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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Hai-bin DUAN and Wan-ying RUAN designed the research. Wan-ying RUAN processed the data and drafted the manuscript. Hai-bin DUAN helped organize and check the manuscript. Wan-ying RUAN revised and finalized the paper.
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Wan-ying RUAN and Hai-bin DUAN declare that they have no conflict of interest.
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Project supported by the Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence,” China (No. 018AAA0102303), the National Natural Science Foundation of China (Nos. 91948204, 91648205, U1913602, and U19B2033), and the Aeronautical Foundation of China (No. 20185851022)
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Ruan, Wy., Duan, Hb. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization. Front Inform Technol Electron Eng 21, 740–748 (2020). https://doi.org/10.1631/FITEE.2000066
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DOI: https://doi.org/10.1631/FITEE.2000066
Key words
- Unmanned aerial vehicle (UAV)
- Obstacle avoidance
- Pigeon-inspired optimization
- Multi-objective social learning pigeon-inspired optimization (MSLPIO)