
- 800 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
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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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
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.
Information
Table of contents
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Acknowledgements
- Chapter 1 An introduction to robotics
- Chapter 2 Localization and mapping
- Chapter 3 Visual SLAM, planning, and control
- Chapter 4 Intelligent graph search basics
- Chapter 5 Graph search-based motion planning
- Chapter 6 Configuration space and collision checking
- Chapter 7 Roadmap and cell decomposition-based motion planning
- Chapter 8 Probabilistic roadmap
- Chapter 9 Rapidly-exploring random trees
- Chapter 10 Artificial potential field
- Chapter 11 Geometric and fuzzy logic-based motion planning
- Chapter 12 An introduction to machine learning and deep learning
- Chapter 13 Learning from demonstrations for robotics
- Chapter 14 Motion planning using reinforcement learning
- Chapter 15 An introduction to evolutionary computation
- Chapter 16 Evolutionary robot motion planning
- Chapter 17 Hybrid planning techniques
- Chapter 18 Multi-robot motion planning
- Chapter 19 Task planning approaches
- Chapter 20 Swarm and evolutionary robotics
- Chapter 21 Simulation systems and case studies
- Index