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.

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

For running each sample code:

  • Python 3.9.x

  • numpy

  • scipy

  • matplotlib

  • pandas

  • 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

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

  1. Install the required libraries.

using conda :

conda env create -f environment.yml

using pip :

pip install -r requirements.txt

  1. Execute python script in each directory.

  2. Add star to this repo if you like it 馃槂 .

Localization

Extended Kalman Filter localization

EKF pic

Documentation: Notebook

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 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.

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

Lidar to grid map

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

2

k-means object clustering

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

2

Rectangle fitting

This is a 2D rectangle fitting for vehicle detection.

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 and 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:

Path Planning

Dynamic Window Approach

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

2

Grid based search

Dijkstra algorithm

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

PythonRobotics/figure_1.png at master 路 AtsushiSakai/PythonRobotics

In the animation, cyan points are searched nodes.

A* algorithm

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

PythonRobotics/figure_1.png at master 路 AtsushiSakai/PythonRobotics

In the animation, cyan points are searched nodes.

Its heuristic is 2D Euclid distance.

D* algorithm

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

figure at master 路 nirnayroy/intelligentrobotics

The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.

Ref:

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:

Grid based coverage path planning

This is a 2D grid based coverage path planning simulation.

PotentialField

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 路 AtsushiSakai/PythonRobotics

Lane sampling

PythonRobotics/figure_1.png at master 路 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:

銆銆

Rapidly-Exploring Random Trees (RRT)

RRT*

PythonRobotics/figure_1.png at master 路 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-shepp path

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

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

LQR-RRT*

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

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

LQR_RRT

Ref:

Quintic polynomials planning

Motion planning with quintic polynomials.

2

It can calculate a 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 the 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 路 AtsushiSakai/PythonRobotics

Ref:

Linear鈥搎uadratic 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.

MPC pic

Ref:

Nonlinear Model predictive control with C-GMRES

A motion planning and path tracking simulation with NMPC of C-GMRES

3

Ref:

Arm Navigation

N joint arm to point control

N joint arm to a point control simulation.

This is an interactive simulation.

You can set the goal position of the end effector with left-click on the plotting 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

rocket powered landing

This is a 3d trajectory generation simulation for a rocket powered landing.

3

Ref:

Bipedal

bipedal planner with inverted pendulum

This is a bipedal planner for modifying footsteps for an inverted pendulum.

You can set the footsteps, and the planner will modify those automatically.

3

License

MIT

Use-case

If this project helps your robotics project, please let me know with creating an issue.

Your robot's video, which is using PythonRobotics, is very welcome!!

This is a list of other user's comment and references:users_comments

Contribution

Any contribution is welcome!!

Citing

If you use this project's code for your academic work, we encourage you to cite our papers

If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly.

Support

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

If you would like to support us in some other way, please contact with creating an issue.

Sponsors

JetBrains

They are providing a free license of their IDEs for this OSS development.

Authors

Owner
Atsushi Sakai
An autonomous navigation system engineer #C++ #ROS #MATLAB #Python #Vim #Robotics #AutonomousDriving #Optimization #ModelPredictiveControl #julialang
Atsushi Sakai
Comments
  • Dynamic Movement Primitives Implementation

    Dynamic Movement Primitives Implementation

    What does this implement/fix?

    Implements Dynamic Movement Primitives, which is a way of 'learning' a path and being able to stretch or squeeze it in space or time as the user sees fit. The path is learned as a weighted sums of gaussian distributions.

    Additional information

    I made a file and a few unit tests, but let me know if there is anything else you'd like me to add to this!

  • Summary paper

    Summary paper

  • Add ICP support for 3d point clouds

    Add ICP support for 3d point clouds

    Reference issue

    fix #464

    What does this implement/fix?

    Adds an implementation of code for ICP to work both for 2d and 3d point clouds.

    Additional information

    Make functions icp_matching() and update_homogeneous_matrix() flexible. Depending on the current_points.shape[0] function svd_motion_estimation() automatically returns the correct size for Rt, Tt. Then, in update_homogeneous_matrix() depending on their size matrix H is formed to be 3x3 or 4x4.

    Test for 3d point cloud is provided. Made the same manner as tests for 2d point cloud.

  • Add Graph SLAM documentation and SE(2) example

    Add Graph SLAM documentation and SE(2) example

    This is a work in progress.

    The Jupyter notebook doesn't render correctly on GitHub, so I moved the write-up into a LaTeX file and included both the .tex source and the generated PDF. I'll probably remove it altogether from the Jupyter notebook.

    TODO: Add code and example problem

    Related issue: https://github.com/AtsushiSakai/PythonRobotics/issues/296

  • Paths ignoring collision(s)...

    Paths ignoring collision(s)...

    Hi Atsushi

    Great work! I've just started exploring and playing but am getting results as in figure below. I'd expect to get "Cannot find path". Am I missing something?

    Thanks in advance.

    Cheers RRT_example

  • Code might be wrong: the true trajectory in

    Code might be wrong: the true trajectory in "particle filter" algorithm shouldn't be a circle

    In the current code in Localization/particle_filter/particle_filter.py,
    the resultant true trajectory is a circle, as shown below: Selection_126

    (The blue line under the red line is the true trajectory, which is a circle. The figure is copied from README)

    However, due to the noise (e.g., wheel slips, noise in the motor), the true trajectory cannot be a perfect circle. So the code might be wrong.

    In my view, the "true trajectory" and "dead reckoning trajectory" needs to be swapped inside the function def observation to solve the error.

    Besides, for the random noise, I think

    np.random.randn() * Rsim[0, 0]
    

    should be replaced by:

    np.random.randn() * Rsim[0, 0]**0.5
    

    (Because Rsim is the variance)

  • MPC have some problem

    MPC have some problem

    when i plan a path along x positive direction means zero degree.if current car's degree is 359 or 0 degree.that will cause problem .yaw error may be -359 or -1

  • FrenetOptimalTrajectory: Following and  Low Speed Trajectories

    FrenetOptimalTrajectory: Following and Low Speed Trajectories

    I've implemented Following and Low Speed Trajectories from Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame. In order to test the algorithms, I've set up a simulation with two vehicles on a race track.

  • KeyError in Mix Integer Path Planning

    KeyError in Mix Integer Path Planning

    Hi @AtsushiSakai , I'm interested in modifying your Mix Integer Path Planning to a multi-vehicle scenario, however I'm having troubles running your code. This is the console output when I run the code:

    mix_integer_opt_path_planning.py start!! ('time:', 0) Academic license - for non-commercial use only Traceback (most recent call last): File "mix_integer_opt_path_planning.py", line 127, in main() File "mix_integer_opt_path_planning.py", line 101, in main s_p, u_p = control(s, gs, ob) File "mix_integer_opt_path_planning.py", line 69, in control prob.solve(solver=cvxpy.GUROBI) File "/usr/local/lib/python2.7/dist-packages/cvxpy-0.4.11-py2.7.egg/cvxpy/problems/problem.py", line 209, in solve return self._solve(*args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/cvxpy-0.4.11-py2.7.egg/cvxpy/problems/problem.py", line 331, in _solve kwargs) File "/usr/local/lib/python2.7/dist-packages/cvxpy-0.4.11-py2.7.egg/cvxpy/problems/solvers/gurobi_intf.py", line 229, in solve A, b) File "/usr/local/lib/python2.7/dist-packages/cvxpy-0.4.11-py2.7.egg/cvxpy/problems/solvers/gurobi_intf.py", line 326, in add_model_lin_constr expr_list[i].append((c, v)) KeyError: 206

    It has something to do with the use of the GUROBI solver, however I verified it is correctly installed already (I ran one of the examples they provide). Do you have an idea of what could be wrong? Thanks!

  • New A star algorithm pr

    New A star algorithm pr

    1. rewrite A star algorithm, improved efficiency by searching the path from two side simultaneously (i.e. from start to goal + from goal to start)

    2. generate obstacles map randomly, the number of obstacle is adjustable

    Find a path: FindPath FindPath2

    No path to the goal, indicate the boundary confine robot or goal: NoPath NoPath2

  • Forward Kinematics and Inverse Kinematics with 3D

    Forward Kinematics and Inverse Kinematics with 3D

    I implemented FK(Forward Kinematics) and IK(Inverse Kinematics) with 3D arm manipulator.

    You can initialize manipulator with Denavit-Hartenberg parameters.

    There are exapmles of FK and IK, random_*.py, initializing with Denavit-Hartenberg parameters of PR2 robot.

    Please advise me if you want this PR be merged.

  • Utilize numpy to reduce calculation time

    Utilize numpy to reduce calculation time

    Change-Id: I6e421a1c2524a3d8f8875121a1a6d2ed832c3150

    Reference issue

    What does this implement/fix?

    I've noticed that the current Implementation uses lists. By replacing them with numpy arrays and using built in numpy functions such as any, I reduced the calculation time of d_star_lite. The other thing that I've noticed, once after initializing class, if the user wants to add new spoofed objects and calculate a new path, it was not using the prior information that is generated. So I modified the initialization process a bit to use the prior information.

    Additional information

    CheckList

    • [ ] Did you add an unittest for your new example or defect fix?
    • [ ] Did you add documents for your new example?
    • [ ] All CIs are green? (You can check it after submitting)
  • Bump flake8 from 5.0.4 to 6.0.0 in /requirements

    Bump flake8 from 5.0.4 to 6.0.0 in /requirements

    Bumps flake8 from 5.0.4 to 6.0.0.

    Commits

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  • support python3.11

    support python3.11

    Reference issue

    fix #740

    What does this implement/fix?

    Additional information

    CheckList

    • [ ] Did you add an unittest for your new example or defect fix?
    • [ ] Did you add documents for your new example?
    • [ ] All CIs are green? (You can check it after submitting)
  • Add setup.py for releasing package to expose common utilities in PythonRobotics.

    Add setup.py for releasing package to expose common utilities in PythonRobotics.

    I think the utilities in this package are helpful, this should be used as a package and should be installed via pip. https://github.com/AtsushiSakai/PythonRobotics/tree/master/utils

    This is the time to release as an official PyPI package. And when this package is mature, we can start to add each algorithm to the package step by step.

  • Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1

    Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1

    Not a bug! Simply a note that I will work on suppressing the warnings. Thanks.

    Describe the deprecated feature warnings.warn(msg, UserWarning) /usr/local/lib/python3.10/dist-packages/cvxpy/expressions/expression.py:593: UserWarning: This use of * has resulted in matrix multiplication. Using * for matrix multiplication has been deprecated since CVXPY 1.1. Use * for matrix-scalar and vector-scalar multiplication. Use @ for matrix-matrix and matrix-vector multiplication. Use multiply for elementwise multiplication. This code path has been hit 2590 times so far.

    warnings.warn(msg, UserWarning)

    Expected behavior No warnings.

    Screenshots Working on making changes to the code to suppress the warnings; I will submit a PR if successful in due course. Thanks.

    Desktop (please complete the following information):

    • Python version 3.10
    • Each library version
    • OS version Ubunut 22.04
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