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In this five-lesson tutorial, we start from the very basics of scalar and matricial operations in Python using numpy, all the way until the basics of kinematic control. Until kinematic control, most is based on Spong et al., 2020.

Using this book

Each lesson is a Jupyter notebook. Each lesson can be opened and executed with popular IDEs, such as VSCode and PyCharm. The reader is expected to follow it sequentially.

Contents

NumberTitle and LinkContent
1L1 A quick Python refresherBasic operations in Python and numpy
2L2 Rigid Body MotionLearn about elements and operations in Rn\mathbb{R}^n, SO(n)SO(n), and SE(n)SE(n) with n∈{2,3}n\in{\{2,3\}} related to positions, orientations, and poses, respectively.
3L3 Forward KinematicsLearn about the composition of rigid body motion in series to obtain the forward kinematics model of a robotic manipulator, mapping their configuration space q∈Rn\myvec{q}\in\mathbb{R}^n into their task space x∈Rm\myvec{x}\in\mathbb{R}^m.
4L4 Differential KinematicsLearn about the first-order differential mapping x˙=Jq˙\dot{\myvec{x}}=\mymatrix{J}\dot{\myvec{q}} between joint space and task space velocities through the calculation of the Jacobian J\mymatrix{J}.
5L5 Kinematic ControlEmploy the previous knowledge in all previous lessons to employ a Lyapunov-stable control law to move a manipulator in task space using configuration-space signals.
References
  1. Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2020). Robot Modeling and Control. Wiley.
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[Basic] Robotics using numpy
L1 A quick Python refresher