RVC3-python

Robotics, Vision & Control: 3rd edition in Python (2023)

Status & Project Health

Ecosystem & Dependencies

[!NOTE] Actively maintained — August 2026. Every chapter notebook, the CLI tools (rvctool, bdsim_path, rvc3-model), and the packaging have been re-verified against current RTB/MVTB/SMTB/bdsim releases and fixed where they’d drifted. See errata.md for the full list of what changed and why.

Welcome to the online hub for the book: Robotics, Vision & Control: fundamental algorithms in Python (3rd edition) Peter Corke, published by Springer-Nature 2023. ISBN 978-3-031-06468-5 (hardcopy), 978-3-031-06469-2 (eBook) DOI 10.1007/978-3-031-06469-2 Report an issue with the book or its supporting code here. Known errata for the book can be viewed here.

This book uses many examples based on the following open-source Python packages

Robotics Toolbox for Python, Machine Vision Toolbox for Python, Spatial Maths Toolbox for Python, Block Diagram Simulation for Python. These in turn have dependencies on other packages created by the author and third parties.

Installing the package

This package provides a simple one-step installation of all the required Toolboxes

pip install rvc3python

or

conda install rvc3python

There are a lot of dependencies and this might take a minute or so. You now have a very powerful computing environment for robotics and computer vision.

To check everything installed and works correctly, run

rvctool --test

This is a quick, non-interactive check that prints package versions and exercises one real code path per toolbox (RTB, MVTB, spatialgeometry, spatialmath, bdsim, and Open3D if installed), reporting PASS/FAIL for each rather than just “it imported”.

[!NOTE] pip/conda install gives you the importable support code only. Jupyter notebooks, figure-generation scripts, and example data live in this GitHub repo — see Additional book resources below, or just git clone now.

Python version

rvc3python requires Python 3.10 or later.

A handful of book examples need extra, optional packages that aren’t installed by default — each has its own platform or Python-version limits, worth knowing about before you hit them as a surprise rather than as a bug:

• PyTorch (pip install rvc3python[pytorch]) — used for the segmentation and object-detection examples in Chapter 12. Check current platform support before installing.
• Open3D (pip install machinevision-toolbox-python[open3d]) — used for the point cloud examples in Chapter 14. Doesn’t yet ship wheels for Python 3.13+.
• coal (pip install roboticstoolbox-python[collision]) — used for the collision-checking examples in Chapter 7. Not available on Windows.

Installing into a Conda environment

It’s probably a good idea to create a virtual environment to keep this package and its dependencies separated from your other Python code and projects. If you’ve never used virtual environments before this might be a good time to start, and it is really easy using Conda:

conda create -n RVC3 python=3.12
conda activate RVC3
pip install rvc3python

Installing deep learning tools

Chapter 12 has some deep learning examples based on PyTorch. If you don’t have PyTorch installed you can use the pytorch install option

pip install rvc3python[pytorch]

or

conda install rvc3python

Additional book resources

This GitHub repo provides additional resources for readers including:

• Jupyter notebooks containing all code lines from each chapter, see the notebooks folder
• The code to produce every Python/Matplotlib (2D) figure in the book, see the figures folder
• 3D points clouds from chapter 14, and the code to create them, see the figures/pointclouds folder.
• 3D figures from chapters 2-3, 7-9, and the code to create them, see the figures/3d folder.
• All example scripts, see the RVC3/examples folder.
• To run the visual odometry example in Sect. 14.8.3 you need to download two image sequence, each over 100MB, see the instructions here.

To get that material you must clone the repo

git clone https://github.com/petercorke/RVC3-python.git

Using the Toolboxes

The simplest way to get going is to use the command line tool

$ rvctool
 ____       _           _   _             __     ___     _                ___      ____            _             _   _____ 
|  _ \ ___ | |__   ___ | |_(_) ___ ___    \ \   / (_)___(_) ___  _ __    ( _ )    / ___|___  _ __ | |_ _ __ ___ | | |___ / 
| |_) / _ \| '_ \ / _ \| __| |/ __/ __|    \ \ / /| / __| |/ _ \| '_ \   / _ \/\ | |   / _ \| '_ \| __| '__/ _ \| |   |_ \ 
|  _ < (_) | |_) | (_) | |_| | (__\__ \_    \ V / | \__ \ | (_) | | | | | (_>  < | |__| (_) | | | | |_| | | (_) | |  ___) |
|_| \_\___/|_.__/ \___/ \__|_|\___|___( )    \_/  |_|___/_|\___/|_| |_|  \___/\/  \____\___/|_| |_|\__|_|  \___/|_| |____/ 
                                      |/                                                                                   
                                                                                 
for Python (RTB==1.3.1, MVTB==2.3.0, SG==1.3.0, SMTB==1.1.16, NumPy==2.5.2, SciPy==1.18.0, Matplotlib==3.11.1)

    import math
    import numpy as np
    from scipy import linalg, optimize
    import matplotlib.pyplot as plt
    from spatialmath import *
    from spatialmath.base import *
    from spatialmath.base import sym
    from spatialgeometry import *
    from roboticstoolbox import *
    from machinevisiontoolbox import *
    import machinevisiontoolbox.base as mvb
    
    # useful variables
    from math import pi
    puma = models.DH.Puma560()
    panda = models.DH.Panda()

    func/object?       - show brief help
    help(func/object)  - show detailed help
    func/object??      - show source code

Results of assignments will be displayed, use trailing ; to suppress

Default numeric formatting: %.3g

RVC3 >>>

This provides an interactive Python (IPython) session with all the Toolboxes and supporting packages imported, and ready to go. It’s a highly capable, convenient, and “MATLAB-like” workbench environment for robotics and computer vision.

For example to load an ETS model of a Panda robot, solve a forward kinematics and inverse kinematics problem, and an interactive graphical display is simply:

RVC3 >>> panda = models.ETS.Panda()
ERobot: Panda (by Franka Emika), 7 joints (RRRRRRR)
┌─────┬───────┬───────┬────────┬─────────────────────────────────────────────┐
│link │ link  │ joint │ parent │             ETS: parent to link             │
├─────┼───────┼───────┼────────┼─────────────────────────────────────────────┤
│   0 │ link0 │     0 │ BASE   │ tz(0.333) ⊕ Rz(q0)                          │
│   1 │ link1 │     1 │ link0  │ Rx(-90°) ⊕ Rz(q1)                           │
│   2 │ link2 │     2 │ link1  │ Rx(90°) ⊕ tz(0.316) ⊕ Rz(q2)                │
│   3 │ link3 │     3 │ link2  │ tx(0.0825) ⊕ Rx(90°) ⊕ Rz(q3)               │
│   4 │ link4 │     4 │ link3  │ tx(-0.0825) ⊕ Rx(-90°) ⊕ tz(0.384) ⊕ Rz(q4) │
│   5 │ link5 │     5 │ link4  │ Rx(90°) ⊕ Rz(q5)                            │
│   6 │ link6 │     6 │ link5  │ tx(0.088) ⊕ Rx(90°) ⊕ tz(0.107) ⊕ Rz(q6)    │
│   7 │ @ee   │       │ link6  │ tz(0.103) ⊕ Rz(-45°)                        │
└─────┴───────┴───────┴────────┴─────────────────────────────────────────────┘

┌─────┬─────┬────────┬─────┬───────┬─────┬───────┬──────┐
│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°  │
└─────┴─────┴────────┴─────┴───────┴─────┴───────┴──────┘

RVC3 >>> panda.fkine(panda.qz)
   0.7071    0.7071    0         0.088     
   0.7071   -0.7071    0         0         
   0         0        -1         0.823     
   0         0         0         1      
RVC3 >>> panda.ikine_LM(SE3.Trans(0.4, 0.5, 0.2) * SE3.Ry(pi/2))
IKSolution(q=array([  -1.849,   -2.576,   -2.914,     1.22,   -1.587,    2.056,   -1.013]), success=True, iterations=13, searches=1, residual=3.3549072615799585e-10, reason='Success')
RVC3 >>> panda.teach(panda.qz)

Computer vision is just as easy. For example, we can import an image, blur it and display it alongside the original

RVC3 >>> mona = Image.Read("monalisa.png")
RVC3 >>> Image.Hstack([mona, mona.smooth(sigma=5)]).disp()

or load two images of the same scene, compute SIFT features and display putative matches

RVC3 >>> sf1 = Image.Read("eiffel-1.png", mono=True).SIFT()
RVC3 >>> sf2 = Image.Read("eiffel-2.png", mono=True).SIFT()
RVC3 >>> matches = sf1.match(sf2)
RVC3 >>> matches.subset(100).plot("w")

rvctool is a wrapper around IPython where:

• robotics and vision functions and classes can be accessed without needing package prefixes
• results are displayed by default like MATLAB does, and like MATLAB you need to put a semicolon on the end of the line to prevent this
• the prompt is RVC3 >>> by default, distinct from a plain Python or IPython prompt so a transcript is recognisable at a glance; override it with --prompt, or pass --book to match the book’s printed transcripts exactly (plain >>> , no Out[N]: labels, no ANSI matrix colouring)
• allows cutting and pasting in lines from the book, and prompt characters are ignored

The Robotics, Vision & Control book uses rvctool for all the included examples.

rvctool imports the all the above mentioned packages using import * which is not considered best Python practice. It is very convenient for interactive experimentation, but in your own code you can handle the imports as you see fit.

Cutting and pasting

IPython is very forgiving when it comes to cutting and pasting in blocks of Python code. It will strip off the >>> prompt character and ignore indentation. The normal python REPL is not so forgiving. IPython also maintains a command history and allows command editing.

Simple scripting

You can write very simple scripts, for example test.py is

T = puma.fkine(puma.qn)
sol = puma.ikine_LM(T)
sol.q
puma.plot(sol.q);

then

$ rvctool test.py
   0         0         1         0.5963    
   0         1         0        -0.1501    
  -1         0         0         0.6575    
   0         0         0         1         

IKSolution(q=array([7.235e-08,  -0.8335,  0.09396,    3.142,   0.8312,   -3.142]), success=True, iterations=15, searches=1, residual=1.406125546650288e-07, reason='Success')
array([7.235e-08,  -0.8335,  0.09396,    3.142,   0.8312,   -3.142])
PyPlot3D backend, t = 0.05, scene:
  robot: Text(0.0, 0.0, 'Puma 560')
RVC3 >>>

and you are dropped into an IPython session after the script has run.

Using Jupyter and Colab

Graphics and animations are problematic in these environments, some things work well, some don’t. As much as possible I’ve tweaked the Jupyter notebooks to work as best they can in these environments.

For local use the Jupyter plugin for Visual Studio Code is pretty decent. Colab suffers from old versions of major packages (though they are getting better at keeping up to date) and animations can suffer from slow update over the network.

Other command line tools

Additional command line tools available (from the Robotics Toolbox) include:

• eigdemo, animation showing linear transformation of a rotating unit vector which demonstrates eigenvalues and eigenvectors.
• tripleangledemo, Swift visualization that lets you experiment with various triple-angle sequences.
• twistdemo, Swift visualization that lets you experiment with 3D twists. The screw axis is the blue rod and you can position and orient it using the sliders, and adjust its pitch. Then apply a rotation about the screw using the bottom slider.

Block diagram models

Block diagram models are key to the pedagogy of the RVC3 book and 25 models are included. To simulate these models we use the Python package bdsim which can run models:

• written in Python using bdsim blocks and wiring.
• created graphically using bdedit, bdsim’s graphical editor, and saved as a .bd (JSON format) file.

The models are included in the RVC3 package when it is installed. There are three ways to run one:

• From inside rvctool (or any Jupyter notebook), %run -i shares the current namespace, which some models rely on:

  RVC3 >>> %run -i vloop_test
  

• From a bare shell, the rvc3-model command runs a model directly — no rvctool/Jupyter session needed:

  $ rvc3-model vloop_test
  

  Run rvc3-model with no arguments to list every model by name.
• To find where the models are installed on disk (e.g. to open a .py or .bd file directly in an editor), use bdsim_path:

  $ bdsim_path