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AI for Autonomous Machines. Build an Autonomous Robot That Sees and Navigates
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- FormatePub
- ISBN8259604650
- EAN9798259604650
- Date de parution29/06/2026
- Protection num.pas de protection
- Taille993 Ko
- Infos supplémentairesepub
- ÉditeurChiify
Résumé
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.
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.