Path planning

IR-SIM includes grid- and sampling-based path planners for computing a collision-free path on an occupancy map. Planners are used programmatically: you build a map from the world, plan a path from the robot’s state to its goal, and (optionally) let the robot follow it with a dash behavior or your own controller.

Overview

Every planner follows the same four-step workflow:

1. Build the map: env.get_map(resolution=...) returns the occupancy grid the planner searches (resolution is the planning cell size in meters).

2. Create the planner: instantiate one planner from irsim.lib.path_planners.

3. Plan: planner.planning(start, goal) returns a trajectory (list of [x, y] points), or None if no path is found.

4. Draw / follow: env.draw_trajectory(trajectory) overlays the path; feed it to a controller to follow it.

The start and goal come from the scene: env.get_robot_state() and env.get_robot_info().goal.

Supported algorithms

Planner	Import from irsim.lib.path_planners	Type	Use when
A*	AStarPlanner	grid, 8-neighbour	you want a fast, optimal grid path
JPS	JPSPlanner	grid (optimised A*)	same paths as A*, faster on open grids
RRT	RRT	sampling	non-grid / Shapely obstacles; a feasible (not optimal) path
RRT*	RRTStar	sampling	shorter, optimised paths via rewiring
Informed RRT*	InformedRRTStar	sampling	faster convergence to the optimum after a first solution
PRM	PRMPlanner	sampling roadmap	many queries against the same static map

Grid planners (A*, JPS) and PRM search the grid occupancy from obstacle_map; RRT / RRT* / Informed RRT* can use the same grid or Shapely obstacle geometry.

Quick example

Plan an A* path on a Perlin-noise grid and draw it. Pass show_animation=True to watch the search expand live.

Python

import irsim
from irsim.lib.path_planners import AStarPlanner

env = irsim.make("path_planning.yaml", save_ani=False, full=False)

# 1. occupancy grid from the world's obstacle_map
env_map = env.get_map(resolution=0.2)

# 2. planner
planner = AStarPlanner(env_map)

# 3. plan from the robot's state to its goal
robot_state = env.get_robot_state()
goal_xy = env.get_robot_info().goal[:2, 0].tolist()
trajectory = planner.planning(robot_state, goal_xy, show_animation=True)

# 4. draw the path as a red line
if trajectory is not None:
    env.draw_trajectory(trajectory, traj_type="r-")

env.end(5)

YAML (path_planning.yaml)

world:
  height: 20
  width: 30
  step_time: 0.1
  obstacle_map:           # procedural occupancy grid, built at load time
    name: perlin
    resolution: 0.1
    complexity: 0.08
    fill: 0.15
    fractal: 1
    attenuation: 0.5
    seed: 56

robot:
  - kinematics: {name: 'diff'}
    shape: {name: 'circle', radius: 0.2}
    state: [2, 2, 0]
    goal: [24, 16, 0]
    behavior: {name: 'dash'}

Demonstration

Swap AStarPlanner for any other planner: only the constructor changes; planning() and draw_trajectory() stay the same.

Planner constructors

The first argument is always the map; sampling planners also take the robot (for collision radius) and tuning parameters.

from irsim.lib.path_planners import (
    AStarPlanner, JPSPlanner, RRT, RRTStar, InformedRRTStar, PRMPlanner,
)

env_map = env.get_map(resolution=0.2)   # 0.1 is typical for the RRT family

AStarPlanner(env_map)
JPSPlanner(env_map)
RRT(env_map, robot=env.robot, expand_dis=1.5, max_iter=5000)
RRTStar(env_map, robot=env.robot, expand_dis=1.5, max_iter=5000)
InformedRRTStar(env_map, robot=env.robot, expand_dis=1.5, max_iter=5000)
PRMPlanner(env_map, robot_radius=env.robot.radius, n_sample=500, n_knn=10)

planning(start, goal, show_animation=False) returns a list of [x, y] points or None. For the sampling planners, planner.end.cost and len(planner.node_list) report the path cost and tree size.

Runnable examples

Scripts under usage/20path_planning run every planner on the shared path_planning.yaml:

Script	Planner
path_planning_astar.py	A*
path_planning_jps.py	Jump Point Search
path_planning_rrt.py	RRT
path_planning_rrt_star.py	RRT*
path_planning_informed_rrt_star.py	Informed RRT*
path_planning_prm.py	PRM

See the irsim.lib.path_planners section of the API Reference for the full planner API.