Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective

by Kshitij Tiwari, Nak Young Chong

Overview

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. - 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)

Product Details

ISBN-13:	9780128176085
Publisher:	Elsevier Science & Technology Books
Publication date:	11/29/2019
Sold by:	Barnes & Noble
Format:	eBook
Pages:	276
File size:	36 MB
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About the Author

Kshitij Tiwari is a Postdoctoral Researcher at the Department of Electrical Engineering & Automation, School of Electrical Engineering, Aalto University, Finland. Heworks 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 Program Chair/CoChair for JCK Robotics 2009, ICAM 2010, IEEE Ro-Man 2011, IEEE CASE 2012, IEEE Ro-Man 2013, URAI 2012/2013, and DARS 2014. He was a General Co-Chair of URAI 2017. He also served as Co-Chair for IEEE-RAS Networked Robots Technical Committee from 2004 to 2006, and Fujitsu Scientific System Working Group from 2004 to 2008.

Table of Contents

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

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From the Publisher

A detailed exploration of applied machine learning for robotics applications