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2020, Volume 21, Issue 5

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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 5 doi: 10.1631/FITEE.2000066

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

Affiliation(s): State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China; Peng Cheng Laboratory, Shenzhen 518000, China; less

Received: 2020-02-10 Accepted: 2020-05-18 Available online: 2020-05-18
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Abstract

We propose multi-objective social learning (MSLPIO) and apply it to for 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 and the improved non-dominated sorting genetic algorithm.

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