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Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization |
Wan-ying RUAN1( ), Hai-bin DUAN1,2( ) |
1. State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China 2. Peng Cheng Laboratory, Shenzhen 518000, China |
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Abstract 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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Keywords
Unmanned aerial vehicle (UAV)
Obstacle avoidance
Pigeon-inspired optimization
Multi-objective social learning pigeon-inspired optimization (MSLPIO)
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Corresponding Author(s):
Wan-ying RUAN,Hai-bin DUAN
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Issue Date: 17 June 2020
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[1] |
FITEE-0740-20007-WYR_suppl_1
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[2] |
FITEE-0740-20007-WYR_suppl_2
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