内容简介：　　　

　　Bio-inspired Computation in Unmanned Aerial Vehicles focuses on the aspects of path planning, formation control, heterogeneous cooperative control and vision-based surveillance and navigation in Unmanned Aerial Vehicles (UAVs) from the perspective of bio-inspired computation. It helps readers to gain a comprehensive understanding of control-related problems in UAVs, presenting the latest advances in bio-inspired computation.　

　　By combining bio-inspired computation and UAV control problems, key questions are explored in depth, and each piece is content-rich while remaining accessible. With abundant illustrations of simulation work, this book links theory, algorithms and implementation procedures, demonstrating the simulation results with graphics that are intuitive without sacrificing academic rigor. Further, it pays due attention to both the conceptual framework and the implementation procedures.　

　　The book offers a valuable resource for scientists, researchers and graduate students in the field of Control, Aerospace Technology and Astronautics, especially those interested in artificial intelligence and Unmanned Aerial Vehicles.

英文目录：
1 Introduction
　　1.1 Unmanned Aerial Vehicle (UAV)
　　　　1.1.1 History of UAVs
　　　　1.1.2 Unmanned Aircraft System
　　　　1.1.3 Autonomy: A Key Enabler
　　1.2 Bio-inspired Computation
　　　　1.2.1 Definition of Swarm
　　　　1.2.2 General Features of Bio-inspired Computation
　　　　1.2.3 Bio-inspired Computation Algorithms
　　1.3 Bio-inspired Intelligence in UAVs
　　　　1.3.1 Achieve Higher Autonomous Capability
　　　　1.3.2 Enhance Robustness and Flexibility
　　　　1.3.3 Cooperative Control of Multiple UAVs
　　　　1.3.4 Cooperative Control of Heterogeneous Vehicle Groups
　　1.4 Outline of the Monograph
　　References
2 Bio-inspired Computation Algorithms
　　2.1 Introduction
　　2.2 Ant Colony Optimization
　　　　2.2.1 Biological Inspiration
　　　　2.2.2 Principle of Ant Colony Optimization
　　　　2.2.3 Ant System and Its Extensions
　　2.3 Particle Swarm Optimization
　　　　2.3.1 Biological Inspiration
　　　　2.3.2 Principle of Particle Swarm Optimization
　　　　2.3.3 Parameters and Population Topology
　　2.4 Artificial Bee Colony
　　　　2.4.1 Biological Inspiration
　　　　2.4.2 Principle of Artificial Bee Colony
　　　　2.4.3 Algorithmic Structure of Artificial Bee Colony
　　2.5 Differential Evolution
　　　　2.5.1 Biological Inspiration
　　　　2.5.2 Principle of Differential Evolution
　　　　2.5.3 Control Parameters of Differential Evolution
　　2.6 Other Algorithms
　　　　2.6.1 Glowworm Swarm Optimization
　　　　2.6.2 Bacteria Foraging Optimization
　　　　2.6.3 Bat-Inspired Algorithm
　　2.7 Conclusions
　　References
3 UAV Modeling and Controller Design
　　3.1 Introduction
　　3.2 Parameter Identification for UAVs Based on Predator–Prey PSO
　　　　3.2.1 Mathematical Model of UAVs
　　　　3.2.2 Predator–Prey PSO for Parameter Identification
　　　　3.2.3 Experiments
　　3.3 PSO Optimized Controller for Unmanned Rotorcraft Pendulum
　　　　3.3.1 Mathematical Model of Pendulum Oscillation for MAVs
　　　　3.3.2 Oscillation Controller Design Based on LQR and PSO
　　　　3.3.3 Experiments
　　3.4 Conclusions
　　References
4 UAV Path Planning
　　4.1 Introduction
　　　　4.1.1 Characteristic of Path Planning for UAVs
　　　　4.1.2 Main Features of Path Replanning for Multiple UAVs
　　4.2 Modeling for Path Planning
　　　　4.2.1 Environment Representation
　　　　4.2.2 Evaluation Function
　　4.3 Chaotic ABC Approach to UAV Path Planning
　　　　4.3.1 Brief Introduction to Chaos Theory
　　　　4.3.2 Procedures of Path Planning Using Chaotic ABC Approach
　　　　4.3.3 Experiments
　　4.4 Hybrid ACO-DE Approach to Three-Dimensional Path Planning for UAVs
　　　　4.4.1 Hybrid Meta-heuristic ACO-DE Algorithm
　　　　4.4.2 Procedures of Three-Dimensional Path Planning Using Hybrid ACO-DE
　　　　4.4.3 Path-Smoothing Strategies
　　　　4.4.4 Experiments
　　4.5 Coordinated Path Replanning for Multiple UAVs Using Max–Min Adaptive ACO
　　　　4.5.1 Model of Multiple UAV Coordinated Path Replanning
　　　　4.5.2 Coordination Mechanism of Multiple UAV Path Replanning
　　　　4.5.3 Procedures of Multiple UAV Coordinated Path Replanning
　　　　4.5.4 Experiments
　　4.6 Conclusions
　　References
5 Multiple UAV Formation Control
　　5.1 Introduction
　　　　5.1.1 Formation Control
　　　　5.1.2 Close Formation
　　　　5.1.3 Formation Configuration
　　5.2 Dual-Mode RHC for Multiple UAV Formation Flight Based on Chaotic PSO
　　　　5.2.1 Leader-Following Formation Model
　　　　5.2.2 Principle of RHC
　　　　5.2.3 Chaotic PSO-Based Dual-Mode RHC Formation Controller Design
　　　　5.2.4 Experiments
　　5.3 DE-Based RHC Controller for Multiple UAV Close Formation
　　　　5.3.1 Model of Multiple UAVs for Close Formation
　　　　5.3.2 Description of RHC-Based Multiple UAV Close Formation
　　　　5.3.3 DE-Based RHC Controller Design for Close Formation
　　　　5.3.4 Experiments
　　5.4 DE-Based RHC Controller for Multiple UAV Formation Reconfiguration
　　　　5.4.1 Model of Multiple UAVs for Formation Configuration
　　　　5.4.2 Description of RHC-Based Multiple UAV Formation Reconfiguration
　　　　5.4.3 DE-Based RHC Controller Design for Formation Reconfiguration
　　　　5.4.4 Experiments
　　5.5 Conclusions
　　References
6 Multiple UAV/UGV Heterogeneous Control
　　6.1 Introduction
　　6.2 Multiple UAV/UGV Heterogeneous Coordinated Control
　　　　6.2.1 Mathematical Model for UAVs and UGVs
　　　　6.2.2 Multiple UGV Coordinated Control Based on RHC
　　　　6.2.3 Multiple UAV Coordinated Control Based on Velocity Vector Control
　　　　6.2.4 Multiple UAV/UGV Heterogeneous Cooperation
　　　　6.2.5 Time-Delay Compensation of Heterogeneous Network Control
　　6.3 DE-Based RHC for Multiple UAV Cooperative Search
　　　　6.3.1 Model Description for Cooperative Search
　　　　6.3.2 DE-Based RHC for Cooperative Area Search
　　　　6.3.3 Experiments
　　6.4 Conclusions
　　References
7 Biological Vision-Based Surveillance and Navigation
　　7.1 Introduction
　　7.2 ABC Optimized Edge Potential Function Approach to Target Identification
　　　　7.2.1 The Principle of Edge Potential Function
　　　　7.2.2 ABC Optimized EPF Approach to Target Identification
　　　　7.2.3 Experiments
　　7.3 A Chaotic Quantum-Behaved PSO Based on Lateral Inhibition for Image Matching
　　　　7.3.1 The Quantum-Behaved PSO Algorithm
　　　　7.3.2 Lateral Inhibition Mechanism
　　　　7.3.3 Chaotic Quantum-Behaved PSO Based on Lateral Inhibition
　　　　7.3.4 Experiments
　　7.4 Implementation of Autonomous Visual Tracking and Landing for Low-Cost Quadrotor
　　　　7.4.1 The Quadrotor and Carrier Test Bed
　　　　7.4.2 Computer Vision Algorithm
　　　　7.4.3 Control Architecture for Tracking and Landing
　　　　7.4.4 Experiments
　　7.5 Conclusions
　　References
8 Conclusions and Outlook
　　8.1 Conclusions
　　8.2 New Trends in UAV Development
　　　　8.2.1 Small Air Vehicles
　　　　8.2.2 Air-Breathing Hyposonic Vehicles
　　　　8.2.3 Design from the Perspective of System Integration
　　8.3 Further Extensions of Bio-inspired Intelligence in UAVs
　　　　8.3.1 Achieve Higher Autonomous Capability
　　　　8.3.2 Enhance the Ability to Understand and Adaptto the Environment
　　　　8.3.3 Cooperative Control of Multiple Autonomous Vehicles
　　References
Author Biography