diff --git a/README.md b/README.md index 7ffc9670..78645b88 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,7 @@ Python sample codes and documents about Autonomous vehicle control algorithm. Th * [Hybrid A*](#hybrid-a) * [Dijkstra](#dijkstra) * [RRT](#rrt) + * [RRT*](#rrt-star) * [Informed RRT*](#informed-rrt) * [Path Tracking](#path-tracking) * [Pure pursuit Path Tracking](#pure-pursuit-path-tracking) @@ -129,6 +130,11 @@ Planning ![](src/simulations/path_planning/rrt_path_planning/rrt_search.gif) Navigation ![](src/simulations/path_planning/rrt_path_planning/rrt_navigate.gif) +#### RRT* +Planning +![](src/simulations/path_planning/rrt_star_path_planning/rrt_star_search.gif) +Navigation +![](src/simulations/path_planning/rrt_star_path_planning/rrt_star_navigate.gif) #### Informed RRT* Planning ![](src/simulations/path_planning/informed_rrt_star_path_planning/informed_rrt_star_search.gif) diff --git a/src/components/plan/rrt_star/rrt_star_path_planner.py b/src/components/plan/rrt_star/rrt_star_path_planner.py new file mode 100644 index 00000000..74feb733 --- /dev/null +++ b/src/components/plan/rrt_star/rrt_star_path_planner.py @@ -0,0 +1,507 @@ +""" +rrt_star_path_planner.py + +Author: Auto-generated + +This module implements the RRT* (Rapidly-exploring Random Tree Star) algorithm for path planning. +RRT* is an improved version of RRT that includes path optimization through rewiring. +After adding a new node, RRT* finds nearby nodes and optimizes the tree structure by +rewiring nodes to use better parents, resulting in asymptotically optimal paths. +""" + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.animation as anm +import sys +from pathlib import Path +from matplotlib.colors import ListedColormap + +abs_dir_path = str(Path(__file__).absolute().parent) +relative_path = "/../../../components/" +relative_simulations = "/../../../simulations/" + + +sys.path.append(abs_dir_path + relative_path + "visualization") +sys.path.append(abs_dir_path + relative_path + "state") +sys.path.append(abs_dir_path + relative_path + "obstacle") +sys.path.append(abs_dir_path + relative_path + "plan/astar") +sys.path.append(abs_dir_path + relative_path + "mapping/grid") + + +from state import State +from obstacle import Obstacle +from obstacle_list import ObstacleList +from binary_occupancy_grid import BinaryOccupancyGrid +from min_max import MinMax +import json + + +class RrtStarPathPlanner: + def __init__(self, start, goal, map_file, x_lim=None, y_lim=None, path_filename=None, gif_name=None, max_iterations=5000, step_size=0.5, goal_sample_rate=0.05, rewire_radius=None): + """ + Initialize the RRT* planner. + Args: + start: (x, y) tuple for start position in world coordinates. + goal: (x, y) tuple for goal position in world coordinates. + map_file: Path to the grid map file. + x_lim: MinMax object for x-axis range of the grid. + y_lim: MinMax object for y-axis range of the grid. + path_filename: Path to save the generated path. + gif_name: Path to save the animation as a GIF. + max_iterations: Maximum number of iterations for RRT*. + step_size: Maximum distance to extend the tree in each step. + goal_sample_rate: Probability of sampling the goal position. + rewire_radius: Radius for finding nearby nodes for rewiring. If None, uses step_size * 2. + """ + self.start = start + self.goal = goal + self.explored_nodes = [] + self.tree_edges = [] # Store edges for visualization + self.grid = self.load_grid_from_file(map_file) + x_min, x_max = x_lim.min_value(), x_lim.max_value() + y_min, y_max = y_lim.min_value(), y_lim.max_value() + self.resolution = (x_max - x_min) / self.grid.shape[1] # Width of each cell + self.x_range = np.arange(x_min, x_max, self.resolution) + self.y_range = np.arange(y_min, y_max, self.resolution) + self.x_min, self.x_max = x_min, x_max + self.y_min, self.y_max = y_min, y_max + self.path = [] + self.path_filename = path_filename + self.max_iterations = max_iterations + self.step_size = step_size + self.goal_sample_rate = goal_sample_rate + self.rewire_radius = rewire_radius if rewire_radius is not None else step_size * 2.0 + self.start_idx = self._world_to_grid(self.start) + self.goal_idx = self._world_to_grid(self.goal) + + self.search() + self.visualize_search(gif_name) + + def load_grid_from_file(self, file_path): + """ + Load a grid from a file and convert it to a numpy array. + Args: + file_path: Path to the file containing the grid data. + Returns: + grid: A numpy array representing the grid. + """ + file_extension = Path(file_path).suffix + + if file_extension == '.npy': + grid = np.load(file_path) + elif file_extension == '.png': + grid = plt.imread(file_path) + if grid.ndim == 3: # If the image has color channels, convert to grayscale + grid = np.mean(grid, axis=2) + grid = (grid > 0.5).astype(int) # Binarize the image + elif file_extension == '.json': + with open(file_path, 'r') as f: + grid_data = json.load(f) + grid = np.array(grid_data) + else: + raise ValueError(f"Unsupported file format: {file_extension}") + + return grid + + def _world_to_grid(self, world_point): + """ + Convert world coordinates to grid indices. + Args: + world_point: (x, y) tuple in world coordinates. + Returns: + (grid_x, grid_y): Corresponding grid indices. + """ + grid_x = int((world_point[0] - self.x_range[0]) / self.resolution) + grid_y = int((world_point[1] - self.y_range[0]) / self.resolution) + return (grid_x, grid_y) + + def is_valid(self, x, y): + """ + Check if a grid cell is within bounds and not an obstacle. + Converts world coordinates to grid indices, accounting for negative min values. + """ + return (0 <= x < self.grid.shape[1] and + 0 <= y < self.grid.shape[0] and + self.grid[y, x] == 0) + + def line_collision_check(self, point1, point2, num_samples=20): + """ + Check if the line segment between two points collides with obstacles. + Args: + point1: (x, y) tuple for the first point in world coordinates. + point2: (x, y) tuple for the second point in world coordinates. + num_samples: Number of points to sample along the line. + Returns: + True if the line is collision-free, False otherwise. + """ + for i in range(num_samples + 1): + t = i / num_samples + x = point1[0] + t * (point2[0] - point1[0]) + y = point1[1] + t * (point2[1] - point1[1]) + grid_x, grid_y = self._world_to_grid((x, y)) + if not self.is_valid(grid_x, grid_y): + return False + return True + + def get_nearest_node(self, nodes, random_point): + """ + Find the nearest node in the tree to a random point. + Args: + nodes: List of nodes in the tree. + random_point: The random point to find the nearest node to. + Returns: + The nearest node in the tree. + """ + distances = [np.linalg.norm(np.array(node) - np.array(random_point)) for node in nodes] + nearest_idx = np.argmin(distances) + return nodes[nearest_idx], nearest_idx + + def extend(self, nearest_node, random_point): + """ + Extend the tree from the nearest node towards the random point. + Args: + nearest_node: The nearest node in the tree. + random_point: The random point to extend towards. + Returns: + A new node if extension is possible, None otherwise. + """ + direction = np.array(random_point) - np.array(nearest_node) + distance = np.linalg.norm(direction) + if distance < 1e-6: # Avoid division by zero + return None + direction = direction / distance + new_point = np.array(nearest_node) + direction * min(self.step_size, distance) + new_point = tuple(new_point) + return new_point + + def get_nearby_nodes(self, nodes, point, radius): + """ + Find all nodes within a given radius of a point. + Args: + nodes: List of nodes in the tree. + point: The point to search around. + radius: Search radius. + Returns: + List of (node, index) tuples for nodes within radius. + """ + nearby = [] + for idx, node in enumerate(nodes): + distance = np.linalg.norm(np.array(node) - np.array(point)) + if distance <= radius: + nearby.append((node, idx)) + return nearby + + def get_cost(self, nodes, parent, node_idx): + """ + Calculate the cost from start to a node. + Args: + nodes: List of all nodes. + parent: Dictionary mapping node index to parent index. + node_idx: Index of the node. + Returns: + Total cost from start to the node. + """ + cost = 0.0 + current_idx = node_idx + while current_idx is not None and current_idx != 0: + parent_idx = parent.get(current_idx) + if parent_idx is None: + break + cost += np.linalg.norm(np.array(nodes[current_idx]) - np.array(nodes[parent_idx])) + current_idx = parent_idx + return cost + + def search(self): + """ + RRT* algorithm to find an optimized path from start to goal. + """ + nodes = [self.start] # Initialize tree with start node + parent = {0: None} # Parent relationship in the tree + cost = {0: 0.0} # Cost from start to each node + print(f"Start: {self.start}, Goal: {self.goal}") + + for iteration in range(self.max_iterations): + # Sample a random point or the goal with a certain probability + if np.random.rand() < self.goal_sample_rate: + random_point = self.goal + else: + random_point = ( + np.random.uniform(self.x_min, self.x_max), + np.random.uniform(self.y_min, self.y_max) + ) + + # Find the nearest node in the tree to the random point + nearest_node, nearest_idx = self.get_nearest_node(nodes, random_point) + + # Try to extend the tree from the nearest node towards the random point + new_node = self.extend(nearest_node, random_point) + if new_node is None: + continue + + # Check if the line segment from nearest_node to new_node is collision-free + if not self.line_collision_check(nearest_node, new_node): + continue + + # Find nearby nodes for potential rewiring + nearby_nodes = self.get_nearby_nodes(nodes, new_node, self.rewire_radius) + + # Choose the best parent (lowest cost) + best_parent_idx = nearest_idx + best_cost = cost[nearest_idx] + np.linalg.norm(np.array(new_node) - np.array(nearest_node)) + + for nearby_node, nearby_idx in nearby_nodes: + if nearby_idx == nearest_idx: + continue + # Check if connecting through this node is collision-free + if self.line_collision_check(nearby_node, new_node): + new_cost = cost[nearby_idx] + np.linalg.norm(np.array(new_node) - np.array(nearby_node)) + if new_cost < best_cost: + best_cost = new_cost + best_parent_idx = nearby_idx + + # Add the new node to the tree + node_idx = len(nodes) + nodes.append(new_node) + parent[node_idx] = best_parent_idx + cost[node_idx] = best_cost + self.explored_nodes.append(new_node) + + # Remove old edge if parent changed, add new edge + if best_parent_idx != nearest_idx: + # Remove old edge from visualization (if it exists) + old_edge = (nodes[nearest_idx], new_node) + if old_edge in self.tree_edges: + self.tree_edges.remove(old_edge) + self.tree_edges.append((nodes[best_parent_idx], new_node)) + + # Rewire: Check if nearby nodes can get a better path through new_node + for nearby_node, nearby_idx in nearby_nodes: + if nearby_idx == node_idx: + continue + # Check if connecting through new_node gives better cost + if self.line_collision_check(new_node, nearby_node): + new_cost = cost[node_idx] + np.linalg.norm(np.array(nearby_node) - np.array(new_node)) + if new_cost < cost[nearby_idx]: + # Rewire: change parent of nearby_node to new_node + old_parent_idx = parent[nearby_idx] + if old_parent_idx is not None: + # Remove old edge + old_edge = (nodes[old_parent_idx], nearby_node) + if old_edge in self.tree_edges: + self.tree_edges.remove(old_edge) + # Update parent and cost + parent[nearby_idx] = node_idx + cost[nearby_idx] = new_cost + # Add new edge + self.tree_edges.append((new_node, nearby_node)) + # Update costs of all descendants (propagate cost change) + self._update_descendant_costs(nodes, parent, cost, nearby_idx) + + # Check if the new node is close enough to the goal + distance_to_goal = np.linalg.norm(np.array(new_node) - np.array(self.goal)) + if distance_to_goal < self.step_size * 2: + # Try to connect to the goal + if self.line_collision_check(new_node, self.goal): + print(f"Goal reached at iteration {iteration}") + # Find best parent for goal + goal_nearby = self.get_nearby_nodes(nodes, self.goal, self.rewire_radius) + best_goal_parent = node_idx + best_goal_cost = cost[node_idx] + np.linalg.norm(np.array(self.goal) - np.array(new_node)) + + for nearby_node, nearby_idx in goal_nearby: + if self.line_collision_check(nearby_node, self.goal): + new_cost = cost[nearby_idx] + np.linalg.norm(np.array(self.goal) - np.array(nearby_node)) + if new_cost < best_goal_cost: + best_goal_cost = new_cost + best_goal_parent = nearby_idx + + goal_node_idx = len(nodes) + parent[goal_node_idx] = best_goal_parent + cost[goal_node_idx] = best_goal_cost + nodes.append(self.goal) + self.explored_nodes.append(self.goal) + self.tree_edges.append((nodes[best_goal_parent], self.goal)) + self.path = self._reconstruct_rrt_path(nodes, parent, goal_node_idx) + sparse_path = self.make_sparse_path(self.path) + self.save_path(sparse_path, self.path_filename) + return + + print(f"Goal not found within {self.max_iterations} iterations.") + # If goal is not found, return the path to the closest node + if len(nodes) > 1: + distances = [np.linalg.norm(np.array(node) - np.array(self.goal)) for node in nodes] + closest_idx = np.argmin(distances) + self.path = self._reconstruct_rrt_path(nodes, parent, closest_idx) + sparse_path = self.make_sparse_path(self.path) + self.save_path(sparse_path, self.path_filename) + + def _update_descendant_costs(self, nodes, parent, cost, node_idx): + """ + Update costs of all descendants after rewiring. + Args: + nodes: List of all nodes. + parent: Dictionary mapping node index to parent index. + cost: Dictionary mapping node index to cost. + node_idx: Index of the node whose cost changed. + """ + # Find all children of this node + children = [idx for idx, p_idx in parent.items() if p_idx == node_idx] + for child_idx in children: + if child_idx in cost and child_idx in parent and parent[child_idx] is not None: + parent_idx = parent[child_idx] + cost[child_idx] = cost[parent_idx] + np.linalg.norm(np.array(nodes[child_idx]) - np.array(nodes[parent_idx])) + # Recursively update children + self._update_descendant_costs(nodes, parent, cost, child_idx) + + def _reconstruct_rrt_path(self, nodes, parent, goal_idx): + """ + Reconstruct the path from start to goal in the RRT* tree. + Args: + nodes: List of nodes in the tree. + parent: Dictionary of parent relationships. + goal_idx: Index of the goal node in the tree. + Returns: + path: List of (x, y) tuples representing the path. + """ + path = [] + current_idx = goal_idx + while current_idx is not None: + path.append(nodes[current_idx]) + current_idx = parent.get(current_idx) + return path[::-1] # Reverse to get path from start to goal + + def make_sparse_path(self, path, num_points=20): + """ + Make the path sparse for use with CubicSplineCourse. + Args: + path: Full path as a list of (x, y) tuples in world coordinates. + num_points: Number of points to include in the sparse path. + Returns: + sparse_path: A sparse path with evenly spaced world coordinates. + """ + if len(path) <= num_points: + return path + + indices = np.linspace(0, len(path) - 1, num_points, dtype=int) + sparse_path = [path[i] for i in indices] + return sparse_path + + def save_path(self, path, filename): + """Save path to a json file.""" + if filename is None: + return + if not Path(filename).exists(): + Path(filename).touch() + path = [node for node in path] + with open(filename, "w") as f: + json.dump(path, f) + + def visualize_search(self, gif_name=None): + print(f"Exploring {len(self.explored_nodes)} nodes.") + if not self.explored_nodes: + print("Error: No explored nodes. Ensure search() is executed before visualize_search().") + return + + figure = plt.figure(figsize=(10, 8)) + axes = figure.add_subplot(111) + axes.set_aspect("equal") + axes.set_xlabel("X [m]", fontsize=15) + axes.set_ylabel("Y [m]", fontsize=15) + + self.anime = anm.FuncAnimation( + figure, + self.update_frame, + fargs=(axes, self.path), + frames=len(self.explored_nodes) + len(self.path), + interval=50, + repeat=False, + ) + + if gif_name is not None: + try: + print("Saving animation...") + self.anime.save(gif_name, writer="pillow") + print("Animation saved successfully.") + except Exception as e: + print(f"Error saving animation: {e}") + else: + plt.show() + + plt.clf() + plt.close() + + def update_frame(self, i, axes, path): + """ + Update frame for visualization showing RRT* tree growth and path. + Args: + i: Current frame index. + axes: Matplotlib axes to draw on. + path: The reconstructed path to draw after exploration. + """ + axes.clear() + + colors = [ + [1.0, 1.0, 1.0], # Free space (white) + [0.0, 0.0, 0.0], # Obstacles (black) + ] + custom_cmap = ListedColormap(colors) + + axes.imshow(self.grid, extent=[self.x_range[0], self.x_range[-1], self.y_range[0], self.y_range[-1]], + origin='lower', cmap=custom_cmap, alpha=0.8) + + # Draw tree edges up to the current frame + num_edges = int((i / (len(self.tree_edges) + len(self.path))) * len(self.tree_edges)) + for j in range(min(num_edges, len(self.tree_edges))): + edge = self.tree_edges[j] + axes.plot([edge[0][0], edge[1][0]], [edge[0][1], edge[1][1]], 'b-', linewidth=0.5, alpha=0.5) + + # Draw explored nodes + if self.explored_nodes: + explored_x = [node[0] for node in self.explored_nodes[:min(i, len(self.explored_nodes))]] + explored_y = [node[1] for node in self.explored_nodes[:min(i, len(self.explored_nodes))]] + axes.scatter(explored_x, explored_y, c='lightblue', s=5, alpha=0.6) + + # Draw path + path_start_frame = len(self.explored_nodes) + if i >= path_start_frame and len(path) > 0: + path_index = min(i - path_start_frame, len(path) - 1) + path_segment = path[:path_index + 1] + if len(path_segment) > 1: + path_x = [node[0] for node in path_segment] + path_y = [node[1] for node in path_segment] + axes.plot(path_x, path_y, 'g-', linewidth=2, label="Path") + + # Draw start and goal + axes.plot(self.start[0], self.start[1], 'go', markersize=10, label="Start") + axes.plot(self.goal[0], self.goal[1], 'ro', markersize=10, label="Goal") + + axes.set_xlabel("X [m]", fontsize=15) + axes.set_ylabel("Y [m]", fontsize=15) + axes.set_title(f"RRT* Path Planning - Iteration {i}", fontsize=12) + axes.legend() + axes.set_aspect("equal") + + +if __name__ == "__main__": + + map_file = "map.json" + path_file = "path.json" + gif_path = "rrt_star_search.gif" + + x_lim, y_lim = MinMax(-5, 55), MinMax(-20, 25) + + start = (0, 0) + goal = (50, -10) + + planner = RrtStarPathPlanner( + start, + goal, + map_file, + x_lim=x_lim, + y_lim=y_lim, + path_filename=path_file, + gif_name=gif_path, + max_iterations=5000, + step_size=0.5, + goal_sample_rate=0.05 + ) diff --git a/src/simulations/path_planning/rrt_star_path_planning/map.json b/src/simulations/path_planning/rrt_star_path_planning/map.json new file mode 100644 index 00000000..79434138 --- /dev/null +++ b/src/simulations/path_planning/rrt_star_path_planning/map.json @@ -0,0 +1 @@ +[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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[25.846605971495077, -4.388931138778045], [29.950736447698546, -4.04278169288967], [33.86200715713186, -2.499180139597338], [34.4037782089103, 2.249546008168026], [35.537387898648554, 7.175364663221906], [36.955413401808045, 12.982359157562843], [41.1508007268567, 15.033334469141959], [45.32268614027619, 15.769149518629249], [45.605148069872165, 10.080477147028189], [48.10681315024125, 6.434419004985622], [50.769896921715016, 2.5701130714834957], [50.34038694284904, -1.4179024281044705], [50.0881233442712, -5.288258291790932], [50, -10]] \ No newline at end of file diff --git a/src/simulations/path_planning/rrt_star_path_planning/rrt_star_navigate.gif b/src/simulations/path_planning/rrt_star_path_planning/rrt_star_navigate.gif new file mode 100644 index 00000000..81f1fb81 Binary files /dev/null and b/src/simulations/path_planning/rrt_star_path_planning/rrt_star_navigate.gif differ diff --git a/src/simulations/path_planning/rrt_star_path_planning/rrt_star_path_planning.py b/src/simulations/path_planning/rrt_star_path_planning/rrt_star_path_planning.py new file mode 100644 index 00000000..6208b503 --- /dev/null +++ b/src/simulations/path_planning/rrt_star_path_planning/rrt_star_path_planning.py @@ -0,0 +1,111 @@ +""" +rrt_star_path_planning.py + +Author: Auto-generated +""" + +# import path setting +import numpy as np +import sys +from pathlib import Path + +abs_dir_path = str(Path(__file__).absolute().parent) +relative_path = "/../../../components/" +relative_simulations = "/../../../simulations/" + + +sys.path.append(abs_dir_path + relative_path + "visualization") +sys.path.append(abs_dir_path + relative_path + "state") +sys.path.append(abs_dir_path + relative_path + "vehicle") +sys.path.append(abs_dir_path + relative_path + "obstacle") +sys.path.append(abs_dir_path + relative_path + "mapping/grid") +sys.path.append(abs_dir_path + relative_path + "course/cubic_spline_course") +sys.path.append(abs_dir_path + relative_path + "control/pure_pursuit") +sys.path.append(abs_dir_path + relative_path + "plan/rrt_star") + + +# import component modules +from global_xy_visualizer import GlobalXYVisualizer +from min_max import MinMax +from time_parameters import TimeParameters +from vehicle_specification import VehicleSpecification +from state import State +from four_wheels_vehicle import FourWheelsVehicle +from obstacle import Obstacle +from obstacle_list import ObstacleList +from cubic_spline_course import CubicSplineCourse +from pure_pursuit_controller import PurePursuitController +from rrt_star_path_planner import RrtStarPathPlanner +from binary_occupancy_grid import BinaryOccupancyGrid +import json + + +# flag to show plot figure +# when executed as unit test, this flag is set as false +show_plot = True + + +def main(): + """ + Main process function + """ + + # set simulation parameters + x_lim, y_lim = MinMax(-5, 55), MinMax(-20, 25) + navigation_gif_path = abs_dir_path + relative_simulations + "path_planning/rrt_star_path_planning/rrt_star_navigate.gif" + map_path = abs_dir_path + relative_simulations + "path_planning/rrt_star_path_planning/map.json" + path_filename = abs_dir_path + relative_simulations + "path_planning/rrt_star_path_planning/path.json" + search_gif_path = abs_dir_path + relative_simulations + "path_planning/rrt_star_path_planning/rrt_star_search.gif" + + + vis = GlobalXYVisualizer(x_lim, y_lim, TimeParameters(span_sec=25), show_zoom=False, gif_name=navigation_gif_path) + occ_grid = BinaryOccupancyGrid(x_lim, y_lim, resolution=0.5, clearance=1.5, map_path=map_path) + + obst_list = ObstacleList() + obst_list.add_obstacle(Obstacle(State(x_m=10.0, y_m=15.0), length_m=10, width_m=8)) + obst_list.add_obstacle(Obstacle(State(x_m=40.0, y_m=0.0), length_m=2, width_m=10)) + obst_list.add_obstacle(Obstacle(State(x_m=10.0, y_m=-10.0, yaw_rad=np.rad2deg(45)), length_m=5, width_m=5)) + obst_list.add_obstacle(Obstacle(State(x_m=30.0, y_m=15.0, yaw_rad=np.rad2deg(10)), length_m=5, width_m=2)) + obst_list.add_obstacle(Obstacle(State(x_m=50.0, y_m=15.0, yaw_rad=np.rad2deg(15)), length_m=5, width_m=2)) + obst_list.add_obstacle(Obstacle(State(x_m=25.0, y_m=0.0), length_m=2, width_m=2)) + obst_list.add_obstacle(Obstacle(State(x_m=35.0, y_m=-15.0), length_m=7, width_m=2)) + + vis.add_object(obst_list) + occ_grid.add_object(obst_list) + occ_grid.save_map() + # Easy Goal = (50,22) + # Hard Goal = (50,-10) + planner = RrtStarPathPlanner((0, 0), (50, -10), map_path, x_lim=x_lim, y_lim=y_lim, path_filename=path_filename, gif_name=search_gif_path, + max_iterations=5000, step_size=0.5, goal_sample_rate=0.05) + + # Load sparse path from json file + with open(path_filename, 'r') as f: + sparse_path = json.load(f) + + # Extract x and y coordinates + sparse_x = [point[0] for point in sparse_path] + sparse_y = [point[1] for point in sparse_path] + + # Use with CubicSplineCourse + course = CubicSplineCourse(sparse_x, sparse_y, 20) + vis.add_object(course) + + # create vehicle instance + spec = VehicleSpecification() + pure_pursuit = PurePursuitController(spec, course) + vehicle = FourWheelsVehicle(State(color=spec.color), spec, + controller=pure_pursuit, + show_zoom=False) + + vis.add_object(vehicle) + + # plot figure is not shown when executed as unit test + if not show_plot: vis.not_show_plot() + + # show plot figure + vis.draw() + + +# execute main process +if __name__ == "__main__": + main() diff --git a/src/simulations/path_planning/rrt_star_path_planning/rrt_star_search.gif b/src/simulations/path_planning/rrt_star_path_planning/rrt_star_search.gif new file mode 100644 index 00000000..1b7060f2 Binary files /dev/null and b/src/simulations/path_planning/rrt_star_path_planning/rrt_star_search.gif differ diff --git a/test/test_rrt_star_path_planning.py b/test/test_rrt_star_path_planning.py new file mode 100644 index 00000000..144dc319 --- /dev/null +++ b/test/test_rrt_star_path_planning.py @@ -0,0 +1,21 @@ +""" +Test of RRT* path planning and navigation simulation + +Author: Auto-generated +""" + +from pathlib import Path +import sys +import pytest + +sys.path.append( + str(Path(__file__).absolute().parent) + + "/../src/simulations/path_planning/rrt_star_path_planning" +) +import rrt_star_path_planning + + +def test_simulation(): + rrt_star_path_planning.show_plot = False + + rrt_star_path_planning.main()