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Multi-robot Exploration for Environmental Monitoring The Resource Constrained Perspective

Langue : Anglais

Auteurs :

Couverture de l’ouvrage Multi-robot Exploration for Environmental Monitoring

Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective provides readers with the necessary robotics and mathematical tools required to realize the correct architecture. The architecture discussed in the book is not confined to environment monitoring, but can also be extended to search-and-rescue, border patrolling, crowd management and related applications. Several law enforcement agencies have already started to deploy UAVs, but instead of using teleoperated UAVs this book proposes methods to fully automate surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process.

Several challenges when deploying such models in real missions are addressed and solved, thus laying stepping stones towards realizing the architecture proposed. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics.

Part-I: The Curtain Raiser1. Introduction2. Target Environment3. Utilizing Robots4. Simultaneuous Localization and Mapping (SLAM)

Part-II: The Essentials5. Preliminaries6. Gaussian Process7. Coverage Path Planning8. Informative Path Planning

Part-III: Mission Characterization9. Problem Formulation10. Endurance and Energy Estimation11. Range Estimation

Part-IV: Scaling to Multiple Robots12. Multi-robot Systems13. Fusion

Part-V: Continuous Spatiotemporal Dynamics14. Temporal Evolutions

Part-VI: Epilogue15. Algal Bloom Monitoring16. Cumulus Cloud Monitoring17. Search and Rescue18. Signal strength based localization 19. Conclusion

[1] Graduate students in Computer Engineering, Robotics, Machine Learning, Mechatronics and Computer Science courses who intend to test their machine learning models on real robot hardware capable of full on-board processing.
[2] Ph.D. students, post-doctoral researchers etc., who intend to develop and optimize state-of-the-art machine learning models to guarantee real-time performance on robotic platforms.
[3] Government agencies like EPA and other associated bodies who want to monitor and control pollutants and hazardous materials
Kshitij Tiwari is a Postdoctoral Researcher at the Department of Electrical Engineering & Automation, School of Electrical Engineering, Aalto University, Finland. He
works with the Intelligent Robotics Group within the Department. He received the Ph.D. (2018) from the Japan Advanced Institute of Science & Technology (JAIST),
Japan. He obtained the M.Sc. in Artificial Intelligence with a special focus in Intelligent Robotics from the University of Edinburgh (2014) and the B.Engg. in Electronics & Communication from the University of Hong Kong (2013). His research interests include (but are not limited to) field robotics, applied machine learning, neuronavigation, path planning under uncertainty, and related domains.
Nak Young Chong is a Professor in Robotics at JAIST, Japan. He received the B.S.,M.S., and Ph.D. degrees in mechanical engineering from Hanyang University, Seoul, Korea, in 1987, 1989, and 1994, respectively. From 1994 to 2007, he was a member of research staff at Daewoo Heavy Industries and KIST in Korea, and MEL and AIST in Japan. In 2003, he joined the faculty of Japan Advanced Institute of Science and Technology (JAIST), where he currently is a Professor of Information Science. He also served as Vice Dean for Research and Director of the Center for Intelligent Robotics at JAIST. He was a Visiting Scholar at Northwestern University, Georgia Institute of Technology, University of Genoa, and Carnegie Mellon University, and also served as an Associate Graduate Faculty at the University of Nevada, Las Vegas, International Scholar at Kyung Hee University, and Distinguished Invited Research Professor at Hanyang University. He serves as Senior Editor of the IEEE Robotics and Automation Letters, Topic Editor-in-Chief of International Journal of Advanced Robotic Systems, and served as Senior Editor of IEEE ICRA CEB, and IEEE CASE CEB, and Associate Editor of the IEEE Transactions on Robotics and Journal of Intelligent Service Robotics. He served as Pr
  • Analyzes the constant conflict between machine learning models and robot resources
  • Presents a novel range estimation framework tested on real robots (custom built and commercially available)