Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization

We propose multi-objective social learning pigeon-inspired optimization (MSLPIO) and apply it to obstacle avoidance for unmanned aerial vehicle (UAV) formation. In the algorithm, each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learnin...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2020-05, Vol.21 (5), p.740-748
Main Authors: Ruan, Wan-ying, Duan, Hai-bin
Format: Article
Language:English
Subjects:
Artificial intelligence
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Control algorithms
Electrical Engineering
Electronics and Microelectronics
Genetic algorithms
Instrumentation
Learning
Multiple objective analysis
Networks
Obstacle avoidance
Optimization
Parameters
Pigeons
Sorting algorithms
Unmanned aerial vehicles
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Summary:We propose multi-objective social learning pigeon-inspired optimization (MSLPIO) and apply it to obstacle avoidance for unmanned aerial vehicle (UAV) formation. In the algorithm, each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learning factor is added to the map and compass operator and the landmark operator. In addition, a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting. We simulate the flight process of five UAVs in a complex obstacle environment. Results verify the effectiveness of the proposed method. MSLPIO has better convergence performance compared with the improved multi-objective pigeon-inspired optimization and the improved non-dominated sorting genetic algorithm.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.2000066