irsim.lib.path_planners.probabilistic_road_map

Probabilistic Road Map (PRM) Planner.

Collision precedence:

1. Grid lookup when env_map.grid is not None; if occupied, collision.

2. When the grid reports free or is unavailable, Shapely vs. obstacle_list. (Grid and obstacle_list are combined when both are present.)

author: Atsushi Sakai (@Atsushi_twi)

adapted by: Reinis Cimurs

Classes

Node	Node class for dijkstra search
PRMPlanner	Initialize the PRM planner.

Module Contents

class irsim.lib.path_planners.probabilistic_road_map.Node(x: float, y: float, cost: float, parent_index: int)

Node class for dijkstra search

Initialize Node

参数:

• x (float) -- x position of the node

• y (float) -- y position of the node

• cost (float) -- heuristic cost of the node

• parent_index (int) -- Nodes parent index

x

y

cost

parent_index

class irsim.lib.path_planners.probabilistic_road_map.PRMPlanner(env_map: irsim.world.map.EnvGridMap, robot_radius: float, n_sample: int = 500, n_knn: int = 10, max_edge_len: float = 30.0)

Initialize the PRM planner.

参数:

• env_map -- Environment map (any EnvGridMap compatible object).

• robot_radius -- Robot radius modeled as a circle.

• n_sample -- Number of sampled points.

• n_knn -- Number of nearest neighbors per node.

• max_edge_len -- Maximum allowed edge length.

rr

obstacle_list

min_x

min_y

max_x

max_y

n_sample = 500

n_knn = 10

max_edge_len = 30.0

planning(start_pose: numpy.ndarray, goal_pose: numpy.ndarray, rng: Any | None = None, show_animation: bool = True) → tuple[list[float], list[float]] | None

Plan a path from start to goal using the PRM method.

参数:

• start_pose (np.array) -- start pose [x,y]

• goal_pose (np.array) -- goal pose [x,y]

• rng (Optional) -- Random generator

• show_animation (bool) -- If true, shows the animation of planning process

返回:

xy position array of the final path

返回类型:

(np.array)

check_node(x: float, y: float, rr: float) → bool

Check position for a collision.

参数:

• x -- World x coordinate.

• y -- World y coordinate.

• rr -- Robot radius for the check.

返回:

True if a collision is detected.

is_collision(sx: float, sy: float, gx: float, gy: float) → bool

Check if line between points is acceptable - within edge limits and free of collisions

参数:

• sx (float) -- start x position

• sy (float) -- start y position

• gx (float) -- goal x position

• gy (float) -- goal y position

返回:

True if node is not acceptable. False otherwise

返回类型:

result (bool)

generate_road_map(sample_x: list[float], sample_y: list[float]) → list[list[int]]

Road map generation

参数:

• sample_x (List) -- [m] x positions of sampled points

• sample_y (List) -- [m] y positions of sampled points

返回:

list of edge ids

返回类型:

road_map (List)

static dijkstra_planning(sx: float, sy: float, gx: float, gy: float, road_map: list[list[int]], sample_x: list[float], sample_y: list[float], show_animation: bool) → tuple[list[float], list[float]] | None

参数:

• sx (float) -- start x position [m]

• sy (float) -- start y position [m]

• gx (float) -- goal x position [m]

• gy (float) -- goal y position [m]

• road_map (list) -- list of edge ids

• sample_x (float) -- ??? [m]

• sample_y (float) -- ??? [m]

返回:

Two lists of path coordinates ([x1, x2, ...], [y1, y2, ...]), empty list when no path was found

返回类型:

(tuple(list, list))

static plot_road_map(road_map: list[list[int]], sample_x: list[float], sample_y: list[float]) → None

sample_points(sx: float, sy: float, gx: float, gy: float, rng: Any | None) → tuple[list[float], list[float]]

Generate sample points

参数:

• sx (float) -- start x position [m]

• sy (float) -- start y position [m]

• gx (float) -- goal x position [m]

• gy (float) -- goal y position [m]

• rng -- Random generator

返回:

sample positions

返回类型:

sample (tuple (list, list))