Topics

#dynamics #hessian #jacobian #kinematics #motion-planning #python #robot-manipulator #robotics #robotics-control #robotics-simulation #robotics-toolbox #trajectory-generation

Language breakdown

C++ 72.9%

Python 26.0%

C 0.9%

CMake 0.1%

MATLAB 0.1%

Makefile 0.0%

▸ Files click to expand

README

Robotics Toolbox for Python

PyPI - Python Version

A Python implementation of the Robotics Toolbox for MATLAB^®

Synopsis

This toolbox brings robotics-specific functionality to Python, and leverages Python's advantages of portability, ubiquity and support, and the capability of the open-source ecosystem for linear algebra (numpy, scipy), graphics (matplotlib, three.js, WebGL), interactive development (Jupyter, JupyterLab, mybinder.org), and documentation (sphinx).

The Toolbox provides tools for representing the kinematics and dynamics of serial-link manipulators - you can easily create your own in Denavit-Hartenberg form, import a URDF file, or use over 30 supplied models for well-known contemporary robots from Franka-Emika, Kinova, Universal Robotics, Rethink as well as classical robots such as the Puma 560 and the Stanford arm.

The Toolbox contains fast implementations of kinematic operations. The forward kinematics and the manipulator Jacobian can be computed in less than 1 microsecond while numerical inverse kinematics can be solved in as little as 4 microseconds.

The toolbox also supports mobile robots with functions for robot motion models (unicycle, bicycle), path planning algorithms (bug, distance transform, D\*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF).

The Toolbox provides:

code that is mature and provides a point of comparison for other

implementations of the same algorithms;

routines which are generally written in a straightforward manner which

allows for easy understanding, perhaps at the expense of computational efficiency;

source code which can be read for learning and teaching;
backward compatability with the Robotics Toolbox for MATLAB

The Toolbox leverages the Spatial Maths Toolbox for Python to provide support for data types such as SO(n) and SE(n) matrices, quaternions, twists and spatial vectors.

Getting going

You will need Python >= 3.10

Using pip

Install a snapshot from PyPI

script
pip install roboticstoolbox-python

Available options are:

swift install Swift, a web-based visualizer
qp install quadratic-programming IK dependencies (qpsolvers, quadprog)
collision install collision checking with coal and trimesh
all install swift, qp, and collision

Windows note: coal does not publish Windows wheels on PyPI, so the

collision/all extras skip it there and collision checking is

unavailable via pip on Windows. It's available via

conda install -c conda-forge coal-python if needed. Everything else in

the Toolbox works normally.

Put the options in a comma separated list like

script
pip install roboticstoolbox-python[optionlist]

If you want the Swift visualizer, install the swift extra.

Install matrix:

Core only

script
pip install roboticstoolbox-python

Swift visualizer only

script
pip install roboticstoolbox-python[swift]

QP solver dependencies only

script
pip install roboticstoolbox-python[qp]

Collision checking dependencies only

script
pip install roboticstoolbox-python[collision]

Everything (swift + qp + collision)

script
pip install roboticstoolbox-python[all]

Multiple extras explicitly

script
pip install roboticstoolbox-python[swift,qp,collision]

From GitHub

To install the bleeding-edge version from GitHub

script
git clone https://github.com/petercorke/robotics-toolbox-python.git
cd robotics-toolbox-python
pip install -e .

To generate a Wasm wheel that will run in the browser see the instructions here.

Tutorials

Do you want to learn about manipulator kinematics, differential kinematics, inverse-kinematics and motion control? Have a look at our tutorial. This tutorial comes with two articles to cover the theory and 12 Jupyter Notebooks providing full code implementations and examples. Most of the Notebooks are also Google Colab compatible allowing them to run online.

Code Examples

We will load a model of the Franka-Emika Panda robot defined by a URDF file

import roboticstoolbox as rtb robot = rtb.models.Panda() print(robot) ERobot: panda (by Franka Emika), 7 joints (RRRRRRR), 1 gripper, geometry, collision ┌─────┬──────────────┬───────┬─────────────┬────────────────────────────────────────────────┐ │link │ link │ joint │ parent │ ETS: parent to link │ ├─────┼──────────────┼───────┼─────────────┼────────────────────────────────────────────────┤ │ 0 │ panda_link0 │ │ BASE │ │ │ 1 │ pandalink1 │ 0 │ pandalink0 │ SE3(0, 0, 0.333) ⊕ Rz(q0) │ │ 2 │ pandalink2 │ 1 │ pandalink1 │ SE3(-90°, -0°, 0°) ⊕ Rz(q1) │ │ 3 │ pandalink3 │ 2 │ pandalink2 │ SE3(0, -0.316, 0; 90°, -0°, 0°) ⊕ Rz(q2) │ │ 4 │ pandalink4 │ 3 │ pandalink3 │ SE3(0.0825, 0, 0; 90°, -0°, 0°) ⊕ Rz(q3) │ │ 5 │ pandalink5 │ 4 │ pandalink4 │ SE3(-0.0825, 0.384, 0; -90°, -0°, 0°) ⊕ Rz(q4) │ │ 6 │ pandalink6 │ 5 │ pandalink5 │ SE3(90°, -0°, 0°) ⊕ Rz(q5) │ │ 7 │ pandalink7 │ 6 │ pandalink6 │ SE3(0.088, 0, 0; 90°, -0°, 0°) ⊕ Rz(q6) │ │ 8 │ @pandalink8 │ │ pandalink7 │ SE3(0, 0, 0.107) │ └─────┴──────────────┴───────┴─────────────┴────────────────────────────────────────────────┘

┌─────┬─────┬────────┬─────┬───────┬─────┬───────┬──────┐ │name │ q0 │ q1 │ q2 │ q3 │ q4 │ q5 │ q6 │ ├─────┼─────┼────────┼─────┼───────┼─────┼───────┼──────┤ │ qr │ 0° │ -17.2° │ 0° │ -126° │ 0° │ 115° │ 45° │ │ qz │ 0° │ 0° │ 0° │ 0° │ 0° │ 0° │ 0° │ └─────┴─────┴────────┴─────┴───────┴─────┴───────┴──────┘

The symbol @ indicates the link as an end-effector, a leaf node in the rigid-body tree (Python prompts are not shown to make it easy to copy+paste the code, console output is indented). We will compute the forward kinematics next

Te = robot.fkine(robot.qr)  # forward kinematics
print(Te)
0.995     0         0.09983   0.484 	0        -1         0         0 	0.09983   0        -0.995     0.4126 	0         0         0         1

We can solve inverse kinematics very easily. We first choose an SE(3) pose defined in terms of position and orientation (end-effector z-axis down (A=-Z) and finger orientation parallel to y-axis (O=+Y)).

from spatialmath import SE3
Tep = SE3.Trans(0.6, -0.3, 0.1) * SE3.OA([0, 1, 0], [0, 0, -1]) sol = robot.ik_LM(Tep)         # solve IK print(sol)
(array([ 0.20592815,  0.86609481, -0.79473206, -1.68254794,  0.74872915, 			2.21764746, -0.10255606]), 1, 114, 7, 2.890164057230228e-07)
q_pickup = sol[0] print(robot.fkine(q_pickup))    # FK shows that desired end-effector pose was achieved
1         -8.913e-05  -0.0003334  0.5996 	-8.929e-05 -1          -0.0004912 -0.2998 	-0.0003334  0.0004912  -1          0.1001 	 0          0           0          1

We can animate a path from the ready pose qr configuration to this pickup configuration

qt = rtb.jtraj(robot.qr, q_pickup, 50)
robot.plot(qt.q, backend='pyplot', movie='panda1.gif')

where we have specified the matplotlib pyplot backend. Blue arrows show the joint axes and the coloured frame shows the end-effector pose.

We can also plot the trajectory in the Swift simulator (a browser-based 3d-simulation environment built to work with the Toolbox)

robot.plot(qt.q)

We can also experiment with velocity controllers in Swift. Here is a resolved-rate motion control example

import swift
import roboticstoolbox as rtb
import spatialmath as sm
import numpy as np
env = swift.Swift() env.launch(realtime=True)
panda = rtb.models.Panda() panda.q = panda.qr
Tep = panda.fkine(panda.q) * sm.SE3.Trans(0.2, 0.2, 0.45)
arrived = False env.add(panda)
dt = 0.05
while not arrived:
v, arrived = rtb.p_servo(panda.fkine(panda.q), Tep, 1)     panda.qd = np.linalg.pinv(panda.jacobe(panda.q)) @ v     env.step(dt)
Uncomment to stop the browser tab from closing
env.hold()

Run some examples

The notebooks folder contains some tutorial Jupyter notebooks which you can browse on GitHub. Additionally, have a look in the examples folder for many ready to run examples.

Toolbox ICRA Paper and Citation Info

Check out our ICRA 2021 paper on IEEE Xplore or get the PDF from Peter's website.

If the toolbox helped you in your research, please cite

@inproceedings{rtb,
  title={Not your grandmother’s toolbox--the Robotics Toolbox reinvented for Python},
  author={Corke, Peter and Haviland, Jesse},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={11357--11363},
  year={2021},
  organization={IEEE}
}

Using the Toolbox in your Open Source Code?

If you are using the Toolbox in your open source code, feel free to add our badge to your readme!

For the powered by robotics toolbox badge

copy the following

For the powered by python robotics badge

copy the following

Common Issues and Solutions

See the common issues with fixes here.

Using the Toolbox with Windows?

Graphical visualisation via swift is currently not supported under Windows. However there is a hotfix, by changing in

.py

.path[9:]

to

.path[10:]

Toolbox Research Applications

The toolbox is incredibly useful for developing and prototyping algorithms for research, thanks to the exhaustive set of well documented and mature robotic functions exposed through clean and painless APIs. Additionally, the ease at which a user can visualize their algorithm supports a rapid prototyping paradigm.

Publication List

J. Haviland, N. Sünderhauf and P. Corke, "A Holistic Approach to Reactive Mobile Manipulation," in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2022.3146554. In the video, the robot is controlled using the Robotics toolbox for Python and features a recording from the Swift Simulator.

[Arxiv Paper] [IEEE Xplore] [Project Website] [Video] [Code Example]

J. Haviland and P. Corke, "NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators," in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2021.3056060. In the video, the robot is controlled using the Robotics toolbox for Python and features a recording from the Swift Simulator.

[Arxiv Paper] [IEEE Xplore] [Project Website] [Video] [Code Example]

K. He, R. Newbury, T. Tran, J. Haviland, B. Burgess-Limerick, D. Kulić, P. Corke, A. Cosgun, "Visibility Maximization Controller for Robotic Manipulation", in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2022.3188430. In the video, the robot is controlled using the Robotics toolbox for Python and features a recording from the Swift Simulator.

[Arxiv Paper] [IEEE Xplore] [Project Website] [Video] [Code Example]

A Purely-Reactive Manipulability-Maximising Motion Controller, J. Haviland and P. Corke. In the video, the robot is controlled using the Robotics toolbox for Python.

[Paper] [Project Website] [Video] [Code Example]

Build a JupyterLite/Pyodide Wasm wheel (cp313)

This project includes a reproducible wasm wheel build target aligned to current JupyterLite runtimes based on Python 3.13.

Build using cibuildwheel's Pyodide platform:

script
make wheel-pyodide

Optionally pin the Pyodide runtime family to match your JupyterLite deployment:

script
PYODIDE_VERSION=0.28.3 make wheel-pyodide

The target writes to dist/ and runs make wheel-pyodide-check, which validates the wheel filename contains:

cp313-cp313
wasm32
pyemscripten<major><minor> or pyodide<major><minor>

To inspect the produced artifact path:

script
ls -1 dist/wasm32.whl

🔗 More in this category

Robotics Toolbox for Python

Contents

Synopsis

Getting going

Using pip

From GitHub

Tutorials

Code Examples

Uncomment to stop the browser tab from closing

`env.hold()`

Run some examples

Toolbox ICRA Paper and Citation Info

Using the Toolbox in your Open Source Code?

Common Issues and Solutions

Using the Toolbox with Windows?

Toolbox Research Applications

Publication List

Build a JupyterLite/Pyodide Wasm wheel (cp313)