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PythonRobotics

Python sample codes for robotics algorithms.

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PythonRobotics

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Python codes for robotics algorithm.

Table of Contents

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:

Requirements

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/

How to use

  1. Clone this repo.

git clone https://github.com/dhiegomaga/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

This is a sensor fusion localization with Extended Kalman Filter(EKF).

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

the green point is positioning observation (ex. GPS), and the red line is estimated trajectory with EKF.

The red ellipse is estimated covariance ellipse with EKF.

Ref:

Particle filter localization

2

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:

Histogram filter localization

3

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:

Mapping

Gaussian grid map

This is a 2D Gaussian grid mapping example.

2

Ray casting grid map

This is a 2D ray casting grid mapping example.

2

k-means object clustering

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

2

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.

3

Ref:

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.

3

Ref:

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.

3

Ref:

Path Planning

Dynamic Window Approach

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

2

Dijkstra algorithm

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

PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics

In the animation, cyan points are searched nodes.

A* algorithm

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

PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics

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.

PotentialField

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

Ref:

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:

Biased polar sampling

PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics

Lane sampling

PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics

Probabilistic Road-Map (PRM) planning

PRM

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:

  

Rapidly-Exploring Random Trees (RRT)

RRT*

PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics

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:

RRT* with reeds-sheep path

Robotics/animation.gif at master · AtsushiSakai/PythonRobotics)

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.

LQRRRT

Ref:

Quintic polynomials planning

Motion planning with quintic polynomials.

2

It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials.

Ref:

Reeds Shepp planning

A sample code with Reeds Shepp path planning.

RSPlanning

Ref:

LQR based path planning

A sample code using LQR based path planning for double integrator model.

RSPlanning

Optimal Trajectory in a Frenet Frame

3

This is optimal trajectory generation in a Frenet Frame.

The cyan line is the target course and black crosses are obstacles.

The red line is predicted path.

Ref:

Path tracking

move to a pose control

This is a simulation of moving to a pose control

2

Ref:

Stanley control

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

2

Ref:

Rear wheel feedback control

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

PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics

Ref:

Linear–quadratic regulator (LQR) speed and steering control

Path tracking simulation with LQR speed and steering control.

3

Ref:

Model predictive speed and steering control

Path tracking simulation with iterative linear model predictive speed and steering control.

Ref:

Arm Navigation

N joint arm to point control

N joint arm to a point control simulation.

This is a interactive simulation.

You can set the goal position of the end effector with left-click on the ploting area.

3

In this simulation N = 10, however, you can change it.

Arm navigation with obstacle avoidance

Arm navigation with obstacle avoidance simulation.

3

Aerial Navigation

drone 3d trajectory following

This is a 3d trajectory following simulation for a quadrotor.

3

License

MIT

Contribution

A small PR like bug fix is welcome.

If your PR is merged multiple times, I will add your account to the author list.

Support

If you or your company would like to support this project, please consider:

You can add your name or your company logo in README if you are a patron.

E-mail consultant is also available.

 

Your comment using Say Thanks! is also welcome.

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Authors