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Kala

Autonomous Mobile Robots

Planning, Navigation and Simulation

Medium: Buch
ISBN: 978-0-443-18908-1
Verlag: Elsevier Science Publishing Co Inc
Erscheinungstermin: 07.09.2023
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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.

Produkteigenschaften

  • Artikelnummer: 9780443189081
  • Medium: Buch
  • ISBN: 978-0-443-18908-1
  • Verlag: Elsevier Science Publishing Co Inc
  • Erscheinungstermin: 07.09.2023
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2023
  • Produktform: Kartoniert
  • Gewicht: 1672 g
  • Seiten: 1088
  • Format (B x H x T): 228 x 154 x 55 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

Rahul Kala is an assistant professor at the Centre of Intelligent Robotics, Indian Institute of Information Technology, Allahabad, India, where he received his B.Tech. and M.Tech. degrees in information technology. He received his Ph.D. degree in cybernetics from the University of Reading, UK in 2013. Dr. Kala has authored four books, 100 scientific papers, and is an active reviewer of leading journals of the domain. He has received numerous scholarships and grants from the Government of India, and is a recipient of the Best PhD Dissertation award from the IEEE Intelligent Transportation Systems Society.

1. An Introduction to Robotics

2. Localization, Mapping, and Control

3. An Introduction to Motion Planning with Bug Algorithms

4. Intelligent Graph Search Basics

5. Graph Search based Motion Planning

6. Configuration Space and Collision Checking

7. Roadmap and Cell Decomposition based Motion Planning

8. Probabilistic Roadmap

9. Rapidly-exploring Random Trees

10. Artificial Potential Field

11. Geometric and Fuzzy-Logic based Motion Planning

12. An Introduction to Machine Learning and Neural Networks

13. Learning-based Robot Motion Planning

14. An Introduction to Evolutionary Computation

15. Evolutionary Robot Motion Planning

16. Hybrid Planning Techniques

17. Multi-Robot Motion Planning

18. Task Planning Approaches

19. Motion Planning in Uncertainties and Reinforcement Learning
20. Swarm and Evolutionary Robotics
21. Simulation Systems and Case Studies