PythonRobotics

Python sample codes for robotics algorithm.

Table of Contents

• Requirements
• Path Planning
  • Dynamic Window Approach
  • Grid based search
    • Dijkstra algorithm
    • A* algorithm
  • Model Predictive Trajectory Generator
    • Path optimization sample
    • Lookup table generation sample
  • State Lattice Planning
    • Uniform polar sampling
    • Biased polar sampling
    • Lane sampling
  • 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
  • Dubins path planning
  • Reeds Shepp planning
  • Mix Integer Optimization based model predictive planning and control
• Path tracking
  • Pure pursuit tracking
  • 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)

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.

Model Predictive Trajectory Generator

This script is a path planning code with model predictive trajectory generator.

Path optimization sample

Lookup table generation sample

see:

• 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

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 *

• Incremental Sampling-based Algorithms for Optimal Motion Planning

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

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

Closed Loop RRT*

A sample code with closed loop RRT*.

see:

• 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.

Dubins path planning

A sample code for Dubins path planning.

Dubins path - Wikipedia

Reeds Shepp planning

A sample code with Reeds Shepp path planning.

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.

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)