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Remote sensing and actuation using networked unmanned vehicles [electronic resource] / Haiyang Chao, Yangquan Chen.

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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 esse...

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Bibliographic Details
Online Access: Full Text (via IEEE)
Main Author: Chao, Haiyang
Other Authors: Chen, YangQuan, 1966-
Format: Electronic eBook
Language:English
Published: Hoboken, New Jersey : Wiley-IEEE Press, ©2012.
Series:IEEE Press series on systems science and engineering.
Subjects:
Geomorphology > Remote sensing.
Environmental monitoring > Remote sensing.
Vehicles, Remotely piloted.
Table of Contents:
  • AggieAir: A Low-Cost Unmanned Aircraft System for Remote Sensing
  • Attitude Estimation Using Low-Cost IMUs for Small Unmanned Aerial Vehicles
  • Lateral Channel Fractional Order Flight Controller Design for a Small UAV
  • Remote Sensing Using Single Unmanned Aerial Vehicle
  • Cooperative Remote Sensing Using Multiple Unmanned Vehicles
  • Diffusion Control Using Mobile Sensor and Actuator Networks
  • Conclusions and Future Research Suggestions.
  • Machine generated contents note: 1. Introduction
  • 1.1. Monograph Roadmap
  • 1.1.1. Sensing and Control in the Information-Rich World
  • 1.1.2. Typical Civilian Application Scenarios
  • 1.1.3. Challenges in Sensing and Control Using Unmanned Vehicles
  • 1.2. Research Motivations
  • 1.2.1. Small Unmanned Aircraft System Design for Remote Sensing
  • 1.2.2. State Estimation for Small UAVs
  • 1.2.3. Advanced Flight Control for Small UAVs
  • 1.2.4. Cooperative Remote Sensing Using Multiple UAVs
  • 1.2.5. Diffusion Control Using Mobile Actuator and Sensor Networks
  • 1.3. Monograph Contributions
  • 1.4. Monograph Organization
  • References
  • 2. AggieAir: A Low-Cost Unmanned Aircraft System for Remote Sensing
  • 2.1. Introduction
  • 2.2. Small UAS Overview
  • 2.2.1. Autopilot Hardware
  • 2.2.2. Autopilot Software
  • 2.2.3. Typical Autopilots for Small UAVs
  • 2.3. AggieAir UAS Platform
  • 2.3.1. Remote Sensing Requirements
  • 2.3.2. AggieAir System Structure
  • 2.3.3. Flying-Wing Airframe
  • 2.3.4. OSAM-Paparazzi Autopilot
  • 2.3.5. OSAM Image Payload Subsystem
  • 2.3.6. gRAID Image Georeference Subsystem
  • 2.4. OSAM-Paparazzi Interface Design for IMU Integration
  • 2.4.1. Hardware Interface Connections
  • 2.4.2. Software Interface Design
  • 2.5. AggieAir UAS Test Protocol and Tuning
  • 2.5.1. AggieAir UAS Test Protocol
  • 2.5.2. AggieAir Controller Tuning Procedure
  • 2.6. Typical Platforms and Flight Test Results
  • 2.6.1. Typical Platforms
  • 2.6.2. Flight Test Results
  • 2.7. Chapter Summary
  • References
  • 3. Attitude Estimation Using Low-Cost IMUs for Small Unmanned Aerial Vehicles
  • 3.1. State Estimation Problem Definition
  • 3.2. Rigid Body Rotations Basics
  • 3.2.1. Frame Definition
  • 3.2.2. Rotation Representations
  • 3.2.3. Conversion Between Rotation Representations
  • 3.2.4. UAV Kinematics
  • 3.3. Low-Cost Inertial Measurement Units: Hardware and Sensor Suites
  • 3.3.1. IMU Basics and Notations
  • 3.3.2. Sensor Packs
  • 3.3.3. IMU Categories
  • 3.3.4. Example Low-Cost IMUs
  • 3.4. Attitude Estimation Using Complementary Filters on SO(3)
  • 3.4.1. Passive Complementary Filter
  • 3.4.2. Explicit Complementary Filter
  • 3.4.3. Flight Test Results
  • 3.5. Attitude Estimation Using Extended Kalman Filters
  • 3.5.1. General Extended Kalman Filter
  • 3.5.2. Quaternion-Based Extended Kalman Filter
  • 3.5.3. Euler Angles-Based Extended Kalman Filter
  • 3.6. AggieEKF: GPS-Aided Extended Kalman Filter
  • 3.7. Chapter Summary
  • References
  • 4. Lateral Channel Fractional Order Flight Controller Design for a Small UAV
  • 4.1. Introduction
  • 4.2. Preliminaries of UAV Flight Control
  • 4.3. Roll-Channel System Identification and Control
  • 4.3.1. System Model
  • 4.3.2. Excitation Signal for System Identification
  • 4.3.3. Parameter Optimization
  • 4.4. Fractional Order Controller Design
  • 4.4.1. Fractional Order Operators
  • 4.4.2. PIλ Controller Design
  • 4.4.3. Fractional Order Controller Implementation
  • 4.5. Simulation Results
  • 4.5.1. Introduction to Aerosim Simulation Platform
  • 4.5.2. Roll-Channel System Identification
  • 4.5.3. Fractional-Order PI Controller Design Procedure
  • 4.5.4. Integer-Order PID Controller Design
  • 4.5.5. Comparison
  • 4.6. UAV Flight Testing Results
  • 4.6.1. ChangE UAV Platform
  • 4.6.2. System Identification
  • 4.6.3. Proportional Controller and Integer Order PI Controller Design
  • 4.6.4. Fractional Order PI Controller Design
  • 4.6.5. Flight Test Results
  • 4.7. Chapter Summary
  • References
  • 5. Remote Sensing Using Single Unmanned Aerial Vehicle
  • 5.1. Motivations for Remote Sensing
  • 5.1.1. Water Management and Irrigation Control Requirements
  • 5.1.2. Introduction of Remote Sensing
  • 5.2. Remote Sensing Using Small UAVs
  • 5.2.1. Coverage Control
  • 5.2.2. Georeference Problem
  • 5.3. Sample Applications for AggieAir UAS
  • 5.3.1. Real-Time Surveillance
  • 5.3.2. Farmland Coverage
  • 5.3.3. Road Surveying
  • 5.3.4. Water Area Coverage
  • 5.3.5. Riparian Surveillance
  • 5.3.6. Remote Data Collection
  • 5.3.7. Other Applications
  • 5.4. Chapter Summary
  • References
  • 6. Cooperative Remote Sensing Using Multiple Unmanned Vehicles
  • 6.1. Consensus-Based Formation Control
  • 6.1.1. Consensus Algorithms
  • 6.1.2. Implementation of Consensus Algorithms
  • 6.1.3. MASnet Hardware Platform
  • 6.1.4. Experimental Results
  • 6.2. Surface Wind Profile Measurement Using Multiple UAVs
  • 6.2.1. Problem Definition: Wind Profile Measurement
  • 6.2.2. Wind Profile Measurement Using UAVs
  • 6.2.3. Wind Profile Measurement Using Multiple UAVs
  • 6.2.4. Preliminary Simulation and Experimental Results
  • 6.3. Chapter Summary
  • References
  • 7. Diffusion Control Using Mobile Sensor and Actuator Networks
  • 7.1. Motivation and Background
  • 7.2. Mathematical Modeling and Problem Formulation
  • 7.3. CVT-Based Dynamical Actuator Motion Scheduling Algorithm
  • 7.3.1. Motion Planning for Actuators with the First-Order Dynamics
  • 7.3.2. Motion Planning for Actuators with the Second-Order Dynamics
  • 7.3.3. Neutralizing Control
  • 7.4. Grouping Effect in CVT-Based Diffusion Control
  • 7.4.1. Grouping for CVT-Based Diffusion Control
  • 7.4.2. Diffusion Control Simulation with Different Group Sizes
  • 7.4.3. Grouping Effect Summary
  • 7.5. Information Consensus in CVT-Based Diffusion Control
  • 7.5.1. Basic Consensus Algorithm
  • 7.5.2. Requirements of Diffusion Control
  • 7.5.3. Consensus-Based CVT Algorithm
  • 7.6. Simulation Results
  • 7.7. Chapter Summary
  • References
  • 8. Conclusions and Future Research Suggestions
  • 8.1. Conclusions
  • 8.2. Future Research Suggestions
  • 8.2.1. VTOL UAS Design for Civilian Applications
  • 8.2.2. Monitoring and Control of Fast-Evolving Processes
  • 8.2.3. Other Future Research Suggestions
  • References
  • Appendix
  • A.1. List of Documents for CSOIS Flight Test Protocol
  • A.1.1. Sample CSOIS-OSAM Flight Test Request Form
  • A.1.2. Sample CSOIS-OSAM 48 in. UAV (IR) In-lab Inspection Form
  • A.1.3. Sample Preflight Checklist
  • A.2. IMU/GPS Serial Communication Protocols
  • A.2.1. u-blox GPS Serial Protocol
  • A.2.2. Crossbow MNAV IMU Serial Protocol
  • A.2.3. Microstrain GX2 IMU Serial Protocol
  • A.2.4. Xsens Mti-g IMU Serial Protocol
  • A.3. Paparazzi Autopilot Software Architecture: A Modification Guide
  • A.3.1. Autopilot Software Structure
  • A.3.2. Airborne C Files
  • A.3.3. OSAM-Paparazzi Interface Implementation
  • A.3.4. Configuration XML Files
  • A.3.5. Roll-Channel Fractional Order Controller Implementation
  • A.4. DiffMas2D Code Modification Guide
  • A.4.1. Files Description
  • A.4.2. Diffusion Animation Generation
  • A.4.3. Implementation of CVT-Consensus Algorithm
  • References.

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