基于电场模型的无人机搜寻改进算法及仿真分析

doi:10.13229/j.cnki.jdxbgxb20210403

摘要/Abstract

摘要：

针对单一无人机视觉导航进行地面移动目标搜索的问题，提出了一种基于物理电场模型的改进蚁群算法。定义复杂搜索区域并进行网格划分，网格节点定义为可激活的信息素点，基于物理电场模型规则优化蚁群算法，引入概率模型，建立基于电场模型的无人机搜索改进粒子群算法控制无人机位姿和速度。仿真实验结果表明：改进蚁群算法搜索移动目标的平均成功率为67.3%，算例的平均计算时间为8.33 s，均优于粒子群算法，为单一无人机在区域快速跟踪搜索提供了一种简单、高效的方法。

关键词: 自动控制技术, 无人机搜寻, 物理电场模型, 蚁群算法, 网格法

Abstract:

An improved ant colony algorithm based on physical electric field model was proposed in order to solve the problem of a single UAV visual navigation for ground moving target search. The complex search area was defined， the grid was divided， and the grid node was defined as an activable pheromone point. The ant colony algorithm was optimized based on the potential field rules of the physical electric field model， and the probability model was introduced. An improved particle swarm optimization algorithm for UAV search based on electric field model was established to control the pose and speed of UAV. The simulation results show that the average success rate of the improved ant colony algorithm for searching moving targets is 67.3%， and the average operation time of the example is 8.33 s. The simulation results show that the improved ant colony algorithm has higher success rate and less time-consuming than the particle swarm optimization algorithm.

Key words: automatic control technology, unmanned aerial vehicle （UAV） search, physical electric field model, ant colony algorithm, grid method

中图分类号:

V249

朱航,于瀚博,梁佳辉,李宏泽. 基于电场模型的无人机搜寻改进算法及仿真分析[J]. 吉林大学学报(工学版), 2022, 52(12): 3029-3038.

Hang ZHU,Han-bo YU,Jia-hui LIANG,Hong-ze LI. Improved algorithm of UAV search based on electric field model and simulation analysis[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 3029-3038.

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