Python Robotics and Computer Vision
A Hands-On Guide to Control, Sensors, and Vision-Language-Action Models for BeginnersBy Vikram Dasgupta
Length11h 11m
About this audiobook
Build robots that see, think, and act — with Python as your only toolkit.
You want a robot that senses the world and acts on it, and you want to build it in Python. This hands-on guide starts with transforms and kinematics, then has you driving motors through tuned PID loops, fusing IMU and range data, and calibrating a camera properly before a single pixel is trusted. Every example runs in simulation first, so you can experiment safely before touching hardware.
From there you segment objects in OpenCV, estimate marker poses, run a neural detector on edge hardware, and close the loop with visual servoing and depth-camera grasping. Mapping, particle-filter localisation and path planning turn the robot loose, and the final chapter shows how vision-language-action policies and imitation learning fit alongside classical control. The progression is deliberate: each chapter builds on the last, moving from rigid-body math to a robot that can navigate a room and pick up an object it has never seen before.
What you will learn:
• Set up a complete Python robotics toolchain for simulation and real hardware
• Apply rigid-body transforms and arm kinematics to describe robot motion
• Drive wheeled mobile robots and tune PID loops with encoder feedback
• Fuse IMU and range data into a reliable state estimate
• Calibrate a camera and build classical vision pipelines in OpenCV
• Run deep vision detectors on edge hardware for real-time perception
• Implement visual servoing and depth-camera grasping
• Map an environment, localise with particle filters, and plan paths
• Understand how vision-language-action models and imitation learning complement classical control
For beginners, makers and engineering students taking their first serious step into robotics and machine vision, this book assumes only basic Python and a willingness to experiment. If you have ever wanted to move from blinking an LED to closing a perception-action loop, this is your starting point.
Audiobook details
GenreTechnology
Length11 hrs 11 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateSep 27, 2026
LanguageEnglish
Table of contents
1Python Robotics and Computer Vision
2Foreword
3Preface
4About This Book
5Chapter 1: Setting Up the Robotics Toolchain
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61.1 Python, Virtual Environments, and Package Management
71.2 NumPy for Robot Mathematics
81.3 OpenCV and the Image Array
91.4 Robot Middleware and Message Passing
101.5 Choosing and Running a Physics Simulator
111.6 Project Layout, Version Control, and Reproducibility
12Chapter 2: Rigid-Body Transforms
132.1 Frames, Vectors, and Coordinate Conventions
142.2 Rotation Matrices in Two and Three Dimensions
152.3 Homogeneous Transforms and Composition
162.4 Euler Angles and Their Pitfalls
172.5 Quaternions and Smooth Rotation
182.6 Transform Trees and Frame Bookkeeping
19Chapter 3: Arm Kinematics
203.1 Joint Types, Links, and the Kinematic Chain
213.2 Denavit-Hartenberg Parameters and Forward Kinematics
223.3 Workspace, Reachability, and Singular Configurations
233.4 The Jacobian and Differential Motion
243.5 Numerical Inverse Kinematics
25Chapter 4: Wheeled Mobile Robots
264.1 Differential-Drive Geometry and the Instantaneous Model
274.2 Wheel Odometry and Pose Integration
284.3 Dead-Reckoning Error and Its Growth
294.4 Ackermann and Omnidirectional Bases
30Chapter 5: Motors, Encoders, and PID Control
315.1 DC Motors, Drivers, and the Command Interface
325.2 Encoders and Velocity Estimation
335.3 The PID Control Law
345.4 Tuning the Loop
355.5 Feedforward, Anti-Windup, and the Serial Link
36Chapter 6: Sensors and State Estimation
376.1 Inertial Measurement Units
386.2 Range Sensors: Ultrasonic and LiDAR
396.3 The Complementary Filter
406.4 The Kalman Filter
416.5 Fusing Odometry with Heading and Range
42Chapter 7: Image Formation and Camera Calibration
437.1 The Pinhole Model and the Camera Matrix
447.2 Lens Distortion and Its Correction
457.3 Checkerboard Calibration
467.4 Extrinsics and the Camera-to-Robot Transform
47Chapter 8: Classical Vision Pipelines in OpenCV
488.1 Color Spaces and Segmentation
498.2 Contours, Moments, and Shape Features
508.3 Edges, Lines, and Feature Points
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