Python sample codes and textbook for robotics algorithms.
Python sample codes and textbook for robotics algorithms.
Python codes and textbook for robotics algorithm.
PythonRobotics is a Python code collection and a textbook of robotics algorithms.
Features:
Easy to read for understanding each algorithm's basic idea.
Widely used and practical algorithms are selected.
Minimum dependency.
See this documentation
or this Youtube video:
or this paper for more details:
For running each sample code:
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)
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
Clone this repo.
git clone https://github.com/AtsushiSakai/PythonRobotics.git
Install the required libraries.
using conda :
conda env create -f requirements/environment.yml
using pip :
pip install -r requirements/requirements.txt
Execute python script in each directory.
Add star to this repo if you like it :smiley:.
Reference
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
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
This is a 2D Gaussian grid mapping example.
This is a 2D ray casting grid mapping example.
This example shows how to convert a 2D range measurement to a grid map.
This is a 2D object clustering with k-means algorithm.
This is a 2D rectangle fitting for vehicle detection.
Simultaneous Localization and Mapping(SLAM) examples
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
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.
Reference
This is a 2D navigation sample code with Dynamic Window Approach.
This is a 2D grid based the shortest path planning with Dijkstra's algorithm.
In the animation, cyan points are searched nodes.
This is a 2D grid based the shortest path planning with A star algorithm.
In the animation, cyan points are searched nodes.
Its heuristic is 2D Euclid distance.
This is a 2D grid based the shortest path planning with D star algorithm.
The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
Reference
This algorithm finds the shortest path between two points while rerouting when obstacles are discovered. It has been implemented here for a 2D grid.
The animation shows a robot finding its path and rerouting to avoid obstacles as they are discovered using the D* Lite search algorithm.
Refs:
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.
Reference
This is a 2D grid based coverage path planning simulation.
This is a 2D path planning simulation using the Particle Swarm Optimization algorithm.
PSO is a metaheuristic optimization algorithm inspired by bird flocking behavior. In path planning, particles explore the search space to find collision-free paths while avoiding obstacles.
The animation shows particles (blue dots) converging towards the optimal path (yellow line) from start (green area) to goal (red star).
References
This script is a path planning code with state lattice planning.
This code uses the model predictive trajectory generator to solve boundary problem.
Reference
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.
Reference
This is a path planning code with RRT*
Black circles are obstacles, green line is a searched tree, red cross