Imaging and Sensing for Unmanned Aircraft Systems: Deployment and Applications (Control, Robotics and Sensors)

Description

This two-volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS). Volume 1 concentrates on UAS control and performance methodologies including Computer Vision and Data Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision in Indoor and Outdoor Drones, Sensors and Computer Vision, and Small UAV for Persistent Surveillance. Volume 2 focuses on UAS deployment and applications including UAV-CPSs as a Testbed for New Technologies and a Primer to Industry 5.0, Human-Machine Interface Design, Open Source Software (OSS) and Hardware (OSH), Image Transmission in MIMO-OSTBC System, Image Database, Communications Requirements, Video Streaming, and Communications Links, Multispectral vs Hyperspectral Imaging, Aerial Imaging and Reconstruction of Infrastructures, Deep Learning as an Alternative to Super Resolution Imaging, and Quality of Experience (QoE) and Quality of Service (QoS). Cover Contents About the editors Preface 1 UAV-CPSs as a test bed for new technologies and a primer to Industry 5.0 1.1 Introduction 1.2 Cloud computing 1.3 Collective UAV learning 1.4 Human computation, crowdsourcing and call centres 1.5 Open-source and open-access resources 1.6 Challenges and future directions 1.7 Conclusions References 2 UAS human factors and human–machine interface design 2.1 Introduction 2.2 UAS HMI functionalities 2.2.1 Reconfigurable displays 2.2.2 Sense and avoid 2.2.3 Mission planning and management 2.2.4 Multi-platform coordination 2.3 GCS HMI elements 2.4 Human factors program 2.4.1 Requirements definition, capture and refinement 2.4.2 Task analysis 2.4.3 Hierarchal task analysis 2.4.4 Cognitive task analysis 2.4.5 Critical task analysis 2.4.6 Operational sequence diagram 2.4.7 Systems design and development 2.4.8 Design evaluation 2.4.9 Verification and validation 2.5 Future work 2.6 Conclusions References 3 Open-source software (OSS) and hardware (OSH) in UAVs 3.1 Introduction 3.2 Open source software 3.3 Open source UAS 3.4 Universal messaging protocol 3.5 GCS software 3.6 Processing software 3.7 Operator information and communication 3.8 Open source platform 3.9 Future work 3.9.1 OSH challenges 3.9.2 Open data 3.9.3 Cloud data centre 3.9.4 Crowd-sourced data in UAV-CPSs 3.9.5 Control of UAV swarms 3.10 Conclusions References 4 Image transmission in UAV MIMO UWB-OSTBC system over Rayleigh channel using multiple description coding (MDC) 4.1 Introduction 4.1.1 The efficiency of the flat Rayleigh fading channel 4.2 Multiple description coding 4.3 Multiple input–multiple output 4.4 Diversity 4.5 Simulations results 4.6 Discussion and future trends 4.7 Conclusion References 5 Image database of low-altitude UAV flights with flight condition-logged for photogrammetry, remote sensing, and computer vision 5.1 Introduction 5.1.1 Image processing system for UAVs 5.2 The aerial image database framework 5.2.1 Database requirements 5.2.2 Database design 5.3 Image capture process 5.4 Results 5.4.1 Images collected 5.5 Use of the image database 5.5.1 Mosaics 5.5.2 Development of CV algorithms 5.6 Conclusion and future works Acknowledgements References 6 Communications requirements, video streaming, communications links and networked UAVs 6.1 Introduction 6.2 Flying Ad-hoc Networks 6.3 The FANET protocol 6.4 FANET: streaming and surveillance 6.5 Discussion and future trends 6.5.1 FNs' placement search algorithms 6.5.2 Event detection and video quality selection algorithms 6.5.3 Onboard video management (UAV) 6.5.4 Video-rate adaptation for the fleet platform 6.5.5 FNs coordination 6.5.6 Data collection and presentation 6.5.7 Software-Defined Networking 6.5.8 Network Function Virtualisation 6.5.9 Data Gathering versus Energy Harvesting 6.6 Conclusion References 7 Multispectral vs hyperspectral imaging for unmanned aerial vehicles: current and prospective state of affairs 7.1 Introduction 7.2 UAV imaging architecture and components 7.2.1 Future scope for UAV 7.3 Multispectral vs. hyperspectral imaging instruments 7.3.1 Multispectral imaging 7.3.1.1 Low-resolution imaging 7.3.1.2 High-resolution imaging 7.3.2 Hyperspectral imaging 7.3.3 Satellite imaging vs UAV imaging 7.4 UAV image processing workflow 7.4.1 Atmospheric correction 7.4.2 Spectral influence mapping 7.4.3 Dimensionality reduction 7.4.4 Computational tasks 7.5 Data processing toolkits for spatial data 7.6 UAV open data sets for research–multispectral and hyperspectral 7.7 Applications of MSI and HSI UAV imaging 7.7.1 Agriculture monitoring 7.7.2 Coastal monitoring 7.7.3 Forestry 7.7.4 Urban planning 7.7.5 Defence applications 7.7.6 Environmental monitoring 7.7.7 Other commercial uses 7.8 Conclusion and future scope References 8 Aerial imaging and reconstruction of infrastructures by UAVs 8.1 Introduction 8.2 Related Studies 8.3 Visual sensors and mission planner 8.3.1 Image projection 8.3.2 Path planner 8.4 3D reconstruction 8.4.1 Stereo mapping 8.4.2 Monocular mapping 8.5 Data-set collection 8.5.1 Experimental setup 8.5.2 Data set 1 8.5.3 Data set 2 8.6 Experimental results 8.6.1 Indoor scenario 8.6.2 Outdoor Scenario 1 8.6.3 Outdoor Scenario 2 8.6.4 Underground scenario 8.7 Future trends 8.8 Conclusions References 9 Deep learning as an alternative to super-resolution imaging in UAV systems 9.1 Introduction 9.2 The super-resolution model 9.2.1 Motion estimation 9.2.2 Dehazing 9.2.3 Patch selection 9.2.4 Super-resolution 9.3 Experiments and results 9.3.1 Peak signal-to-noise ratio 9.4 Critical issues in SR deployment in UAV-CPSs 9.4.1 Big data 9.4.2 Cloud computing services 9.4.3 Image acquisition hardware limitations 9.4.4 Video SR 9.4.5 Efficient metrics and other evaluation strategies 9.4.6 Multiple priors 9.4.7 Regularisation 9.4.8 Novel architectures 9.4.9 3D SR 9.4.9.1 Depth map super-resolution 9.4.10 Deep learning and computational intelligence 9.4.11 Network design 9.5 Conclusion References 10 Quality of experience (QoE) and quality of service (QoS) in UAV systems 10.1 Introduction 10.1.1 Airborne network from a CPS perspective 10.2 Definitions 10.2.1 Parameters that impact QoS/QoE 10.2.2 Impact of cloud distance on QoS/QoE 10.2.3 QoS/QoE monitoring framework in UAV-CPSs 10.2.4 Application-level management 10.2.5 Network-level management 10.2.6 Cloud distance management 10.2.7 QoS/QoE service-level management 10.2.8 QoS/QoE metrics in UAV-CPSs 10.2.9 Mapping of QoS to QoE 10.2.10 Subjective vs objective measurement 10.2.11 Tools to measure QoS/QoE 10.3 Applications 10.3.1 Social networks, gaming and human–machine interfaces 10.3.2 Data centres 10.3.3 Electric power grid and energy systems 10.3.4 Networking systems 10.3.5 Surveillance 10.4 Case studies 10.4.1 Application scenario 1: UAV-CPSs in traffic congestion management 10.4.1.1 QoS management at the FN 10.4.1.2 QoS management at the UAV-CPS UCN 10.4.2 Application scenario 2: congestion and accident avoidance using intelligent vehicle systems 10.5 Future and open challenges 10.5.1 Modelling and design 10.5.2 Collaborative services 10.5.3 Streaming 10.5.4 Security 10.5.5 Flying ad hoc networks 10.5.6 User emotions 10.6 Conclusion References 11 Conclusions Index Back Cover