# Robotics Toolbox for Python

Robotics without the cruft
A high-productivity framework for robotics research and education.

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GitHub • Wiki • Changelog • Installation

--- ### Status & Project Health [![Build Status](https://github.com/petercorke/robotics-toolbox-python/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/petercorke/robotics-toolbox-python/actions/workflows/ci.yml) [![Downloads](https://static.pepy.tech/badge/roboticstoolbox-python/month)](https://pepy.tech/projects/roboticstoolbox-python) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/roboticstoolbox-python.svg) [![codecov](https://codecov.io/gh/petercorke/robotics-toolbox-python/graph/badge.svg?token=0rqN39PDEO)](https://codecov.io/gh/petercorke/robotics-toolbox-python) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/e662475c8c774eac954f31112ee7711b)](https://app.codacy.com/gh/petercorke/robotics-toolbox-python/dashboard) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ### Ecosystem & Dependencies [![A Python Robotics Package](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/py_collection.min.svg)](https://github.com/petercorke/robotics-toolbox-python) [![QUT Centre for Robotics Open Source](https://github.com/qcr/qcr.github.io/raw/master/misc/badge.svg)](https://qcr.github.io) [![powered by NumPy](https://img.shields.io/badge/powered_by-NumPy-013243?logo=numpy&logoColor=white)](https://numpy.org) [![powered by SciPy](https://img.shields.io/badge/powered_by-SciPy-0054a6?logo=scipy&logoColor=white)](https://scipy.org) [![powered by Matplotlib](https://img.shields.io/badge/powered_by-Matplotlib-11557c?logo=matplotlib&logoColor=white)](https://matplotlib.org) [![Powered by Spatial Maths](https://raw.githubusercontent.com/petercorke/spatialmath-python/master/.github/svg/sm_powered.min.svg)](https://github.com/petercorke/spatialmath-python) ## Contents - [Synopsis](#synopsis) - [Getting going](#getting-going) - [Tutorials](#tutorials) - [Code Examples](#code-examples) - [Toolbox Research Applications](#toolbox-research-applications) - [References](#references) - [Using the Toolbox in your Open Source Code?](#using-the-toolbox-in-your-open-source-code) - [Common Issues and Solutions](#common-issues-and-solutions) - [Build a JupyterLite/Pyodide Wasm wheel](#build-a-jupyterlitepyodide-wasm-wheel)
## 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 50 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](https://github.com/petercorke/spatialmath-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 ```shell script pip install roboticstoolbox-python ``` Available options are: - `swift` install [Swift](https://github.com/jhavl/swift), a web-based visualizer - `qp` install quadratic-programming IK dependencies (`qpsolvers`, `quadprog`) - `collision` install collision checking with [coal](https://github.com/coal-library/coal) and `trimesh` - `tool` install `IPython` and `pygments`, needed to run the `rtbtool` interactive shell - `all` install `swift`, `qp`, `collision`, and `tool` > **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 ```shell script pip install roboticstoolbox-python[optionlist] ``` If you want the Swift visualizer, install the `swift` extra. Install matrix: - Core only ```shell script pip install roboticstoolbox-python ``` - Swift visualizer only ```shell script pip install roboticstoolbox-python[swift] ``` - QP solver dependencies only ```shell script pip install roboticstoolbox-python[qp] ``` - Collision checking dependencies only ```shell script pip install roboticstoolbox-python[collision] ``` - `rtbtool` interactive shell dependencies only ```shell script pip install roboticstoolbox-python[tool] ``` - Everything (swift + qp + collision + tool) ```shell script pip install roboticstoolbox-python[all] ``` - Multiple extras explicitly ```shell script pip install roboticstoolbox-python[swift,qp,collision] ``` ### From GitHub To install the bleeding-edge version from GitHub ```shell 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](#build-a-jupyterlitepyodide-wasm-wheel). ## 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 ```python 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 │ panda_link1 │ 0 │ panda_link0 │ SE3(0, 0, 0.333) ⊕ Rz(q0) │ │ 2 │ panda_link2 │ 1 │ panda_link1 │ SE3(-90°, -0°, 0°) ⊕ Rz(q1) │ │ 3 │ panda_link3 │ 2 │ panda_link2 │ SE3(0, -0.316, 0; 90°, -0°, 0°) ⊕ Rz(q2) │ │ 4 │ panda_link4 │ 3 │ panda_link3 │ SE3(0.0825, 0, 0; 90°, -0°, 0°) ⊕ Rz(q3) │ │ 5 │ panda_link5 │ 4 │ panda_link4 │ SE3(-0.0825, 0.384, 0; -90°, -0°, 0°) ⊕ Rz(q4) │ │ 6 │ panda_link6 │ 5 │ panda_link5 │ SE3(90°, -0°, 0°) ⊕ Rz(q5) │ │ 7 │ panda_link7 │ 6 │ panda_link6 │ SE3(0.088, 0, 0; 90°, -0°, 0°) ⊕ Rz(q6) │ │ 8 │ @panda_link8 │ │ panda_link7 │ 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)). ```python 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 ```python 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) ```python robot.plot(qt.q) ```

We can also experiment with velocity controllers in Swift. Here is a resolved-rate motion control example ```python 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() handle = env.add(panda) handle.q = panda.qr Tep = panda.fkine(handle.q) * sm.SE3.Trans(0.2, 0.2, 0.45) arrived = False dt = 0.05 while not arrived: v, arrived = rtb.p_servo(panda.fkine(handle.q), Tep, 1) handle.qd = np.linalg.pinv(panda.jacobe(handle.q)) @ v env.step(dt) # Uncomment to stop the browser tab from closing # env.hold() ``` `env.add(panda)` returns a handle owning this instance's live joint state -- drive the simulation via `handle.q`/`handle.qd`, not `panda.q`/`panda.qd` directly. `panda` itself stays a plain, shareable kinematic model, so the same `panda` object can back several independent handles/instances at once.

### Loading models from robot_descriptions The Panda example above uses one of the ~50 models shipped with the Toolbox, but many more robots are available on demand via the [robot_descriptions](https://github.com/robot-descriptions/robot_descriptions.py) package, which is installed automatically as a dependency. Passing a bare name (no file suffix) to `URDFRobot` fetches and loads it from there: ```python from roboticstoolbox.models.URDF.URDFRobot import URDFRobot ur5 = URDFRobot("ur5") ``` `rtb.models.catalog()` lists everything available — the models built into the Toolbox as well as those it can load from `robot_descriptions` — with filtering by keyword/DoF/model type and column sorting: ```python rtb.models.catalog(mtype="URDF", sorton="name") ``` ### Run some examples The [`notebooks`](https://github.com/petercorke/robotics-toolbox-python/tree/main/notebooks) folder contains some tutorial Jupyter notebooks which you can browse on GitHub. Additionally, have a look in the [`examples`](https://github.com/petercorke/robotics-toolbox-python/tree/main/roboticstoolbox/examples) folder for many ready to run examples.
## References ### Key papers - P. Corke, "A computer tool for simulation and analysis: the Robotics Toolbox for MATLAB," Proc. National Conf. Australian Robot Association, pp. 319–330, Melbourne, July 1995. [[PDF]](http://www.petercorke.com/RTB/ARA95.pdf) - P. Corke, "A robotics toolbox for MATLAB," IEEE Robotics and Automation Magazine, 3(1):24–32, Sept. 1996. [[IEEE Xplore]](https://ieeexplore.ieee.org/document/486658) - P. Corke, "A simple and systematic approach to assigning Denavit-Hartenberg parameters," IEEE Transactions on Robotics, 23(3):590–594, 2007. [[IEEE Xplore]](https://ieeexplore.ieee.org/document/4252158) — introduces the Elementary Transform Sequence (ETS) notation used throughout the Toolbox. - J. Haviland and P. Corke, "A systematic approach to computing the manipulator Jacobian and Hessian using the elementary transform sequence," arXiv preprint, 2020. [[arXiv]](https://arxiv.org/abs/2010.08696) - P. Corke and J. Haviland, "Not your grandmother's toolbox – the Robotics Toolbox reinvented for Python," Proc. ICRA 2021. [[IEEE Xplore]](https://ieeexplore.ieee.org/document/9561366) [[PDF]](https://bit.ly/3ChcyNp) ### Talks - [Peter Corke – The Robotics Toolbox: 30 Years Old and Still Going Strong](https://www.youtube.com/watch?v=U37NMe7anXc&list=PL1pxneANaikCP5jwrw91UBkrCklkQXlyr&index=1) — a retrospective on the Toolbox's history and motivation, a good starting point before diving into the API. ### Related book The Toolbox is a companion to Peter Corke's textbook [*Robotics, Vision & Control*](https://petercorke.com/books/robotics-vision-control-all-versions/) (Springer) — many docstrings and examples reference specific figures/sections from the book. ### Citing the Toolbox 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 [![Powered by the Robotics Toolbox](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/rtb_powered.min.svg)](https://github.com/petercorke/robotics-toolbox-python) copy the following ``` [![Powered by the Robotics Toolbox](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/rtb_powered.min.svg)](https://github.com/petercorke/robotics-toolbox-python) ``` For the powered by python robotics badge [![Powered by Python Robotics](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/pr_powered.min.svg)](https://github.com/petercorke/robotics-toolbox-python) copy the following ``` [![Powered by Python Robotics](https://raw.githubusercontent.com/petercorke/robotics-toolbox-python/main/.github/svg/pr_powered.min.svg)](https://github.com/petercorke/robotics-toolbox-python) ```
## Common Issues and Solutions See the common issues with fixes [here](https://github.com/petercorke/robotics-toolbox-python/wiki/FAQ). ### Using the Toolbox with Windows? Graphical visualisation via Swift is currently not supported under Windows. However there is a hotfix, by changing in ```SwiftRoute.py``` ```self.path[9:]``` to ```self.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. It's been the implementation substrate for several published reactive-control papers, and its kinematics are a direct implementation of a two-part differential-kinematics tutorial — see the wiki's [Reactive control](https://github.com/petercorke/robotics-toolbox-python/wiki/Reactive-control) and [Inverse kinematics](https://github.com/petercorke/robotics-toolbox-python/wiki/Inverse-kinematics) pages.