Haiyang Chao, YangQuan Chen

Remote Sensing and Actuation Using Unmanned Vehicles

• Gebundenes Buch

Produktbeschreibung

How to design and use unmanned vehicles for remote sensing and actuation--a practical guide
Owing 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 vehicles
Including 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.

Produktdetails

• IEEE Press Series on Systems Science and Engineering
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 240
• Erscheinungstermin: 28. August 2012
• Englisch
• Abmessung: 235mm x 157mm x 17mm
• Gewicht: 500g
• ISBN-13: 9781118122761
• ISBN-10: 1118122763
• Artikelnr.: 35060413

Autorenporträt

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.

Inhaltsangabe

List 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 3.3 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
PIlambda 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 5.2.1 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 8.2.1 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