000			12362nam a2201033 i 4500
001			6266789
003			IEEE
005			20220712205832.0
006			m o d
007			cr |n|||||||||
008			151221s2012 njua ob 001 eng d
020			_a9781118377178 _qebook
020			_z9781118122761 _qprint
020			_z1118377176 _qelectronic
024	7		_a10.1002/9781118377178 _2doi
035			_a(CaBNVSL)mat06266789
035			_a(IDAMS)0b000064818b36d3
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aGB400.42.R4 _bC46 2012eb
082	0	4	_a621.36/78 _223
100	1		_aChao, Haiyang, _eauthor. _928030
245	1	0	_aRemote sensing and actuation using networked unmanned vehicles / _cHaiyang Chao, Yangquan Chen.
264		1	_aHoboken, New Jersey : _bWiley-IEEE Press, _c2012.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012]
300			_a1 PDF (axxviii, 198 pages) : _billustrations (some color).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE press series on systems science and engineering ; _v3
500			_aIn Wiley online library
504			_aIncludes bibliographical references.
505	0		_aList of Figures xv -- List of Tables xix -- Foreword xxi -- Preface xxiii -- Acknowledgments xxv -- Acronyms xxvii -- 1 Introduction 1 -- 1.1 Monograph Roadmap 1 -- 1.1.1 Sensing and Control in the Information-Rich World 1 -- 1.1.2 Typical Civilian Application Scenarios 3 -- 1.1.3 Challenges in Sensing and Control Using Unmanned Vehicles 5 -- 1.2 Research Motivations 7 -- 1.2.1 Small Unmanned Aircraft System Design for Remote Sensing 7 -- 1.2.2 State Estimation for Small UAVs 8 -- 1.2.3 Advanced Flight Control for Small UAVs 9 -- 1.2.4 Cooperative Remote Sensing Using Multiple UAVs 10 -- 1.2.5 Diffusion Control Using Mobile Actuator and Sensor Networks 11 -- 1.3 Monograph Contributions 11 -- 1.4 Monograph Organization 12 -- References 12 -- 2 AggieAir: A Low-Cost Unmanned Aircraft System for Remote Sensing 15 -- 2.1 Introduction 15 -- 2.2 Small UAS Overview 17 -- 2.2.1 Autopilot Hardware 19 -- 2.2.2 Autopilot Software 21 -- 2.2.3 Typical Autopilots for Small UAVs 22 -- 2.3 AggieAir UAS Platform 26 -- 2.3.1 Remote Sensing Requirements 26 -- 2.3.2 AggieAir System Structure 27 -- 2.3.3 Flying-Wing Airframe 30 -- 2.3.4 OSAM-Paparazzi Autopilot 31 -- 2.3.5 OSAM Image Payload Subsystem 32 -- 2.3.6 gRAID Image Georeference Subsystem 36 -- 2.4 OSAM-Paparazzi Interface Design for IMU Integration 39 -- 2.4.1 Hardware Interface Connections 40 -- 2.4.2 Software Interface Design 41 -- 2.5 AggieAir UAS Test Protocol and Tuning 45 -- 2.5.1 AggieAir UAS Test Protocol 45 -- 2.5.2 AggieAir Controller Tuning Procedure 46 -- 2.6 Typical Platforms and Flight Test Results 47 -- 2.6.1 Typical Platforms 47 -- 2.6.2 Flight Test Results 48 -- 2.7 Chapter Summary 50 -- References 50 -- 3 Attitude Estimation Using Low-Cost IMUs for Small Unmanned Aerial Vehicles 53 -- 3.1 State Estimation Problem Definition 54 -- 3.2 Rigid Body Rotations Basics 55 -- 3.2.1 Frame Definition 55 -- 3.2.2 Rotation Representations 56 -- 3.2.3 Conversion Between Rotation Representations 57 -- 3.2.4 UAV Kinematics 58.
505	8		_a3.3 Low-Cost Inertial Measurement Units: Hardware and Sensor Suites 60 -- 3.3.1 IMU Basics and Notations 60 -- 3.3.2 Sensor Packs 61 -- 3.3.3 IMU Categories 63 -- 3.3.4 Example Low-Cost IMUs 63 -- 3.4 Attitude Estimation Using Complementary Filters on SO(3) 65 -- 3.4.1 Passive Complementary Filter 66 -- 3.4.2 Explicit Complementary Filter 66 -- 3.4.3 Flight Test Results 67 -- 3.5 Attitude Estimation Using Extended Kalman Filters 68 -- 3.5.1 General Extended Kalman Filter 68 -- 3.5.2 Quaternion-Based Extended Kalman Filter 69 -- 3.5.3 Euler Angles-Based Extended Kalman Filter 69 -- 3.6 AggieEKF: GPS-Aided Extended Kalman Filter 70 -- 3.7 Chapter Summary 74 -- References 74 -- 4 Lateral Channel Fractional Order Flight Controller Design for a Small UAV 77 -- 4.1 Introduction 77 -- 4.2 Preliminaries of UAV Flight Control 78 -- 4.3 Roll-Channel System Identification and Control 79 -- 4.3.1 System Model 80 -- 4.3.2 Excitation Signal for System Identification 80 -- 4.3.3 Parameter Optimization 81 -- 4.4 Fractional Order Controller Design 81 -- 4.4.1 Fractional Order Operators 81 -- 4.4.2 PI(Sn(B Controller Design 82 -- 4.4.3 Fractional Order Controller Implementation 85 -- 4.5 Simulation Results 86 -- 4.5.1 Introduction to Aerosim Simulation Platform 87 -- 4.5.2 Roll-Channel System Identification 87 -- 4.5.3 Fractional-Order PI Controller Design Procedure 89 -- 4.5.4 Integer-Order PID Controller Design 90 -- 4.5.5 Comparison 90 -- 4.6 UAV Flight Testing Results 92 -- 4.6.1 The ChangE UAV Platform 92 -- 4.6.2 System Identification 94 -- 4.6.3 Proportional Controller and Integer Order PI Controller Design 96 -- 4.6.4 Fractional Order PI Controller Design 97 -- 4.6.5 Flight Test Results 98 -- 4.7 Chapter Summary 99 -- References 99 -- 5 Remote Sensing Using Single Unmanned Aerial Vehicle 101 -- 5.1 Motivations for Remote Sensing 102 -- 5.1.1 Water Management and Irrigation Control Requirements 102 -- 5.1.2 Introduction of Remote Sensing 102 -- 5.2 Remote Sensing Using Small UAVs 103.
505	8		_a5.2.1 Coverage Control 103 -- 5.2.2 Georeference Problem 105 -- 5.3 Sample Applications for AggieAir UAS 109 -- 5.3.1 Real-Time Surveillance 109 -- 5.3.2 Farmland Coverage 109 -- 5.3.3 Road Surveying 111 -- 5.3.4 Water Area Coverage 112 -- 5.3.5 Riparian Surveillance 112 -- 5.3.6 Remote Data Collection 115 -- 5.3.7 Other Applications 116 -- 5.4 Chapter Summary 119 -- References 119 -- 6 Cooperative Remote Sensing Using Multiple Unmanned Vehicles 121 -- 6.1 Consensus-Based Formation Control 122 -- 6.1.1 Consensus Algorithms 122 -- 6.1.2 Implementation of Consensus Algorithms 123 -- 6.1.3 MASnet Hardware Platform 123 -- 6.1.4 Experimental Results 125 -- 6.2 Surface Wind Profile Measurement Using Multiple UAVs 129 -- 6.2.1 Problem Definition: Wind Profile Measurement 131 -- 6.2.2 Wind Profile Measurement Using UAVs 133 -- 6.2.3 Wind Profile Measurement Using Multiple UAVs 135 -- 6.2.4 Preliminary Simulation and Experimental Results 136 -- 6.3 Chapter Summary 140 -- References 140 -- 7 Diffusion Control Using Mobile Sensor and Actuator Networks 143 -- 7.1 Motivation and Background 143 -- 7.2 Mathematical Modeling and Problem Formulation 144 -- 7.3 CVT-Based Dynamical Actuator Motion Scheduling Algorithm 146 -- 7.3.1 Motion Planning for Actuators with the First-Order Dynamics 146 -- 7.3.2 Motion Planning for Actuators with the Second-Order Dynamics 147 -- 7.3.3 Neutralizing Control 147 -- 7.4 Grouping Effect in CVT-Based Diffusion Control 147 -- 7.4.1 Grouping for CVT-Based Diffusion Control 148 -- 7.4.2 Diffusion Control Simulation with Different Group Sizes 148 -- 7.4.3 Grouping Effect Summary 150 -- 7.5 Information Consensus in CVT-Based Diffusion Control 154 -- 7.5.1 Basic Consensus Algorithm 154 -- 7.5.2 Requirements of Diffusion Control 154 -- 7.5.3 Consensus-Based CVT Algorithm 155 -- 7.6 Simulation Results 158 -- 7.7 Chapter Summary 164 -- References 164 -- 8 Conclusions and Future Research Suggestions 167 -- 8.1 Conclusions 167 -- 8.2 Future Research Suggestions 168.
505	8		_a8.2.1 VTOL UAS Design for Civilian Applications 168 -- 8.2.2 Monitoring and Control of Fast-Evolving Processes 169 -- 8.2.3 Other Future Research Suggestions 169 -- References 170 -- Appendix 171 -- A.1 List of Documents for CSOIS Flight Test Protocol 171 -- A.1.1 Sample CSOIS-OSAM Flight Test Request Form 171 -- A.1.2 Sample CSOIS-OSAM 48 in. UAV (IR) In-lab Inspection Form 172 -- A.1.3 Sample Preflight Checklist 172 -- A.2 IMU/GPS Serial Communication Protocols 173 -- A.2.1 u-blox GPS Serial Protocol 173 -- A.2.2 Crossbow MNAV IMU Serial Protocol 173 -- A.2.3 Microstrain GX2 IMU Serial Protocol 174 -- A.2.4 Xsens Mti-g IMU Serial Protocol 178 -- A.3 Paparazzi Autopilot Software Architecture: A Modification Guide 182 -- A.3.1 Autopilot Software Structure 182 -- A.3.2 Airborne C Files 183 -- A.3.3 OSAM-Paparazzi Interface Implementation 184 -- A.3.4 Configuration XML Files 185 -- A.3.5 Roll-Channel Fractional Order Controller Implementation 189 -- A.4 DiffMas2D Code Modification Guide 192 -- A.4.1 Files Description 192 -- A.4.2 Diffusion Animation Generation 193 -- A.4.3 Implementation of CVT-Consensus Algorithm 193 -- References 195 -- Topic Index 197.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aHow to design and use unmanned vehicles for remote sensing and actuation-a practical guideOwing to their ability to replace human beings in dangerous, tedious, or repetitive jobs, unmanned systems are increasingly used in river/reservoir surveillance and the monitoring and control of chemical/nuclear leaks. This book presents new and innovative techniques for the design and use of unmanned vehicles for remote sensing and distributed control in agricultural and environmental systems.Focusing on small, unmanned aerial vehicles (UAVs), Remote Sensing and Actuation Using Unmanned Vehicles first describes the design of AggieAir, a low-cost UAV platform for remote sensing. It then explains how to solve state estimation and advanced lateral flight controller design problems in the small UAV platform before examining remote sensing problems with single and multiple UAVs. The book also includes flight test results-building upon these measurements to present actuation algorithms for such missions as diffusion control.Inside, readers will discover:. How to develop low-cost, small unmanned aircraft systems (UAS) for remote sensing applications. What autopilots are available for small UAVs, including a series of flight test protocols for the safe operation of small UAVs. How to design and implement advanced fractional-order controllers for autonomous navigation of UAVs. Voronoi diagram-based cooperative controller design for diffusion control in unmanned vehicles for both sensing and actuation. How to design and validate consensus-based controllers for rendezvous and formation control in unmanned ground vehiclesIncluding an appendix with IMU communication protocols and Paparazzi UAV code modification guides, Remote Sensing and Actuation Using Unmanned Vehicles is an invaluable guide for scientists and engineers in remote sensing, aerospace, robotics, and autonomous control.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aGeomorphology _xRemote sensing. _928031
650		0	_aEnvironmental monitoring _xRemote sensing. _925281
650		0	_aVehicles, Remotely piloted. _928032
655		0	_aElectronic books. _93294
695			_aAcceleration
695			_aActuators
695			_aBatteries
695			_aBuildings
695			_aCameras
695			_aChemicals
695			_aClosed loop systems
695			_aDiffusion processes
695			_aElevators
695			_aEstimation
695			_aFuel cells
695			_aGlobal Positioning System
695			_aHardware
695			_aIndexes
695			_aMathematical model
695			_aMobile communication
695			_aMonitoring
695			_aNavigation
695			_aPath planning
695			_aPayloads
695			_aPollution
695			_aQuaternions
695			_aRemote sensing
695			_aRobot kinematics
695			_aRobot sensing systems
695			_aSea measurements
695			_aSensors
695			_aSpatial resolution
695			_aStability analysis
695			_aSteady-state
695			_aTemperature measurement
695			_aTemperature sensors
695			_aTopology
695			_aTuning
695			_aVectors
695			_aVegetation mapping
695			_aVehicles
700	1		_aChen, Yangquan, _d1966- _928033
710	2		_aIEEE Xplore (Online Service), _edistributor. _928034
710	2		_aJohn Wiley & Sons, _epublisher. _96902
776	0	8	_iPrint version: _z9781118122761
830		0	_aIEEE press series on systems science and engineering ; _v3 _98461
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266789
942			_cEBK
999			_c74249 _d74249