AI for Autonomous Machines
Build an Autonomous Robot That Sees and NavigatesBy Greta Pereira
Length7h 40m
About this audiobook
Build intelligent machines with autonomous systems that perceive, plan, and act. AI for Autonomous Machines is your hands-on guide to programming perception and control for robots, from beginner to pro. Master sensors, computer vision, motion planning, and the machine learning that makes machines act.
Start with sensor fusion and camera calibration, then dive into object detection with deep learning. Implement SLAM for mapping, use path planning algorithms like A* and RRT, and control robots with PID and model predictive control. Each chapter pairs theory with real code examples in Python and ROS2, so you can build a working autonomous machine by the end.
Learn to: Program LiDAR, cameras, and IMUs for robust perception
Apply computer vision techniques like feature matching and 3D reconstruction
Design motion planners for dynamic environments
Train reinforcement learning agents for decision-making
Deploy models on edge devices for real-time control
Whether you're a hobbyist or a software engineer, this book bridges the gap between theory and practice. Compare with [placeholder] and [placeholder] for a more practical, code-first approach. AI for Autonomous Machines gives you the tools to create robots that navigate, manipulate, and learn on their own.
This hands-on AI/ML/Data guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
Audiobook details
GenreTechnology
Length7 hrs 40 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1AI for Autonomous Machines
2Preface
3Chapter 1 — Welcome to Rover-1: Your Autonomous Robot Project
4Chapter 2 — Setting Up the Robot’s Brain: Software and Hardware
5Chapter 3 — Making It Move: Motor Control and Teleoperation
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6Chapter 4 — First Sight: Connecting the Camera and Live Video
7Chapter 5 — OpenCV Essentials: Colors, Thresholds, and Contours
8Chapter 6 — Tracking the World: Color Detection and Ball Following
9Chapter 7 — Seeing Shapes and Edges: Advanced Contour Analysis
10Chapter 8 — Depth from a Single Camera: Motion and Perspective
11Chapter 9 — Adding a Depth Camera: Stereo and 3D Vision
12Chapter 10 — Custom Object Detection: Training a Neural Network
13Chapter 11 — Deploying AI on the Robot: Real-Time Object Detection
14Chapter 12 — Fusing Sensors: Odometry, IMU, and Ultrasonics
15Chapter 13 — Mapping the Environment: Occupancy Grids and SLAM
16Chapter 14 — Path Planning: A* and Dijkstra for Autonomous Navigation
17Chapter 15 — Smooth Moves: PID Control for Precise Motion
18Chapter 16 — Decision Making: Behavior Trees and State Machines
19Chapter 17 — Learning to Navigate: Reinforcement Learning in Simulation
20Chapter 18 — Bringing It All Together: The Autonomous Explorer
21About the Author
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