[i][:3]) - np.array(path[i-1][:3])) + energy += distance * velocity + return energy + + + + def map_action_to_control(self, action): + # Simplified action space + velocity_change = [-0.1, 0.0, 0.1][action % 3] # Example 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