Unmanned Aerial Systems (eBook, ePUB)

Theoretical Foundation and Applications
Redaktion: Koubaa, Anis; Azar, Ahmad Taher

• Format: ePub

Produktbeschreibung

Unmanned Aerial Systems: Theoretical Foundation and Applications presents some of the latest innovative approaches to drones from the point-of-view of dynamic modeling, system analysis, optimization, control, communications, 3D-mapping, search and rescue, surveillance, farmland and construction monitoring, and more. With the emergence of low-cost UAS, a vast array of research works in academia and products in the industrial sectors have evolved. The book covers the safe operation of UAS, including, but not limited to, fundamental design, mission and path planning, control theory, computer vision, artificial intelligence, applications requirements, and more.

This book provides a unique reference of the state-of-the-art research and development of unmanned aerial systems, making it an essential resource for researchers, instructors and practitioners.

• Covers some of the most innovative approaches to drones
• Provides the latest state-of-the-art research and development surrounding unmanned aerial systems
• Presents a comprehensive reference on unmanned aerial systems, with a focus on cutting-edge technologies and recent research trends in the area

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 650
• Erscheinungstermin: 21. Januar 2021
• Englisch
• ISBN-13: 9780128202777
• Artikelnr.: 61688820

Autorenporträt

Anis Koubaa is a Professor in Computer Science, Advisor to the Rector, and Leader of the Robotics and Internet of Things Research Lab, in Prince Sultan University. He is also R&D Consultant at Gaitech Robotics in China and Senior Researcher in CISTER/INESC TEC and ISEP-IPP, Porto, Portugal. He has been the Chair of the ACM Chapter in Saudi Arabia since 2014. He is also a Senior Fellow of the Higher Education Academy (HEA) in UK. He received several distinctions and awards including the Rector Research Award in 2010 at Al-Imam Mohamed bin Saud University, and the Rector Teaching Award in 2016 at Prince Sultan University. He is the Editor in Chief of the Robotics Software Engineering topic of the International Journal of Advanced Robotics Systems, Associate Editor in the Cyber-Physical Journal (Taylor & Francis). He is also the authors of six books with Springer on robots, sensor networks and Robot Operating Systems (ROS). He has been also actively participating in program committee

s of several international conferences including, ACM/IEEE International Conference on Cyber-Physical Systems, International Conference on Robotics Computing, European Conference on Wireless Sensor Networks, IEEE International Conference on Autonomous Robot Systems and Competitions, IEEE International Workshop on Factory Communication Systems. He is the author of more than 200 journal and conference publications, and one patent. He received several research grants as principal investigator, and he established research collaboration between Prince Sultan University and Gaitech Robotics for the development of robots and drones, and ROS.

Inhaltsangabe

1. UAS System Design
2. UAS Control systems
3. Hybrid control of UAS
4. Obstacle and collision avoidance of UAS
5. UAV onboard data storage, transmission and retrieval
6. Kalman and Particle filtering and other advanced techniques for motion sensor data fusion
7. Simultaneous Localization and Mapping (SLAM)
8. Single/multiple IMU-Vision-based navigation and orientation
9. Autopilots and navigation: standard and advanced solutions for navigation integrity
10. Integration of UAS into the Internet
11. IoT applications using UAS
12. Safety issues of UAS
13. Ultra-Wide Band (UWB) localization
14. Security threats of UAS
15. UAS public deployment challenges
16. UAS for cloud robotics
17. Deep neural networks (DNN) for field aerial robot perception (e.g., object detection, or semantic classification for navigation)
18. Recurrent networks for state estimation and dynamic identification of aerial vehicles
19. Deep-reinforcement learning for aerial robots (discrete-, or continuous-control) in dynamic environments
20. Learning-based aerial manipulation in cluttered environments
21. Decision making or task planning using machine learning for field aerial robots
22. Long-term ecological monitoring based on UAVs
23. Ecological Integrity parameters mapping
24. Rapid risk and disturbance assessment using drones
25. Ecosystem structure and processes assessment by using UAVs