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)
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. 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. Government agencies like EPA and other associated bodies who want to monitor and control pollutants and hazardous materials
3. Modelling the Spatial Variations of the Environment using Stationary Homoscedastic GPs
4. Resource Constrained Path Planning with Homing Guarantee
5. Operational Range Estimation
6. Fusion of Distributed Gaussian Process Experts (FuDGE)
7. Towards a Spatiotemporal Environment Monitoring for Continuous Domains
8. Conclusion and Future Works
- No. of pages:
- © Academic Press 2020
- 1st November 2019
- Academic Press
- Paperback ISBN:
Kshitij received his B.Engg (2013) in Electronics and Communication Engineering from the Department of EEE at the University of Hong Kong, HKSAR of China. He then pursued a M.Sc. in Artificial Intelligence (2014) with a special focus in Intelligent Robotics from the University of Edinburgh, Scotland. In 2015 he enrolled at the Japan Advanced Institute of Science and Technology (or JAIST) where he got his Ph.D. He has extensive experience in both the hardware (sensor design, signal processing, noise filtering, and mechatronics) and software (ROS, AI, Machine Learning). Furthermore, this experience spans both academia and industrial cadres and as of recently, he has been investigating the possibilities to optimize cutting edge ML models to make them amicable to existing robot hardware in light of current limitations.
Scholar, School of Information Science, Japan Advanced Institute of Science and Technology
Nak-Young 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/Co-Chair 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.
Professor, School of Information Science, Japan Advanced Institute of Science and Technology