PythonRobotics

Python codes for robotics algorithm.

Table of Contents

• What is this?
• Requirements
• Documentation
• How to use
• Localization
  • Extended Kalman Filter localization
  • Particle filter localization
  • Histogram filter localization
• Mapping
  • Gaussian grid map
  • Ray casting grid map
  • k-means object clustering
  • Rectangle fitting
• SLAM
  • Iterative Closest Point (ICP) Matching
  • FastSLAM 1.0
  • Graph based SLAM
• Path Planning
  • Dynamic Window Approach
  • Grid based search
    • Dijkstra algorithm
    • A* algorithm
    • Potential Field algorithm
  • State Lattice Planning
    • Biased polar sampling
    • Lane sampling
  • Probabilistic Road-Map (PRM) planning
  • Rapidly-Exploring Random Trees (RRT)
    • RRT*
    • RRT* with reeds-sheep path
    • LQR-RRT*
  • Quintic polynomials planning
  • Reeds Shepp planning
  • LQR based path planning
  • Optimal Trajectory in a Frenet Frame
• Path Tracking
  • move to a pose control
  • Stanley control
  • Rear wheel feedback control
  • Linear–quadratic regulator (LQR) speed and steering control
  • Model predictive speed and steering control
  • Nonlinear Model predictive control with C-GMRES
• Arm Navigation
  • N joint arm to point control
  • Arm navigation with obstacle avoidance
• Aerial Navigation
  • drone 3d trajectory following
  • rocket powered landing
• Bipedal
  • bipedal planner with inverted pendulum
• License
• Use-case
• Contribution
• Support
• Authors

What is this?

This is a Python code collection of robotics algorithms, especially for autonomous navigation.

Features:

1. Easy to read for understanding each algorithm's basic idea.

2. Widely used and practical algorithms are selected.

3. Minimum dependency.

See this paper for more details:

• [1808.10703] PythonRobotics: a Python code collection of robotics algorithms (BibTeX)

Requirements

• Python 3.6.x (2.7 is not supported)

• numpy

• scipy

• matplotlib

• pandas

• cvxpy

Documentation

This README only shows some examples of this project.

If you are interested in other examples or mathematical backgrounds of each algorithm,

You can check the full documentation online: https://pythonrobotics.readthedocs.io/

All animation gifs are stored here: AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics

How to use

1. Clone this repo.

git clone https://github.com/AtsushiSakai/PythonRobotics.git

cd PythonRobotics/

1. Install the required libraries. You can use environment.yml with conda command.

conda env create -f environment.yml

1. Execute python script in each directory.

2. Add star to this repo if you like it :smiley:.

Localization

Extended Kalman Filter localization

Documentation: Notebook

Particle filter localization

This is a sensor fusion localization with Particle Filter(PF).

The blue line is true trajectory, the black line is dead reckoning trajectory,

and the red line is estimated trajectory with PF.

It is assumed that the robot can measure a distance from landmarks (RFID).

This measurements are used for PF localization.

Ref:

• PROBABILISTIC ROBOTICS

Histogram filter localization

This is a 2D localization example with Histogram filter.

The red cross is true position, black points are RFID positions.

The blue grid shows a position probability of histogram filter.

In this simulation, x,y are unknown, yaw is known.

The filter integrates speed input and range observations from RFID for localization.

Initial position is not needed.

Ref:

• PROBABILISTIC ROBOTICS

Mapping

Gaussian grid map

This is a 2D Gaussian grid mapping example.

Ray casting grid map

This is a 2D ray casting grid mapping example.

k-means object clustering

This is a 2D object clustering with k-means algorithm.

Rectangle fitting

This is a 2D rectangle fitting for vehicle detection.

SLAM

Simultaneous Localization and Mapping(SLAM) examples

Iterative Closest Point (ICP) Matching

This is a 2D ICP matching example with singular value decomposition.

It can calculate a rotation matrix and a translation vector between points to points.

Ref:

• Introduction to Mobile Robotics: Iterative Closest Point Algorithm

FastSLAM 1.0

This is a feature based SLAM example using FastSLAM 1.0.

The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM.

The red points are particles of FastSLAM.

Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM.

Ref:

• PROBABILISTIC ROBOTICS

• SLAM simulations by Tim Bailey

Graph based SLAM

This is a graph based SLAM example.

The blue line is ground truth.

The black line is dead reckoning.

The red line is the estimated trajectory with Graph based SLAM.

The black stars are landmarks for graph edge generation.

Ref:

• A Tutorial on Graph-Based SLAM

Path Planning

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.

Its 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

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.

Ref:

• Optimal rough terrain trajectory generation for wheeled mobile robots

• State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments

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

Rapidly-Exploring Random Trees (RRT)

RRT*

This is a path planning code with RRT*

Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.

Ref:

• Incremental Sampling-based Algorithms for Optimal Motion Planning

• Sampling-based Algorithms for Optimal Motion Planning

RRT* with reeds-sheep path

)

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

LQR-RRT*

This is a path planning simulation with LQR-RRT*.

A double integrator motion model is used for LQR local planner.

Ref:

• LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics

• [MahanFathi/LQR-RRTstar: LQR-RRT* method is used for ra