<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/>

PythonRobotics

Python codes and textbook 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
  • Lidar to grid map
  • k-means object clustering
  • Rectangle fitting
• SLAM
  • Iterative Closest Point (ICP) Matching
  • FastSLAM 1.0
• Path Planning
  • Dynamic Window Approach
  • Grid based search
    • Dijkstra algorithm
    • A* algorithm
    • D* algorithm
    • D* Lite algorithm
    • Potential Field algorithm
    • Grid based coverage path planning
    • Particle Swarm Optimization (PSO)
  • State Lattice Planning
    • Biased polar sampling
    • Lane sampling
  • Probabilistic Road-Map (PRM) planning
  • Rapidly-Exploring Random Trees (RRT)
    • RRT*
    • RRT* with reeds-shepp 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
• Citing
• Support
• Sponsors
  • JetBrains
  • 1Password
• Authors

What is PythonRobotics?

PythonRobotics is a Python code collection and a textbook of robotics algorithms.

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 documentation

• Getting Started — PythonRobotics documentation

or this Youtube video:

• PythonRobotics project audio overview

or this paper for more details:

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

Requirements to run the code

For running each sample code:

• Python 3.13.x

• NumPy

• SciPy

• Matplotlib

• cvxpy

For development:

• pytest (for unit tests)

• pytest-xdist (for parallel unit tests)

• mypy (for type check)

• sphinx (for document generation)

• pycodestyle (for code style check)

Documentation (Textbook)

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 (textbook) online: Welcome to PythonRobotics’s documentation! — PythonRobotics documentation

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
   

2. Install the required libraries.

• using conda :

  conda env create -f requirements/environment.yml
  

• using pip :

  pip install -r requirements/requirements.txt
  

3. Execute python script in each directory.

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

Localization

Extended Kalman Filter localization

<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">

Reference

• documentation

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 an estimated trajectory with PF.

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

These measurements are used for PF localization.

Reference

• 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.

Reference

• 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.

Lidar to grid map

This example shows how to convert a 2D range measurement to a grid map.

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 and points.

Reference

• 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.

Refer