Autonomous Mobile Robots
eBook - ePub

Autonomous Mobile Robots

Planning, Navigation and Simulation

  1. 800 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Autonomous Mobile Robots

Planning, Navigation and Simulation

About this book

Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice. Finally, the book presents fusion mechanisms to design hybrid algorithms. - Presents intuitive and practical coverage of all sub-problems of mobile robotics to enable easy comprehension of sophisticated modern-day robots - Covers a wide variety of motion planning algorithms, giving a near-exhaustive treatment of the domain with thought provoking comparisons between algorithms - Dives into detailed discussions on robot operating systems and other simulators to get hands-on knowledge without the need of in-house robots

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Yes, you can access Autonomous Mobile Robots by Rahul Kala in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title of Book
  2. Cover image
  3. Title page
  4. Table of Contents
  5. Copyright
  6. Preface
  7. Acknowledgements
  8. Chapter 1 An introduction to robotics
  9. Chapter 2 Localization and mapping
  10. Chapter 3 Visual SLAM, planning, and control
  11. Chapter 4 Intelligent graph search basics
  12. Chapter 5 Graph search-based motion planning
  13. Chapter 6 Configuration space and collision checking
  14. Chapter 7 Roadmap and cell decomposition-based motion planning
  15. Chapter 8 Probabilistic roadmap
  16. Chapter 9 Rapidly-exploring random trees
  17. Chapter 10 Artificial potential field
  18. Chapter 11 Geometric and fuzzy logic-based motion planning
  19. Chapter 12 An introduction to machine learning and deep learning
  20. Chapter 13 Learning from demonstrations for robotics
  21. Chapter 14 Motion planning using reinforcement learning
  22. Chapter 15 An introduction to evolutionary computation
  23. Chapter 16 Evolutionary robot motion planning
  24. Chapter 17 Hybrid planning techniques
  25. Chapter 18 Multi-robot motion planning
  26. Chapter 19 Task planning approaches
  27. Chapter 20 Swarm and evolutionary robotics
  28. Chapter 21 Simulation systems and case studies
  29. Index