基于改进蚁群算法的植保无人机路径规划
Path Planning of Plant Protection UAV Based on Improved Ant Colony Algorithm
刘璐 1沈小伟 1葛超 2王红1
作者信息
- 1. 华北理工大学电气工程学院,河北 唐山 063210
- 2. 华北理工大学电气工程学院,河北 唐山 063210;唐山市半导体集成电路重点实验室,河北 唐山 063000
- 折叠
摘要
针对传统蚁群算法在含障碍植保无人机路径规划中存在的易早熟、易陷入局部最优的问题,提出了一种改进蚁群算法.为降低算法陷入局部最优的概率,对启发因子进行了改进,在路径搜索的后阶段把利用贪心算法求得的从当前点到终点的距离作为新的启发因子.为提高算法的全局搜索能力,对挥发系数进行了自适应改进.采用最大最小蚁群策略对信息素浓度进行限制,避免算法早熟.仿真结果表明在复杂的作业环境下,改进蚁群算法的性能更好,具有更强的路径寻优能力.
Abstract
Aiming at the problems of ant colony algorithm in the path planning problem of plant protection UAV with multiple obstacles,such as premature maturity and easy fall into local optimum,the paper proposes an improved ant colony algorithm.First,in order to reduce the probability of the algorithm falling into the local optimum,the heu-ristic factor was improved.In the later stage of the path search,the distance from the current point to the end point ob-tained by the greedy algorithm was used as a new heuristic factor.Secondly,in order to improve the global search abil-ity of the algorithm,the volatility coefficient was adjusted adaptively.Finally,the maximum and minimum ant colony strategy was used to limit the concentration of pheromone to avoid the premature maturity of the algorithm.The simu-lation results show that the improved ant colony algorithm has better performance and stronger path optimization ability under complex operating environments.
关键词
植保无人机/路径规划/避障/改进蚁群算法Key words
Plant protection UAV/Path planning/Obstacle avoidance/Improved ant colony algorithm引用本文复制引用
基金项目
国家自然基金资助项目(61503120)
河北省自然科学基金(F202109006)
出版年
2024
NETL
NSTL
维普
万方数据