Del 3 - IEEE Press Series on Systems Science and Engineering

Remote Sensing and Actuation Using Unmanned Vehicles

Inbunden, Engelska, 2012

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Unmanned systems and robotics technologies have become very popular recently owing to their ability to replace human beings in dangerous, tedious, or repetitious jobs. This book fill the gap in the field between research and real-world applications, providing scientists and engineers with essential information on how to design and employ networked unmanned vehicles for remote sensing and distributed control purposes. Target scenarios include environmental or agricultural applications such as river/reservoir surveillance, wind profiling measurement, and monitoring/control of chemical leaks.

Produktinformation

Utgivningsdatum2012-11-20
Mått161 x 243 x 18 mm
Vikt463 g
FormatInbunden
SpråkEngelska
SerieIEEE Press Series on Systems Science and Engineering
Antal sidor240
FörlagJohn Wiley & Sons Inc
ISBN9781118122761

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HAIYANG CHAO, PhD, is a postdoctoral fellow in the Department of Mechanical and Aerospace Engineering at West Virginia University in Morgantown. He authored or coauthored more than twenty peer-reviewed research papers and is one of the key developers of AggieAir, a low-cost, small UAV platform for remote sensing applications.YANGQUAN CHEN, PhD, is Associate Professor of Electrical and Computer Engineering at Utah State University in Logan. He holds fourteen U.S. patents and is the author of several research monographs and edited volumes, five textbooks, and over 500 peer-reviewed research papers.

List of Figures xvList of Tables xixForeword xxiPreface xxiiiAcknowledgments xxvAcronyms xxvii1 Introduction 11.1 Monograph Roadmap 11.1.1 Sensing and Control in the Information-Rich World 11.1.2 Typical Civilian Application Scenarios 31.1.3 Challenges in Sensing and Control Using Unmanned Vehicles 51.2 Research Motivations 71.2.1 Small Unmanned Aircraft System Design for Remote Sensing 71.2.2 State Estimation for Small UAVs 81.2.3 Advanced Flight Control for Small UAVs 91.2.4 Cooperative Remote Sensing Using Multiple UAVs 101.2.5 Diffusion Control Using Mobile Actuator and Sensor Networks 111.3 Monograph Contributions 111.4 Monograph Organization 12References 122 AggieAir: A Low-Cost Unmanned Aircraft System for Remote Sensing 152.1 Introduction 152.2 Small UAS Overview 172.2.1 Autopilot Hardware 192.2.2 Autopilot Software 212.2.3 Typical Autopilots for Small UAVs 222.3 AggieAir UAS Platform 262.3.1 Remote Sensing Requirements 262.3.2 AggieAir System Structure 272.3.3 Flying-Wing Airframe 302.3.4 OSAM-Paparazzi Autopilot 312.3.5 OSAM Image Payload Subsystem 322.3.6 gRAID Image Georeference Subsystem 362.4 OSAM-Paparazzi Interface Design for IMU Integration 392.4.1 Hardware Interface Connections 402.4.2 Software Interface Design 412.5 AggieAir UAS Test Protocol and Tuning 452.5.1 AggieAir UAS Test Protocol 452.5.2 AggieAir Controller Tuning Procedure 462.6 Typical Platforms and Flight Test Results 472.6.1 Typical Platforms 472.6.2 Flight Test Results 482.7 Chapter Summary 50References 503 Attitude Estimation Using Low-Cost IMUs for Small Unmanned Aerial Vehicles 533.1 State Estimation Problem Definition 543.2 Rigid Body Rotations Basics 553.2.1 Frame Definition 553.2.2 Rotation Representations 563.2.3 Conversion Between Rotation Representations 573.2.4 UAV Kinematics 583.3 Low-Cost Inertial Measurement Units: Hardware and Sensor Suites 603.3.1 IMU Basics and Notations 603.3.2 Sensor Packs 613.3.3 IMU Categories 633.3.4 Example Low-Cost IMUs 633.4 Attitude Estimation Using Complementary Filters on SO(3) 653.4.1 Passive Complementary Filter 663.4.2 Explicit Complementary Filter 663.4.3 Flight Test Results 673.5 Attitude Estimation Using Extended Kalman Filters 683.5.1 General Extended Kalman Filter 683.5.2 Quaternion-Based Extended Kalman Filter 693.5.3 Euler Angles-Based Extended Kalman Filter 693.6 AggieEKF: GPS-Aided Extended Kalman Filter 703.7 Chapter Summary 74References 744 Lateral Channel Fractional Order Flight Controller Design for a Small UAV 774.1 Introduction 774.2 Preliminaries of UAV Flight Control 784.3 Roll-Channel System Identification and Control 794.3.1 System Model 804.3.2 Excitation Signal for System Identification 804.3.3 Parameter Optimization 814.4 Fractional Order Controller Design 814.4.1 Fractional Order Operators 814.4.2 PIλ Controller Design 824.4.3 Fractional Order Controller Implementation 854.5 Simulation Results 864.5.1 Introduction to Aerosim Simulation Platform 874.5.2 Roll-Channel System Identification 874.5.3 Fractional-Order PI Controller Design Procedure 894.5.4 Integer-Order PID Controller Design 904.5.5 Comparison 904.6 UAV Flight Testing Results 924.6.1 The ChangE UAV Platform 924.6.2 System Identification 944.6.3 Proportional Controller and Integer Order PI Controller Design 964.6.4 Fractional Order PI Controller Design 974.6.5 Flight Test Results 984.7 Chapter Summary 99References 995 Remote Sensing Using Single Unmanned Aerial Vehicle 1015.1 Motivations for Remote Sensing 1025.1.1 Water Management and Irrigation Control Requirements 1025.1.2 Introduction of Remote Sensing 1025.2 Remote Sensing Using Small UAVs 1035.2.1 Coverage Control 1035.2.2 Georeference Problem 1055.3 Sample Applications for AggieAir UAS 1095.3.1 Real-Time Surveillance 1095.3.2 Farmland Coverage 1095.3.3 Road Surveying 1115.3.4 Water Area Coverage 1125.3.5 Riparian Surveillance 1125.3.6 Remote Data Collection 1155.3.7 Other Applications 1165.4 Chapter Summary 119References 1196 Cooperative Remote Sensing Using Multiple Unmanned Vehicles 1216.1 Consensus-Based Formation Control 1226.1.1 Consensus Algorithms 1226.1.2 Implementation of Consensus Algorithms 1236.1.3 MASnet Hardware Platform 1236.1.4 Experimental Results 1256.2 Surface Wind Profile Measurement Using Multiple UAVs 1296.2.1 Problem Definition: Wind Profile Measurement 1316.2.2 Wind Profile Measurement Using UAVs 1336.2.3 Wind Profile Measurement Using Multiple UAVs 1356.2.4 Preliminary Simulation and Experimental Results 1366.3 Chapter Summary 140References 1407 Diffusion Control Using Mobile Sensor and Actuator Networks 1437.1 Motivation and Background 1437.2 Mathematical Modeling and Problem Formulation 1447.3 CVT-Based Dynamical Actuator Motion Scheduling Algorithm 1467.3.1 Motion Planning for Actuators with the First-Order Dynamics 1467.3.2 Motion Planning for Actuators with the Second-Order Dynamics 1477.3.3 Neutralizing Control 1477.4 Grouping Effect in CVT-Based Diffusion Control 1477.4.1 Grouping for CVT-Based Diffusion Control 1487.4.2 Diffusion Control Simulation with Different Group Sizes 1487.4.3 Grouping Effect Summary 1507.5 Information Consensus in CVT-Based Diffusion Control 1547.5.1 Basic Consensus Algorithm 1547.5.2 Requirements of Diffusion Control 1547.5.3 Consensus-Based CVT Algorithm 1557.6 Simulation Results 1587.7 Chapter Summary 164References 1648 Conclusions and Future Research Suggestions 1678.1 Conclusions 1678.2 Future Research Suggestions 1688.2.1 VTOL UAS Design for Civilian Applications 1688.2.2 Monitoring and Control of Fast-Evolving Processes 1698.2.3 Other Future Research Suggestions 169References 170Appendix 171A.1 List of Documents for CSOIS Flight Test Protocol 171A.1.1 Sample CSOIS-OSAM Flight Test Request Form 171A.1.2 Sample CSOIS-OSAM 48 in. UAV (IR) In-lab Inspection Form 172A.1.3 Sample Preflight Checklist 172A.2 IMU/GPS Serial Communication Protocols 173A.2.1 u-blox GPS Serial Protocol 173A.2.2 Crossbow MNAV IMU Serial Protocol 173A.2.3 Microstrain GX2 IMU Serial Protocol 174A.2.4 Xsens Mti-g IMU Serial Protocol 178A.3 Paparazzi Autopilot Software Architecture: A Modification Guide 182A.3.1 Autopilot Software Structure 182A.3.2 Airborne C Files 183A.3.3 OSAM-Paparazzi Interface Implementation 184A.3.4 Configuration XML Files 185A.3.5 Roll-Channel Fractional Order Controller Implementation 189A.4 DiffMas2D Code Modification Guide 192A.4.1 Files Description 192A.4.2 Diffusion Animation Generation 193A.4.3 Implementation of CVT-Consensus Algorithm 193References 195Topic Index 197