Unmanned Aerial Systems
Theoretical Foundation and Applications
Series: Advances in Nonlinear Dynamical Systems and Robotics (ANDC);
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Product details:
- Publisher Elsevier Science
- Date of Publication 26 January 2021
- ISBN 9780128202760
- Binding Paperback
- No. of pages650 pages
- Size 228x152 mm
- Weight 1040 g
- Language English 132
Categories
Long 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.
MoreTable of Contents:
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
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