LEADER 11430nam 2200733 450 001 9910130592203321 005 20221206105359.0 010 $a1-283-94127-9 010 $a1-118-37718-4 010 $a1-118-37716-8 010 $a1-118-37717-6 024 7 $a10.1002/9781118377178 035 $a(CKB)3400000000085569 035 $a(EBL)861774 035 $a(SSID)ssj0000715377 035 $a(PQKBManifestationID)11472378 035 $a(PQKBTitleCode)TC0000715377 035 $a(PQKBWorkID)10700916 035 $a(PQKB)11028612 035 $a(MiAaPQ)EBC861774 035 $a(CaBNVSL)mat06266789 035 $a(IDAMS)0b000064818b36d3 035 $a(IEEE)6266789 035 $a(PPN)169585328 035 $a(OCoLC)809248238 035 $a(EXLCZ)993400000000085569 100 $a20151221d2012 uy 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRemote sensing and actuation using networked unmanned vehicles /$fHaiyang Chao, Yangquan Chen 210 1$aHoboken, New Jersey :$cWiley-IEEE Press,$d2012. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2012] 215 $a1 online resource (236 p.) 225 1 $aIEEE press series on systems science and engineering ;$v3 225 0$aIEEE Press series on systems science and engineering 300 $aDescription based upon print version of record. 311 $a1-118-12276-3 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $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. 327 $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? 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. 327 $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. 327 $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. 330 $aHow to design and use unmanned vehicles for remote sensing and actuation-a practical guideOwing to their ability to replace human beings in dangerous, tedious, or repetitive jobs, unmanned systems are increasingly used in river/reservoir surveillance and the monitoring and control of chemical/nuclear leaks. This book presents new and innovative techniques for the design and use of unmanned vehicles for remote sensing and distributed control in agricultural and environmental systems.Focusing on small, unmanned aerial vehicles (UAVs), Remote Sensing and Actuation Using Unmanned Vehicles first describes the design of AggieAir, a low-cost UAV platform for remote sensing. It then explains how to solve state estimation and advanced lateral flight controller design problems in the small UAV platform before examining remote sensing problems with single and multiple UAVs. The book also includes flight test results-building upon these measurements to present actuation algorithms for such missions as diffusion control.Inside, readers will discover:. How to develop low-cost, small unmanned aircraft systems (UAS) for remote sensing applications. What autopilots are available for small UAVs, including a series of flight test protocols for the safe operation of small UAVs. How to design and implement advanced fractional-order controllers for autonomous navigation of UAVs. Voronoi diagram-based cooperative controller design for diffusion control in unmanned vehicles for both sensing and actuation. How to design and validate consensus-based controllers for rendezvous and formation control in unmanned ground vehiclesIncluding an appendix with IMU communication protocols and Paparazzi UAV code modification guides, Remote Sensing and Actuation Using Unmanned Vehicles is an invaluable guide for scientists and engineers in remote sensing, aerospace, robotics, and autonomous control. 410 0$aIEEE press series on systems science and engineering ;$v3 606 $aGeomorphology$xRemote sensing 606 $aEnvironmental monitoring$xRemote sensing 606 $aVehicles, Remotely piloted 615 0$aGeomorphology$xRemote sensing. 615 0$aEnvironmental monitoring$xRemote sensing. 615 0$aVehicles, Remotely piloted. 676 $a621.3678 686 $aTEC036000$2bisacsh 700 $aChao$b Haiyang$0845642 701 $aChen$b Yangquan$f1966-$0633747 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910130592203321 996 $aRemote sensing and actuation using networked unmanned vehicles$91887813 997 $aUNINA LEADER 01407nam 2200433 450 001 9910138042903321 005 20230421054708.0 010 $a88-913-0104-3 035 $a(CKB)3280000000012064 035 $a(ItFiC)it 02486781 035 $a(MiAaPQ)EBC5991187 035 $a(EXLCZ)993280000000012064 100 $a20200523d1993 uy 0 101 0 $aita 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdriano Augusto $estudi e ricerche /$fMario Attilio Levi 210 1$aRoma :$c"L'Erma" di Bretschneider,$d[1993] 210 4$d©1993 215 $a148 p 225 1 $aMonografie / Centro ricerche e documentazione sull'antichita? classica ;$v16 300 $aM.A.Levi, professor at the Universitą statale of Milan. 300 $aTitle and number of series appear on cover. 311 $a88-7062-843-4 320 $aIncludes bibliographical references and index. 410 0$aMonografie (Centro ricerche e documentazione sull'antichita? classica (Rome, Italy)) ;$v16. 607 $aRome$xHistory$yHadrian, 117-138 676 $a937.07092 676 $a937.07092 700 $aLevi$b Mario Attilio$f1902-1998,$0155679 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910138042903321 996 $aAdriano Augusto$9176109 997 $aUNINA LEADER 02726nam 2200589 a 450 001 9910791073303321 005 20230330183230.0 010 $a1-322-42337-7 010 $a1-4833-2580-6 010 $a1-4522-5315-3 035 $a(CKB)2550000001194145 035 $a(EBL)1598374 035 $a(SSID)ssj0001111554 035 $a(PQKBManifestationID)12503486 035 $a(PQKBTitleCode)TC0001111554 035 $a(PQKBWorkID)11156261 035 $a(PQKB)10349219 035 $a(MiAaPQ)EBC1598374 035 $a(OCoLC)875379032 035 $a(StDuBDS)EDZ0000159145 035 $a(EXLCZ)992550000001194145 100 $a20130912h19921992 fy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aChild psychiatric epidemiology $econcepts, methods, and findings /$fFrank C. Verhulst, Hans M. Koot 210 1$aThousand Oaks, Calif. :$cSAGE,$d1992. 210 4$a©1992 215 $a1 online resource (x, 157 pages) $cillustrations 225 1 $aDevelopmental clinical psychology and psychiatry ;$vvolume 23 300 $aDescription based upon print version of record. 311 0 $a0-8039-3997-3 311 0 $a0-8039-3996-5 320 $aIncludes bibliographical references and indexes. 327 $aCover; Contents; Series Editor's Introduction; Preface; Chapter 1 - Basic Concepts; Chapter 2 - Child Psychiatric Epidemiology; Chapter 3 - Assessment and Diagnosis; Chapter 4 - Sampling; Chapter 5 - Prevalence Studies: A Review; References; Author Index; Subject Index; About the Authors 330 $aA wide variety of questions pertaining to the etiology, course and therapy of child psychiatric conditions are answered in this concise volume. Central issues related to diagnosis, assessment, sampling and cross-cultural studies are presented in a highly readable fashion. Research on prevalence and patterns of childhood dysfunction and how these are influenced by such factors as age, sex and social class are presented in detail. The authors also provide a detailed description of community surveys and offer a referral pathway to psychiatric care for children. 410 0$aDevelopmental clinical psychology and psychiatry ;$v23. 606 $aChild psychiatry 606 $aPsychiatric epidemiology 615 0$aChild psychiatry. 615 0$aPsychiatric epidemiology. 676 $a618.9289 700 $aVerhulst$b F. C$g(Frank Cornelis),$f1951-$01493156 701 $aKoot$b Hans M$01493157 801 0$bStDuBDS 801 1$bStDuBDS 906 $aBOOK 912 $a9910791073303321 996 $aChild psychiatric epidemiology$93716019 997 $aUNINA