Sense and Avoid in UAS

Plamen Parvanov Angelov, Lancaster University, UKPlamen 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.

“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 xvAbout the Editor xixAbout the Contributors xxiPart I Introduction1 Introduction 3George Limnaios, Nikos Tsourveloudis and Kimon P. Valavanis1.1 UAV versus UAS 31.2 Historical Perspective on Unmanned Aerial Vehicles 51.3 UAV Classification 91.4 UAV Applications 141.5 UAS Market Overview 171.6 UAS Future Challenges 201.7 Fault Tolerance for UAS 26References 312 Performance Tradeoffs and the Development of Standards 35Andrew Zeitlin2.1 Scope of Sense and Avoid 352.2 System Configurations 362.3 S&A Services and Sub-functions 382.4 Sensor Capabilities 392.4.1 Airborne Sensing 392.4.2 Ground-Based Sensing 412.4.3 Sensor Parameters 412.5 Tracking and Trajectory Prediction 422.6 Threat Declaration and Resolution Decisions 432.6.1 Collision Avoidance 432.6.2 Self-separation 452.6.3 Human Decision versus Algorithm 452.7 Sense and Avoid Timeline 462.8 Safety Assessment 482.9 Modeling and Simulation 492.10 Human Factors 502.11 Standards Process 512.11.1 Description 512.11.2 Operational and Functional Requirements 522.11.3 Architecture 522.11.4 Safety, Performance, and Interoperability Assessments 522.11.5 Performance Requirements 522.11.6 Validation 532.12 Conclusion 54References 543 Integration of SAA Capabilities into a UAS Distributed Architecture for Civil Applications 55Pablo Royo, Eduard Santamaria, Juan Manuel Lema, Enric Pastor and Cristina Barrado3.1 Introduction 553.2 System Overview 573.2.1 Distributed System Architecture 583.3 USAL Concept and Structure 593.4 Flight and Mission Services 613.4.1 Air Segment 613.4.2 Ground Segment 653.5 Awareness Category at USAL Architecture 683.5.1 Preflight Operational Procedures: Flight Dispatcher 703.5.2 USAL SAA on Airfield Operations 723.5.3 Awareness Category during UAS Mission 753.6 Conclusions 82Acknowledgments 82References 82Part II Regulatory Issues and Human Factors4 Regulations and Requirements 87Xavier Prats, Jorge Ramírez, Luis Delgado and Pablo Royo4.1 Background Information 884.1.1 Flight Rules 904.1.2 Airspace Classes 914.1.3 Types of UAS and their Missions 934.1.4 Safety Levels 964.2 Existing Regulations and Standards 974.2.1 Current Certification Mechanisms for UAS 994.2.2 Standardization Bodies and Safety Agencies 1024.3 Sense and Avoid Requirements 1034.3.1 General Sense Requirements 1034.3.2 General Avoidance Requirements 1064.3.3 Possible SAA Requirements as a Function of the Airspace Class 1084.3.4 Possible SAA Requirements as a Function of the Flight Altitude and Visibility Conditions 1094.3.5 Possible SAA Requirements as a Function of the Type of Communications Relay 1104.3.6 Possible SAA Requirements as a Function of the Automation Level of the UAS 1114.4 Human Factors and Situational Awareness Considerations 1124.5 Conclusions 113Acknowledgments 114References 1155 Human Factors in UAV 119Marie Cahillane, Chris Baber and Caroline Morin5.1 Introduction 1195.2 Teleoperation of UAVs 1225.3 Control of Multiple Unmanned Vehicles 1235.4 Task-Switching 1245.5 Multimodal Interaction with Unmanned Vehicles 1275.6 Adaptive Automation 1285.7 Automation and Multitasking 1295.8 Individual Differences 1315.8.1 Attentional Control and Automation 1315.8.2 Spatial Ability 1345.8.3 Sense of Direction 1355.8.4 Video Games Experience 1355.9 Conclusions 136References 137Part III SAA Methodologies6 Sense and Avoid Concepts: Vehicle-Based SAA Systems (Vehicle-to-Vehicle) 145Štěpán Kopřiva, David Šišlák and Michal Pěchouček6.1 Introduction 1456.2 Conflict Detection and Resolution Principles 1466.2.1 Sensing 1466.2.2 Trajectory Prediction 1476.2.3 Conflict Detection 1486.2.4 Conflict Resolution 1496.2.5 Evasion Maneuvers 1506.3 Categorization of Conflict Detection and Resolution Approaches 1506.3.1 Taxonomy 1506.3.2 Rule-Based Methods 1516.3.3 Game Theory Methods 1526.3.4 Field Methods 1536.3.5 Geometric Methods 1546.3.6 Numerical Optimization Approaches 1566.3.7 Combined Methods 1586.3.8 Multi-agent Methods 1606.3.9 Other Methods 163Acknowledgments 166References 1667 UAS Conflict Detection and Resolution Using Differential Geometry Concepts 175Hyo-Sang Shin, Antonios Tsourdos and Brian White7.1 Introduction 1757.2 Differential Geometry Kinematics 1777.3 Conflict Detection 1787.3.1 Collision Kinematics 1787.3.2 Collision Detection 1807.4 Conflict Resolution: Approach I 1827.4.1 Collision Kinematics 1837.4.2 Resolution Guidance 1867.4.3 Analysis and Extension 1887.5 Conflict Resolution: Approach II 1917.5.1 Resolution Kinematics and Analysis 1927.5.2 Resolution Guidance 1937.6 CD&R Simulation 1957.6.1 Simulation Results: Approach I 1957.6.2 Simulation Results: Approach II 1997.7 Conclusions 200References 2038 Aircraft Separation Management Using Common Information Network SAA 205Richard Baumeister and Graham Spence8.1 Introduction 2058.2 CIN Sense and Avoid Requirements 2088.3 Automated Separation Management on a CIN 2128.3.1 Elements of Automated Aircraft Separation 2128.3.2 Grid-Based Separation Automation 2148.3.3 Genetic-Based Separation Automation 2148.3.4 Emerging Systems-Based Separation Automation 2168.4 Smart Skies Implementation 2178.4.1 Smart Skies Background 2178.4.2 Flight Test Assets 2178.4.3 Communication Architecture 2198.4.4 Messaging System 2218.4.5 Automated Separation Implementation 2238.4.6 Smart Skies Implementation Summary 2238.5 Example SAA on a CIN – Flight Test Results 2248.6 Summary and Future Developments 229Acknowledgments 231References 231Part IV SAA Applications9 AgentFly: Scalable, High-Fidelity Framework for Simulation, Planning and Collision Avoidance of Multiple UAVs 235David Šišlák, Přemysl Volf, Štěpán Kopřiva and Michal Pěchouček9.1 Agent-Based Architecture 2369.1.1 UAV Agents 2379.1.2 Environment Simulation Agents 2379.1.3 Visio Agents 2389.2 Airplane Control Concept 2389.3 Flight Trajectory Planner 2419.4 Collision Avoidance 2459.4.1 Multi-layer Collision Avoidance Architecture 2469.4.2 Cooperative Collision Avoidance 2479.4.3 Non-cooperative Collision Avoidance 2509.5 Team Coordination 2529.6 Scalable Simulation 2569.7 Deployment to Fixed-Wing UAV 260Acknowledgments 263References 26310 See and Avoid Using Onboard Computer Vision 265John Lai, Jason J. Ford, Luis Mejias, Peter O’Shea and Rod Walker10.1 Introduction 26510.1.1 Background 26510.1.2 Outline of the SAA Problem 26510.2 State-of-the-Art 26610.3 Visual-EO Airborne Collision Detection 26810.3.1 Image Capture 26810.3.2 Camera Model 26910.4 Image Stabilization 26910.4.1 Image Jitter 26910.4.2 Jitter Compensation Techniques 27010.5 Detection and Tracking 27210.5.1 Two-Stage Detection Approach 27210.5.2 Target Tracking 27810.6 Target Dynamics and Avoidance Control 27810.6.1 Estimation of Target Bearing 27810.6.2 Bearing-Based Avoidance Control 27910.7 Hardware Technology and Platform Integration 28110.7.1 Target/Intruder Platforms 28110.7.2 Camera Platforms 28210.7.3 Sensor Pod 28610.7.4 Real-Time Image Processing 28810.8 Flight Testing 28910.8.1 Test Phase Results 29010.9 Future Work 29010.10 Conclusions 291Acknowledgements 291References 29111 The Use of Low-Cost Mobile Radar Systems for Small UAS Sense and Avoid 295Michael Wilson11.1 Introduction 29511.2 The UAS Operating Environment 29711.2.1 Why Use a UAS? 29711.2.2 Airspace and Radio Carriage 29711.2.3 See-and-Avoid 29711.2.4 Midair Collisions 29811.2.5 Summary 29911.3 Sense and Avoid and Collision Avoidance 30011.3.1 A Layered Approach to Avoiding Collisions 30011.3.2 SAA Technologies 30011.3.3 The UA Operating Volume 30311.3.4 Situation Awareness 30411.3.5 Summary 30411.4 Case Study: The Smart Skies Project 30511.4.1 Introduction 30511.4.2 Smart Skies Architecture 30511.4.3 The Mobile Aircraft Tracking System 30711.4.4 The Airborne Systems Laboratory 31011.4.5 The Flamingo UAS 31111.4.6 Automated Dynamic Airspace Controller 31111.4.7 Summary 31211.5 Case Study: Flight Test Results 31211.5.1 Radar Characterisation Experiments 31211.5.2 Sense and Avoid Experiments 31911.5.3 Automated Sense and Avoid 32411.5.4 Dynamic Sense and Avoid Experiments 32611.5.5 Tracking a Variety of Aircraft 32611.5.6 Weather Monitoring 33111.5.7 The Future 33211.6 Conclusion 333Acknowledgements 333References 334Epilogue 337Index 339