Overview
- Investigates the latest Deep Learning applications in theoretical and practical fields of for any unmanned system, robot, drone, underwater, etc.
- Includes selected and extended high-quality papers related to application of Deep Learning for Unmanned Systems from the Smart Systems and Emerging Technologies conference (SMARTTECH 2020) which was held at Prince Sultan University, Riyadh, Saudi Arabia, during November 3–5, 2020
- Discusses different applications of Deep Learning in drones where Computational Intelligence methods have excellent potentials for use
Part of the book series: Studies in Computational Intelligence (SCI, volume 984)
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Table of contents (20 chapters)
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Front Matter
Keywords
About this book
This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.
In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN).
The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.
- The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)
- The book chapters present various techniques of deep learning for robotic applications.
- The book chapters contain a good literature survey with a long list of references.
- The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.
- The book chapters are lucidly illustrated with numerical examples and simulations.
- The book chapters discuss details of applications and future research areas.
Editors and Affiliations
Bibliographic Information
Book Title: Deep Learning for Unmanned Systems
Editors: Anis Koubaa, Ahmad Taher Azar
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-77939-9
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-77938-2Published: 02 October 2021
Softcover ISBN: 978-3-030-77941-2Published: 03 October 2022
eBook ISBN: 978-3-030-77939-9Published: 01 October 2021
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: VIII, 732
Number of Illustrations: 82 b/w illustrations, 281 illustrations in colour
Topics: Control, Robotics, Mechatronics, Computational Intelligence, Data Engineering