Unmanned Aerial Systems
Theoretical Foundation and Applications
Published on 21. January 2021
650 pages
978-0-12-820277-7 (ISBN)
Description
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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Other editions
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Book
01/2021
Academic Press
€153.50
Shipment within 15-20 days
Content
1. UAS System Design2. UAS Control systems3. Hybrid control of UAS4. Obstacle and collision avoidance of UAS5. UAV onboard data storage, transmission and retrieval6. Kalman and Particle filtering and other advanced techniques for motion sensor data fusion7. Simultaneous Localization and Mapping (SLAM)8. Single/multiple IMU-Vision-based navigation and orientation9. Autopilots and navigation: standard and advanced solutions for navigation integrity10. Integration of UAS into the Internet11. IoT applications using UAS12. Safety issues of UAS13. Ultra-Wide Band (UWB) localization14. Security threats of UAS15. UAS public deployment challenges16. UAS for cloud robotics17. 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 vehicles19. Deep-reinforcement learning for aerial robots (discrete-, or continuous-control) in dynamic environments20. Learning-based aerial manipulation in cluttered environments21. Decision making or task planning using machine learning for field aerial robots22. Long-term ecological monitoring based on UAVs23. Ecological Integrity parameters mapping24. Rapid risk and disturbance assessment using drones25. Ecosystem structure and processes assessment by using UAVs
