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

Über die Autorinnen und Autoren

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 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.

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Multi-robot Exploration for Environmental Monitoring

By Kshitij Tiwari and Nak Young Chong

Multi-robot Exploration for Environmental Monitoring is designed to cater to both machine learning and robotics enthusiasts alike. This is the first-of-a-kind book length discussion pertaining to deployment of mobile robots for environment monitoring and the challenges faced therewith. This book presents the novel resource constrained perspective, which discusses how to balance the trade-off between the amount of information to be gathered to the available resources (battery, flight time, etc.)

This six-part book is organized so that the former parts introduce the basic concepts. The intermediate parts talk about how to deploy a single robot whilst accounting for its resource constraints and the machine learning models that can be used to monitor the environment of choice. The latter parts of the book then scale this to a multi-robot setting, highlight the challenges hence introduced, and describe some real-world success stories.

As a case study, the environment monitoring application is discussed at length, and the interplay between machine learning models (software) and robots (hardware) is analyzed. Several challenges when deploying such models in real missions have been addressed and solved, thereby laying down stepping stones toward realizing the architecture proposed herewith. The architecture discussed in the book is not confined only to environment monitoring and can be extended to search-and-rescue, border patrolling, crowd management, and related applications. Several law enforcement agencies have already started to deploy UAVs to assist them, but instead of using teleoperated UAVs, this proposed method can be used to fully automate the surveillance missions in the future.

Key Features

• 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)