The book has been extensively revised and uses Python, rather than MATLAB, for all examples.  It builds on new Python toolbox for robotics and machine vision. All code examples can be run with zero code install using Google CoLab.

This edition includes new topics such as graph-based path planning, Dubins and Reeds-Shepp paths, branched robots, URDF models, collision checking, task-space control, deep learning for object detection and semantic segmentation, fiducial markers, and point clouds.

The book, written with colleagues Witek Jachimczyk and Remo Pillat from the MathWorks,  has been extensively revised and uses proprietary and supported MATLAB Toolboxes for all examples. All code examples can be run with zero code install using MATLAB Online.

This edition includes new topics such as graph-based path planning, Dubins and Reeds-Shepp paths, branched robots, URDF models, collision checking, task-space control, deep learning for object detection and semantic segmentation, fiducial markers, and point clouds.

The QUT Robot Academy provides free-to-use undergraduate-level learning resources for robotics and robotic vision. Although targeted at undergraduate-level around 20% of the lessons require no more than general knowledge, and the required knowledge (on a 5-point scale) is indicated for each lesson.