Introduction

Motion planning plans the state sequence of the robot without conflict between the start and goal.

Motion planning mainly includes Path planning and Trajectory planning.

• Path Planning: It's based on path constraints (such as obstacles), planning the optimal path sequence for the robot to travel without conflict between the start and goal.
• Trajectory planning: It plans the motion state to approach the global path based on kinematics, dynamics constraints and path sequence.

This repository provides the implement of common Motion planning algorithm, welcome your star & fork & PR.

The theory analysis can be found at motion-planning

We also provide ROS C++ version at https://github.com/ai-winter/ros_motion_planning and Matlab Version at https://github.com/ai-winter/matlab_motion_planning

Quick Start

The file structure is shown below

├─gif
├─graph_search
├─sample_search
├─utils
└─main.py

The global planning algorithm implementation is in the folder graph_search and sample_search; The local planning algorithm implementation is in the folder local_planner.

To start simulation, open ./main.py and select the algorithm, for example

if __name__ == '__main__':
    '''
    sample search
    '''
    # build environment
    start = (18, 8)
    goal = (37, 18)
    env = Map(51, 31)

    planner = InformedRRT(start, goal, env, max_dist=0.5, r=12, sample_num=1500)

    # animation
    planner.run()

Version

Global Planner

Planner	Version	Animation
GBFS
Dijkstra
A*
JPS
D*
LPA*
D* Lite
RRT
RRT*
Informed RRT
RRT-Connect

Local Planner

Planner	Version	Animation
PID
APF
DWA
TEB
MPC
Lattice

Intelligent Algorithm

Planner	Version	Animation
ACO
GA
PSO
ABC

Papers

Search-based Planning

• A*: A Formal Basis for the heuristic Determination of Minimum Cost Paths
• JPS: Online Graph Pruning for Pathfinding On Grid Maps
• Lifelong Planning A*: Lifelong Planning A*
• D*: Optimal and Efficient Path Planning for Partially-Known Environments
• D* Lite: D* Lite

Sample-based Planning

• RRT: Rapidly-Exploring Random Trees: A New Tool for Path Planning
• RRT-Connect: RRT-Connect: An Efficient Approach to Single-Query Path Planning
• RRT*: Sampling-based algorithms for optimal motion planning
• Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal heuristic

Local Planning

• DWA: The Dynamic Window Approach to Collision Avoidance

Acknowledgment

• Our visualization and animation framework of Python Version refers to https://github.com/zhm-real/PathPlanning. Thanks sincerely.