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E-raamat: Sense and Avoid in UAS: Research and Applications

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  • Sari: Aerospace Series
  • Ilmumisaeg: 06-Mar-2012
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119963950
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Sense and Avoid in UAS: Research and ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Aerospace Series
  • Ilmumisaeg: 06-Mar-2012
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119963950
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There is increasing interest in the potential of UAV (Unmanned Aerial Vehicle) and MAV (Micro Air Vehicle) technology and their wide ranging applications including defence missions, reconnaissance and surveillance, border patrol, disaster zone assessment and atmospheric research. High investment levels from the military sector globally is driving research and development and increasing the viability of autonomous platforms as replacements for the remotely piloted vehicles more commonly in use.

UAV/UAS pose a number of new challenges, with the autonomy and in particular collision avoidance, detect and avoid, or sense and avoid, as the most challenging one, involving both regulatory and technical issues.

Sense and Avoid in UAS: Research and Applications covers the problem of detect, sense and avoid in UAS (Unmanned Aircraft Systems) in depth and combines the theoretical and application results by leading academics and researchers from industry and academia.

Key features:

  • Presents a holistic view of the sense and avoid problem in the wider application of autonomous systems
  • Includes information on human factors, regulatory issues and navigation, control, aerodynamics and physics aspects of the sense and avoid problem in UAS
  • Provides professional, scientific and reliable content that is easy to understand, and
  • Includes contributions from leading engineers and researchers in the field
Sense and Avoid in UAS: Research and Applications is an invaluable source of original and specialised information. It acts as a reference manual for practising engineers and advanced theoretical researchers and also forms a useful resource for younger engineers and postgraduate students. With its credible sources and thorough review process, Sense and Avoid in UAS: Research and Applications provides a reliable source of information in an area that is fast expanding but scarcely covered.

Arvustused

This book is a good introductory book for anyone interested in unmanned aerial systems and presents in a very comprehensive manner the challenges associated with the basic task of sense and avoid.  (The Aeronautical Journal, 1 January 2014)

 

Preface xv
About the Editor xix
About the Contributors xxi
Part I Introduction
1 Introduction
3(32)
George Limnaios
Nikos Tsourveloudis
Kimon P. Valavanis
1.1 UAV versus UAS
3(2)
1.2 Historical Perspective on Unmanned Aerial Vehicles
5(4)
1.3 UAV Classification
9(5)
1.4 UAV Applications
14(3)
1.5 UAS Market Overview
17(3)
1.6 UAS Future Challenges
20(6)
1.7 Fault Tolerance for UAS
26(5)
References
31(4)
2 Performance Tradeoffs and the Development of Standards
35(20)
Andrew Zeitlin
2.1 Scope of Sense and Avoid
35(1)
2.2 System Configurations
36(2)
2.3 S&A Services and Sub-functions
38(1)
2.4 Sensor Capabilities
39(3)
2.4.1 Airborne Sensing
39(2)
2.4.2 Ground-Based Sensing
41(1)
2.4.3 Sensor Parameters
41(1)
2.5 Tracking and Trajectory Prediction
42(1)
2.6 Threat Declaration and Resolution Decisions
43(3)
2.6.1 Collision Avoidance
43(2)
2.6.2 Self-separation
45(1)
2.6.3 Human Decision versus Algorithm
45(1)
2.7 Sense and Avoid Timeline
46(2)
2.8 Safety Assessment
48(1)
2.9 Modeling and Simulation
49(1)
2.10 Human Factors
50(1)
2.11 Standards Process
51(3)
2.11.1 Description
51(1)
2.11.2 Operational and Functional Requirements
52(1)
2.11.3 Architecture
52(1)
2.11.4 Safety, Performance, and Interoperability Assessments
52(1)
2.11.5 Performance Requirements
52(1)
2.11.6 Validation
53(1)
2.12 Conclusion
54(1)
References
54(1)
3 Integration of SAA Capabilities into a UAS Distributed Architecture for Civil Applications
55(32)
Pablo Royo
Eduard Santamaria
Juan Manuel Lema
Enric Pastor
Cristina Barrado
3.1 Introduction
55(2)
3.2 System Overview
57(2)
3.2.1 Distributed System Architecture
58(1)
3.3 USAL Concept and Structure
59(2)
3.4 Flight and Mission Services
61(7)
3.4.1 Air Segment
61(4)
3.4.2 Ground Segment
65(3)
3.5 Awareness Category at USAL Architecture
68(14)
3.5.1 Preflight Operational Procedures: Flight Dispatcher
70(2)
3.5.2 USAL SAA on Airfield Operations
72(3)
3.5.3 Awareness Category during UAS Mission
75(7)
3.6 Conclusions
82(1)
Acknowledgments
82(1)
References
82(5)
Part II Regulatory Issues and Human Factors
4 Regulations and Requirements
87(32)
Xavier Prats
Jorge Ramirez
Luis Delgado
Pablo Royo
4.1 Background Information
88(9)
4.1.1 Flight Rules
90(1)
4.1.2 Airspace Classes
91(2)
4.1.3 Types of UAS and their Missions
93(3)
4.1.4 Safety Levels
96(1)
4.2 Existing Regulations and Standards
97(6)
4.2.1 Current Certification Mechanisms for UAS
99(3)
4.2.2 Standardization Bodies and Safety Agencies
102(1)
4.3 Sense and Avoid Requirements
103(9)
4.3.1 General Sense Requirements
103(3)
4.3.2 General Avoidance Requirements
106(2)
4.3.3 Possible SAA Requirements as a Function of the Airspace Class
108(1)
4.3.4 Possible SAA Requirements as a Function of the Flight Altitude and Visibility Conditions
109(1)
4.3.5 Possible SAA Requirements as a Function of the Type of Communications Relay
110(1)
4.3.6 Possible SAA Requirements as a Function of the Automation Level of the UAS
111(1)
4.4 Human Factors and Situational Awareness Considerations
112(1)
4.5 Conclusions
113(1)
Acknowledgments
114(1)
References
115(4)
5 Human Factors in UAV
119(26)
Marie Cahillane
Chris Baber
Caroline Morin
5.1 Introduction
119(3)
5.2 Teleoperation of UAVs
122(1)
5.3 Control of Multiple Unmanned Vehicles
123(1)
5.4 Task-Switching
124(3)
5.5 Multimodal Interaction with Unmanned Vehicles
127(1)
5.6 Adaptive Automation
128(1)
5.7 Automation and Multitasking
129(2)
5.8 Individual Differences
131(5)
5.8.1 Attentional Control and Automation
131(3)
5.8.2 Spatial Ability
134(1)
5.8.3 Sense of Direction
135(1)
5.8.4 Video Games Experience
135(1)
5.9 Conclusions
136(1)
References
137(8)
Part III SAA Methodologies
6 Sense and Avoid Concepts: Vehicle-Based SAA Systems (Vehicle-to-Vehicle)
145(30)
Stepan Kopriva
David Sislak
Michal Pechoucek
6.1 Introduction
145(1)
6.2 Conflict Detection and Resolution Principles
146(4)
6.2.1 Sensing
146(1)
6.2.2 Trajectory Prediction
147(1)
6.2.3 Conflict Detection
148(1)
6.2.4 Conflict Resolution
149(1)
6.2.5 Evasion Maneuvers
150(1)
6.3 Categorization of Conflict Detection and Resolution Approaches
150(16)
6.3.1 Taxonomy
150(1)
6.3.2 Rule-Based Methods
151(1)
6.3.3 Game Theory Methods
152(1)
6.3.4 Field Methods
153(1)
6.3.5 Geometric Methods
154(2)
6.3.6 Numerical Optimization Approaches
156(2)
6.3.7 Combined Methods
158(2)
6.3.8 Multi-agent Methods
160(3)
6.3.9 Other Methods
163(3)
Acknowledgments
166(1)
References
166(9)
7 UAS Conflict Detection and Resolution Using Differential Geometry Concepts
175(30)
Hyo-Sang Shin
Antonios Tsourdos
Brian White
7.1 Introduction
175(2)
7.2 Differential Geometry Kinematics
177(1)
7.3 Conflict Detection
178(4)
7.3.1 Collision Kinematics
178(2)
7.3.2 Collision Detection
180(2)
7.4 Conflict Resolution: Approach I
182(9)
7.4.1 Collision Kinematics
183(3)
7.4.2 Resolution Guidance
186(2)
7.4.3 Analysis and Extension
188(3)
7.5 Conflict Resolution: Approach II
191(4)
7.5.1 Resolution Kinematics and Analysis
192(1)
7.5.2 Resolution Guidance
193(2)
7.6 CD&R Simulation
195(5)
7.6.1 Simulation Results: Approach I
195(4)
7.6.2 Simulation Results: Approach II
199(1)
7.7 Conclusions
200(3)
References
203(2)
8 Aircraft Separation Management Using Common Information Network SAA
205(30)
Richard Baumeister
Graham Spence
8.1 Introduction
205(3)
8.2 CIN Sense and Avoid Requirements
208(4)
8.3 Automated Separation Management on a CIN
212(5)
8.3.1 Elements of Automated Aircraft Separation
212(2)
8.3.2 Grid-Based Separation Automation
214(1)
8.3.3 Genetic-Based Separation Automation
214(2)
8.3.4 Emerging Systems-Based Separation Automation
216(1)
8.4 Smart Skies Implementation
217(7)
8.4.1 Smart Skies Background
217(1)
8.4.2 Flight Test Assets
217(2)
8.4.3 Communication Architecture
219(2)
8.4.4 Messaging System
221(2)
8.4.5 Automated Separation Implementation
223(1)
8.4.6 Smart Skies Implementation Summary
223(1)
8.5 Example SAA on a CIN - Flight Test Results
224(5)
8.6 Summary and Future Developments
229(2)
Acknowledgments
231(1)
References
231(4)
Part IV SAA Applications
9 AgentFly: Scalable, High-Fidelity Framework for Simulation, Planning and Collision Avoidance of Multiple UAVs
235(30)
David Sislak
Premysl Volf
Stepan Kopriva
Michal Pechoucek
9.1 Agent-Based Architecture
236(2)
9.1.1 UAV Agents
237(1)
9.1.2 Environment Simulation Agents
237(1)
9.1.3 Visio Agents
238(1)
9.2 Airplane Control Concept
238(3)
9.3 Flight Trajectory Planner
241(4)
9.4 Collision Avoidance
245(7)
9.4.1 Multi-layer Collision Avoidance Architecture
246(1)
9.4.2 Cooperative Collision Avoidance
247(3)
9.4.3 Non-cooperative Collision Avoidance
250(2)
9.5 Team Coordination
252(4)
9.6 Scalable Simulation
256(4)
9.7 Deployment to Fixed-Wing UAV
260(3)
Acknowledgments
263(1)
References
263(2)
10 See and Avoid Using Onboard Computer Vision
265(30)
John Lai
Jason J. Ford
Luis Mejias
Peter O'Shea
Rod Walker
10.1 Introduction
265(1)
10.1.1 Background
265(1)
10.1.2 Outline of the SAA Problem
265(1)
10.2 State-of-the-Art
266(2)
10.3 Visual-EO Airborne Collision Detection
268(1)
10.3.1 Image Capture
268(1)
10.3.2 Camera Model
269(1)
10.4 Image Stabilization
269(3)
10.4.1 Image Jitter
269(1)
10.4.2 Jitter Compensation Techniques
270(2)
10.5 Detection and Tracking
272(6)
10.5.1 Two-Stage Detection Approach
272(6)
10.5.2 Target Tracking
278(1)
10.6 Target Dynamics and Avoidance Control
278(3)
10.6.1 Estimation of Target Bearing
278(1)
10.6.2 Bearing-Based Avoidance Control
279(2)
10.7 Hardware Technology and Platform Integration
281(8)
10.7.1 Target/Intruder Platforms
281(1)
10.7.2 Camera Platforms
282(4)
10.7.3 Sensor Pod
286(2)
10.7.4 Real-Time Image Processing
288(1)
10.8 Flight Testing
289(1)
10.8.1 Test Phase Results
290(1)
10.9 Future Work
290(1)
10.10 Conclusions
291(1)
Acknowledgements
291(1)
References
291(4)
11 The Use of Low-Cost Mobile Radar Systems for Small UAS Sense and Avoid
295(42)
Michael Wilson
11.1 Introduction
295(2)
11.2 The UAS Operating Environment
297(3)
11.2.1 Why Use a UAS?
297(1)
11.2.2 Airspace and Radio Carriage
297(1)
11.2.3 See-and-Avoid
297(1)
11.2.4 Midair Collisions
298(1)
11.2.5 Summary
299(1)
11.3 Sense and Avoid and Collision Avoidance
300(5)
11.3.1 A Layered Approach to Avoiding Collisions
300(1)
11.3.2 SAA Technologies
300(3)
11.3.3 The UA Operating Volume
303(1)
11.3.4 Situation Awareness
304(1)
11.3.5 Summary
304(1)
11.4 Case Study: The Smart Skies Project
305(7)
11.4.1 Introduction
305(1)
11.4.2 Smart Skies Architecture
305(2)
11.4.3 The Mobile Aircraft Tracking System
307(3)
11.4.4 The Airborne Systems Laboratory
310(1)
11.4.5 The Flamingo UAS
311(1)
11.4.6 Automated Dynamic Airspace Controller
311(1)
11.4.7 Summary
312(1)
11.5 Case Study: Flight Test Results
312(21)
11.5.1 Radar Characterisation Experiments
312(7)
11.5.2 Sense and Avoid Experiments
319(5)
11.5.3 Automated Sense and Avoid
324(2)
11.5.4 Dynamic Sense and Avoid Experiments
326(1)
11.5.5 Tracking a Variety of Aircraft
326(5)
11.5.6 Weather Monitoring
331(1)
11.5.7 The Future
332(1)
11.6 Conclusion
333(1)
Acknowledgements
333(1)
References
334(3)
Epilogue 337(2)
Index 339
Plamen Parvanov Angelov, Lancaster University, UK Plamen Parvanov is a senior lecturer in the School of Computing and Communications at Lancaster University. He is an Associate Editor of three international journals and the founding co-Editor-in-Chief of the Springer journal Evolving Systems. He is also the Vice Chair of the Technical Committee on Standards, Computational Intelligence Society, IEEE and co-Chair of several IEEE conferences. His research in UAV/UAS is often publicised in external publications, e.g. the prestigious Computational Intelligence Magazine; Aviation Week, Flight Global, Airframer, Flight International, etc. His research focuses on computational intelligence and evolving systems, and his research in to autonomous systems has received worldwide recognition. As the Principle Investigator at Lancaster University for a team working on UAV Sense and Avoid fortwo projects of ASTRAEA his work was recognised by 'The Engineer Innovation and Technology 2008 Award in two categories: i) Aerospace and Defence and ii) The Special Award which is an outstanding achievement.
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