首页|基于蚁群算法的物资运送小车路径规划研究

基于蚁群算法的物资运送小车路径规划研究

扫码查看
原文链接
  • 万方数据
  • 维普
针对路径规划蚁群算法的盲目性、收敛速度慢、路径较长和路径折点多等问题，提出了一种改进蚁群路径规划算法。首先通过改进启发信息的数学模型，限制轮盘赌在8个方向的选择概率，降低迭代次数;然后建立自适应更新影响因子，通过实时监测目标点位置，进一步提高路径的选择方式和算法的鲁棒性;最后通过路径二次寻优，对改进蚁群路径规划算法形成的最优路径进一步消除冗余节点，在已知最优路径进一步寻优，从而提高路径平滑度、减少路径折点，以及缩短路径长度，提高物资运送小车的使用效率。通过栅格环境地图中障碍物不同占比的仿真试验，验证了所提出的改进蚁群算法的迭代速率更快、寻优能力更强、鲁棒性更好和路径更短。
Research on route planning of material transport vehicle based on ant colony algorithm
An improved ant colony path planning algorithm was proposed to solve the problems such as blindness,slow conver-gence,long path and many break points.Firstly,by improving the mathematical model of heuristic factor,the selection probability of roulette in eight directions is limited,so as to reduce the number of iterations.Then,the adaptive updating influence factor is es-tablished to further improve the routing method and the robustness of the algorithm by monitoring the location of target points in real time.Finally,through the path secondary optimization,the shortest path formed by the ant colony algorithm is further elimina-ted redundant nodes,and the shortest path is further optimized,so as to smooth path,reduce the break point of the path,reduce the path length,and improve the efficiency of the material transport trolley.Through the simulation experiment of different propor-tions of obstacles in the grid environment map,it is verified that the proposed improved ant colony algorithm has faster iteration rate,stronger searching ability,better robustness and shorter path.

path planningheuristic factormonitoringsecondary optimizationsmooth path

唐宏伟、高方坤、邓嘉鑫、丁祥、罗佳强、王军权

展开 >

邵阳学院机械与能源工程学院多电源地区电网运行与控制湖南省重点实验室,邵阳 422000

路径规划 启发因子 监测 二次寻优 平滑路径

湖南省自科基金项目湖南省教育厅科研项目湖南省教育厅科研项目湖南省科技计划项目国家级大学生创新创业训练计划项目湖南省研究生科研创新项目邵阳学院研究生科研创新项目邵阳学院研究生科研创新项目

2022JJ5020521B068221B06762016TP1023202210547018CX20221314CX2022SY005CX2022SY023

2024

10.16731/j.cnki.1671-3133.2024.02.004

现代制造工程
北京机械工程学会 北京市机械工业局技术开发研究所

现代制造工程

CSTPCD北大核心
影响因子：0.374
ISSN：1671-3133
年,卷(期)：2024.(2)
  • 1
  • 13