Full Text:   <142>

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CLC number: TP242.6; V279

On-line Access: 2020-05-18

Received: 2020-02-10

Revision Accepted: 2020-03-30

Crosschecked: 2020-04-13

Cited: 0

Clicked: 241

Citations:  Bibtex RefMan EndNote GB/T7714


Wan-ying Ruan


Hai-bin Duan


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.5 P.740-748


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

Author(s):  Wan-ying Ruan, Hai-bin Duan

Affiliation(s):  State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China; more

Corresponding email(s):   wyruan@buaa.edu.cn, hbduan@buaa.edu.cn

Key Words:  Unmanned aerial vehicle (UAV), Obstacle avoidance, Pigeon-inspired optimization, Multi-objective social learning pigeon-inspired optimization (MSLPIO)

Wan-ying Ruan, Hai-bin Duan. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 740-748.

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%T Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization
%A Wan-ying Ruan
%A Hai-bin Duan
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T1 - Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization
A1 - Wan-ying Ruan
A1 - Hai-bin Duan
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.2000066

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.





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