PythonRobotics

Python codes for robotics algorithm.

Table of Contents

• Requirements
• How to use
• Path Planning
  • Dynamic Window Approach
  • Grid based search
    • Dijkstra algorithm
    • A* algorithm
    • Potential Field algorithm
  • Model Predictive Trajectory Generator
    • Path optimization sample
    • Lookup table generation sample
  • State Lattice Planning
    • Uniform polar sampling
    • Biased polar sampling
    • Lane sampling
  • Probabilistic Road-Map (PRM) planning
  • Voronoi Road-Map planning
  • Rapidly-Exploring Random Trees (RRT)
    • Basic RRT
    • RRT*
    • RRT with dubins path
    • RRT* with dubins path
    • RRT* with reeds-sheep path
    • Closed Loop RRT*
  • Cubic spline planning
  • Bezier path planning
  • Dubins path planning
  • Reeds Shepp planning
  • Mix Integer Optimization based model predictive planning and control
• Path tracking
  • Pure pursuit tracking
  • Stanley control
  • Rear wheel feedback control
  • Linear–quadratic regulator (LQR) control
• License
• Author

Requirements

• Python 3.6.x

• numpy

• scipy

• matplotlib

• pyReedsShepp (Only for reeds sheep path and RRTStarCar_reeds_sheep)

• cvxpy (Only for mix integer optimization based model predictive planning and control)

How to use

1. Install the required libraries.

2. Clone this repo.

3. Execute python script in each dir.

4. Add star to this repo if you like it 😃.

Path Planning

Path planning algorithm.

Dynamic Window Approach

This is a 2D navigation sample code with Dynamic Window Approach.

• The Dynamic Window Approach to Collision Avoidance

Grid based search

Dijkstra algorithm

This is a 2D grid based shortest path planning with Dijkstra's algorithm.

In the animation, cyan points are searched nodes.

A* algorithm

This is a 2D grid based shortest path planning with A star algorithm.

In the animation, cyan points are searched nodes.

It's heuristic is 2D Euclid distance.

Potential Field algorithm

This is a 2D grid based path planning with Potential Field algorithm.

In the animation, the blue heat map shows potential value on each grid.

Ref:

• Robotic Motion Planning:Potential Functions

Model Predictive Trajectory Generator

This is a path optimization sample on model predictive trajectory generator.

This algorithm is used for state lattice planner.

Path optimization sample

Lookup table generation sample

Ref:

• Optimal rough terrain trajectory generation for wheeled mobile robots

　

State Lattice Planning

This script is a path planning code with state lattice planning.

This code uses the model predictive trajectory generator to solve boundary problem.

Uniform polar sampling

Biased polar sampling

Lane sampling

Probabilistic Road-Map (PRM) planning

This PRM planner uses Dijkstra method for graph search.

In the animation, blue points are sampled points,

Cyan crosses means searched points with Dijkstra method,

The red line is the final path of PRM.

Ref:

• Probabilistic roadmap - Wikipedia

　　

Voronoi Road-Map planning

This Voronoi road-map planner uses Dijkstra method for graph search.

In the animation, blue points are Voronoi points,

Cyan crosses means searched points with Dijkstra method,

The red line is the final path of Vornoi Road-Map.

Ref:

• Robotic Motion Planning

Rapidly-Exploring Random Trees (RRT)

Basic RRT

Rapidly Randamized Tree Path planning sample.

This script is a simple path planning code with Rapidly-Exploring Random Trees (RRT)

RRT*

This script is a path planning code with RRT *

Ref:

• Incremental Sampling-based Algorithms for Optimal Motion Planning

• Sampling-based Algorithms for Optimal Motion Planningj

RRT with dubins path

Path planning for a car robot with RRT and dubins path planner.

RRT* with dubins path

Path planning for a car robot with RRT* and dubins path planner.

RRT* with reeds-sheep path

)

Path planning for a car robot with RRT* and reeds sheep path planner.

Closed Loop RRT*

A sample code with closed loop RRT*.

Ref:

• Motion Planning in Complex Environments using Closed-loop Prediction

• Real-time Motion Planning with Applications to Autonomous Urban Driving

• [1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction

Cubic spline planning

A sample code for cubic path planning.

This code generates a curvature continious path based on x-y waypoints with cubic spline.

Heading angle of each point can be also calculated analytically.

Bezier path planning

A sample code of Bezier path planning.

It is based on 4 control points Beier path.

If you change the offset distance from start and end point,

You can get different Beizer course:

Ref:

• Continuous Curvature Path Generation Based on B ́ezier Curves for Autonomous Vehicles

Dubins path planning

A sample code for Dubins path planning.

Ref:

• Dubins path - Wikipedia

Reeds Shepp planning

A sample code with Reeds Shepp path planning.

Ref:

• 15.3.2 Reeds-Shepp Curves

• optimal paths for a car that goes both forwards and backwards

• ghliu/pyReedsShepp: Implementation of Reeds Shepp curve.

　

Mix Integer Optimization based model predictive planning and control

A model predictive planning and control code with mixed integer programming.

It is based on this paper.

• MIXED INTEGER PROGRAMMING FOR MULTI-VEHICLE PATH PLANNING

This code used cvxpy as optimization modeling tool,

• Welcome to CVXPY 1.0 — CVXPY 1.0.0 documentation

and Gurobi is used as a solver for mix integer optimization problem.

• Gurobi Optimization - The State-of-the-Art Mathematical Programming Solver

Path tracking

Path tracking algorithm samples.

Pure pursuit tracking

Path tracking simulation with pure pursuit steering control and PID speed control.

Stanley control

Path tracking simulation with Stanley steering control and PID speed control.

Ref:

• Stanley: The robot that won the DARPA grand challenge

• Automatic Steering Methods for Autonomous Automobile Path Tracking

Rear wheel feedback control

Path tracking simulation with rear wheel feedback steering control and PID speed control.

Linear–quadratic regulator (LQR) control

Path tracking simulation with LQR steering control and PID speed control.

License

MIT

Author

Atsushi Sakai (@Atsushi_twi)