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

Wan-ying RUAN; Hai-bin DUAN

doi:10.1631/FITEE.2000066

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

Special Issue on Intelligent Unmanned Systems | Updated：2022-05-19

- Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization
- 基于多目标社会学习鸽群优化的多无人机避障控制
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 5, Pages: 740-748(2020)
- Affiliations：
  
  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
- Author bio：
  
  [ "Wan-ying RUAN, E-mail: wyruan@buaa.edu.cn" ]
  Hai-bin DUAN, E-mail: hbduan@buaa.edu.cn
- Funds：
- DOI：10.1631/FITEE.2000066
  CLC： TP242.6;V279
- Received：10 February 2020，
  
  Revised：2020-;4-13，
  
  Published：2020-05
- Accepted：
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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.
DOI：

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. DOI： 10.1631/FITEE.2000066.

摘要

提出多目标社会学习鸽群优化（MSLPIO）方法，将其应用于无人机编队避障控制。该算法特点在于，每只鸽子在更新过程中并非向全局最优的鸽子学习，而是学习比自己占优的任何鸽子。在地图指南针算子和地标算子中引入社会学习因子。此外，为避免参数设置的盲目性，采用维数相关的参数设置方法。本文模拟了5架飞机在复杂障碍环境下的飞行过程，实验结果验证了该方法的有效性。与改进的多目标鸽群优化算法和改进的非占优排序遗传算法相比，MSLPIO具有更好的收敛性。

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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references

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