Atjaunināt sīkdatņu piekrišanu

E-grāmata: Remote Sensing and Actuation Using Unmanned Vehicles

,
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 114,15 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Remote Sensing and Actuation Using Unmanned VehiclesPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

"This book will stimulate other researchers in this field to work on more practical questions and provide some insights to industrial engineers who want to use unmanned systems for their application problems"--

"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"--



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.
List of Figures
xv
List of Tables
xix
Foreword xxi
Preface xxiii
Acknowledgments xxv
Acronyms xxvii
1 Introduction
1(14)
1.1 Monograph Roadmap
1(6)
1.1.1 Sensing and Control in the Information-Rich World
1(2)
1.1.2 Typical Civilian Application Scenarios
3(2)
1.1.3 Challenges in Sensing and Control Using Unmanned Vehicles
5(2)
1.2 Research Motivations
7(4)
1.2.1 Small Unmanned Aircraft System Design for Remote Sensing
7(1)
1.2.2 State Estimation for Small UAVs
8(1)
1.2.3 Advanced Flight Control for Small UAVs
9(1)
1.2.4 Cooperative Remote Sensing Using Multiple UAVs
10(1)
1.2.5 Diffusion Control Using Mobile Actuator and Sensor Networks
11(1)
1.3 Monograph Contributions
11(1)
1.4 Monograph Organization
12(3)
References
12(3)
2 AggieAir: A Low-Cost Unmanned Aircraft System for Remote Sensing
15(38)
2.1 Introduction
15(2)
2.2 Small UAS Overview
17(9)
2.2.1 Autopilot Hardware
19(2)
2.2.2 Autopilot Software
21(1)
2.2.3 Typical Autopilots for Small UAVs
22(4)
2.3 AggieAir UAS Platform
26(13)
2.3.1 Remote Sensing Requirements
26(1)
2.3.2 AggieAir System Structure
27(3)
2.3.3 Flying-Wing Airframe
30(1)
2.3.4 OSAM-Paparazzi Autopilot
31(1)
2.3.5 OSAM Image Payload Subsystem
32(4)
2.3.6 gRAID Image Georeference Subsystem
36(3)
2.4 OSAM-Paparazzi Interface Design for IMU Integration
39(6)
2.4.1 Hardware Interface Connections
40(1)
2.4.2 Software Interface Design
41(4)
2.5 AggieAir UAS Test Protocol and Tuning
45(2)
2.5.1 AggieAir UAS Test Protocol
45(1)
2.5.2 AggieAir Controller Tuning Procedure
46(1)
2.6 Typical Platforms and Flight Test Results
47(3)
2.6.1 Typical Platforms
47(1)
2.6.2 Flight Test Results
48(2)
2.7
Chapter Summary
50(3)
References
50(3)
3 Attitude Estimation Using Low-Cost IMUs for Small Unmanned Aerial Vehicles
53(24)
3.1 State Estimation Problem Definition
54(1)
3.2 Rigid Body Rotations Basics
55(5)
3.2.1 Frame Definition
55(1)
3.2.2 Rotation Representations
56(1)
3.2.3 Conversion Between Rotation Representations
57(1)
3.2.4 UAV Kinematics
58(2)
3.3 Low-Cost Inertial Measurement Units: Hardware and Sensor Suites
60(5)
3.3.1 IMU Basics and Notations
60(1)
3.3.2 Sensor Packs
61(2)
3.3.3 IMU Categories
63(1)
3.3.4 Example Low-Cost IMUs
63(2)
3.4 Attitude Estimation Using Complementary Filters on SO(3)
65(3)
3.4.1 Passive Complementary Filter
66(1)
3.4.2 Explicit Complementary Filter
66(1)
3.4.3 Flight Test Results
67(1)
3.5 Attitude Estimation Using Extended Kalman Filters
68(2)
3.5.1 General Extended Kalman Filter
68(1)
3.5.2 Quaternion-Based Extended Kalman Filter
69(1)
3.5.3 Euler Angles-Based Extended Kalman Filter
69(1)
3.6 AggieEKF: GPS-Aided Extended Kalman Filter
70(4)
3.7
Chapter Summary
74(3)
References
74(3)
4 Lateral Channel Fractional Order Flight Controller Design for a Small UAV
77(24)
4.1 Introduction
77(1)
4.2 Preliminaries of UAV Flight Control
78(1)
4.3 Roll-Channel System Identification and Control
79(2)
4.3.1 System Model
80(1)
4.3.2 Excitation Signal for System Identification
80(1)
4.3.3 Parameter Optimization
81(1)
4.4 Fractional Order Controller Design
81(5)
4.4.1 Fractional Order Operators
81(1)
4.4.2 PIλ Controller Design
82(3)
4.4.3 Fractional Order Controller Implementation
85(1)
4.5 Simulation Results
86(6)
4.5.1 Introduction to Aerosim Simulation Platform
87(1)
4.5.2 Roll-Channel System Identification
87(2)
4.5.3 Fractional-Order PI Controller Design Procedure
89(1)
4.5.4 Integer-Order PID Controller Design
90(1)
4.5.5 Comparison
90(2)
4.6 UAV Flight Testing Results
92(7)
4.6.1 The ChangE UAV Platform
92(2)
4.6.2 System Identification
94(2)
4.6.3 Proportional Controller and Integer Order PI Controller Design
96(1)
4.6.4 Fractional Order PI Controller Design
97(1)
4.6.5 Flight Test Results
98(1)
4.7
Chapter Summary
99(2)
References
99(2)
5 Remote Sensing Using Single Unmanned Aerial Vehicle
101(20)
5.1 Motivations for Remote Sensing
102(1)
5.1.1 Water Management and Irrigation Control Requirements
102(1)
5.1.2 Introduction of Remote Sensing
102(1)
5.2 Remote Sensing Using Small UAVs
103(6)
5.2.1 Coverage Control
103(2)
5.2.2 Georeference Problem
105(4)
5.3 Sample Applications for AggieAir UAS
109(10)
5.3.1 Real-Time Surveillance
109(1)
5.3.2 Farmland Coverage
109(2)
5.3.3 Road Surveying
111(1)
5.3.4 Water Area Coverage
112(1)
5.3.5 Riparian Surveillance
112(3)
5.3.6 Remote Data Collection
115(1)
5.3.7 Other Applications
116(3)
5.4
Chapter Summary
119(2)
References
119(2)
6 Cooperative Remote Sensing Using Multiple Unmanned Vehicles
121(22)
6.1 Consensus-Based Formation Control
122(7)
6.1.1 Consensus Algorithms
122(1)
6.1.2 Implementation of Consensus Algorithms
123(1)
6.1.3 MASnet Hardware Platform
123(2)
6.1.4 Experimental Results
125(4)
6.2 Surface Wind Profile Measurement Using Multiple UAVs
129(11)
6.2.1 Problem Definition: Wind Profile Measurement
131(2)
6.2.2 Wind Profile Measurement Using UAVs
133(2)
6.2.3 Wind Profile Measurement Using Multiple UAVs
135(1)
6.2.4 Preliminary Simulation and Experimental Results
136(4)
6.3
Chapter Summary
140(3)
References
140(3)
7 Diffusion Control Using Mobile Sensor and Actuator Networks
143(24)
7.1 Motivation and Background
143(1)
7.2 Mathematical Modeling and Problem Formulation
144(2)
7.3 CVT-Based Dynamical Actuator Motion Scheduling Algorithm
146(1)
7.3.1 Motion Planning for Actuators with the First-Order Dynamics
146(1)
7.3.2 Motion Planning for Actuators with the Second-Order Dynamics
147(1)
7.3.3 Neutralizing Control
147(1)
7.4 Grouping Effect in CVT-Based Diffusion Control
147(7)
7.4.1 Grouping for CVT-Based Diffusion Control
148(1)
7.4.2 Diffusion Control Simulation with Different Group Sizes
148(2)
7.4.3 Grouping Effect Summary
150(4)
7.5 Information Consensus in CVT-Based Diffusion Control
154(4)
7.5.1 Basic Consensus Algorithm
154(1)
7.5.2 Requirements of Diffusion Control
154(1)
7.5.3 Consensus-Based CVT Algorithm
155(3)
7.6 Simulation Results
158(6)
7.7
Chapter Summary
164(3)
References
164(3)
8 Conclusions and Future Research Suggestions
167(4)
8.1 Conclusions
167(1)
8.2 Future Research Suggestions
168(3)
8.2.1 VTOL UAS Design for Civilian Applications
168(1)
8.2.2 Monitoring and Control of Fast-Evolving Processes
169(1)
8.2.3 Other Future Research Suggestions
169(1)
References
170(1)
Appendix
171(26)
A.1 List of Documents for CSOIS Flight Test Protocol
171(2)
A.1.1 Sample CSOIS-OSAM Flight Test Request Form
171(1)
A.1.2 Sample CSOIS-OSAM 48 in. UAV (IR) In-lab Inspection Form
172(1)
A.1.3 Sample Preflight Checklist
172(1)
A.2 IMU/GPS Serial Communication Protocols
173(9)
A.2.1 u-blox GPS Serial Protocol
173(1)
A.2.2 Crossbow MNAV IMU Serial Protocol
173(1)
A.2.3 Microstrain GX2 IMU Serial Protocol
174(4)
A.2.4 Xsens Mti-g IMU Serial Protocol
178(4)
A.3 Paparazzi Autopilot Software Architecture: A Modification Guide
182(10)
A.3.1 Autopilot Software Structure
182(1)
A.3.2 Airborne C Files
183(1)
A.3.3 OSAM-Paparazzi Interface Implementation
184(1)
A.3.4 Configuration XML Files
185(4)
A.3.5 Roll-Channel Fractional Order Controller Implementation
189(3)
A.4 DiffMas2D Code Modification Guide
192(5)
A.4.1 Files Description
192(1)
A.4.2 Diffusion Animation Generation
193(1)
A.4.3 Implementation of CVT-Consensus Algorithm
193(2)
References
195(2)
Topic Index 197
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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811183771852e.html
Keywords: