计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 241100179-6.doi: 10.11896/jsjkx.241100179

• 人工智能 • 上一篇    下一篇

基于蚁群混合势场法的无人机路径规划

余浩楠1, 席万强2, 齐飞3   

  1. 1 南京信息工程大学 南京 210000
    2 无锡学院 江苏 无锡 214000
    3 常州大学 江苏 常州 213164
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 余浩楠(2220538617@qq.com)

UAV Path Planning Method Based on Ant Colony Mixed Potential Field Method

YU Haonan1, XI Wanqiang2, QI Fei3   

  1. 1 Nanjing University of Information Science and Technology,Nanjing 210000,China
    2 Wuxi University,Wuxi,Jiangsu 214000,China3 Changzhou University,Changzhou,Jiangsu 213164,China
  • Online:2025-11-15 Published:2025-11-10

摘要: 针对无人机路径规划过程中,因无人机运动速度过快,且动态避障算法需要大量计算,而难以进行高效动态避障的问题,提出了一种基于蚁群混合势场法的路径规划。首先,将路径规划分为全局规划和局部规划两个阶段,全局规划采用优化蚁群算法,局部规划采用动态改进势场法进行优化。其次,通过改进启发式函数优化蚁群算法,并添加安全性规则提高其运算速度。接着,针对势场法中的死点问题进行优化并添加速度势场对动态势场法进行改进,使得其动态目标的分析能力明显提高。最后,在仿真中对比了所提算法与传统势场法和经典优化势场法在不同场景下的性能,结果表明,所提算法在避障成功率和路径距离方面均表现优异。

关键词: 无人机, 路径规划, 人工势场, 蚁群算法, 动态避障

Abstract: This paper proposes a path planning method based on ant colony hybrid potential field method for the path problem of UAV in motion.This method divides path planning into global path and local path,uses the improved ant colony optimization algorithm to plan the global path,and adds the dynamic improves potential field method for local path optimization.The improved ant colony algorithm improves the rapidity and safety by improving the heuristic function and safety rule,and the dynamic improved potential field algorithm improves the analysis ability of dynamic targets by adding the velocity potential field.Finally,the performance of the proposed algorithm,the traditional potential field method and the current classical optimization potential field method in different scenarios is compared in the simulation.The results show that the proposed algorithm performs well in the success rate of obstacle avoidance and the path length.

Key words: UAV, Path planning, Artificial potential field, Ant colony algorithm, Dynamic obstacle avoidance

中图分类号: 

  • V279
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