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

Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective is designed to cater to both beginners and experts in the domain alike. Former parts of the book serve to familiarize the readers with the necessary robotics and mathematical tools required to realize the architecture. The architecture discussed in the book is not confined to environment monitoring, 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 book proposed methods to fully automate the surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process. This book deals with applied machine learning for Robotics applications. As a case study, the environment monitoring application is discussed and the interplay between machine learning models (software) and robots (hardware) is analysed. Several challenges when deploying such models in real missions have been addressed and solved, thereby, laying down stepping stones towards realizing the architecture proposed herewith. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics Analyses the constant conflict between the machine learning models and robot resourcesUnlike existing machine learning techniques, this book does not split the data into training and testing data a priori The expert? is capable of doing this in real time This allows for active data acquisition which is useful especially in situations when robots are deployed in unknown environmentsPresents a novel range estimation framework and tested on real robots (custom built and commercially available) INDICE: 1. Introduction2. Preliminaries3. Modelling the Spatial Variations of the Environment using Stationary Homoscedastic GPs4. Resource Constrained Path Planning with Homing Guarantee5. Operational Range Estimation6. Fusion of Distributed Gaussian Process Experts (FuDGE)7. Towards a Spatiotemporal Environment Monitoring for Continuous Domains8. Conclusion and Future Works9. Appendix

• ISBN: 978-0-12-817607-8
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 290
• Fecha Publicación: 01/11/2019
• Nº Volúmenes: 1
• Idioma: Inglés