电子科技 ›› 2019, Vol. 32 ›› Issue (9): 5-9.doi: 10.16180/j.cnki.issn1007-7820.2019.09.002

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基于改进蚁群算法的移动机器人路径规划研究

刘学芳,曾国辉,黄勃,鲁敦科   

  1. 上海工程技术大学 电子电气工程学院,上海 201620
  • 收稿日期:2018-08-30 出版日期:2019-09-15 发布日期:2019-09-19
  • 作者简介:刘学芳(1992-),女,硕士研究生。研究方向:机器人技术,自动化控制。|曾国辉(1975-),男,博士,副教授。研究方向:机器人技术、电力电子系统及其控制电机拖动及控制、电能质量监测与控制。
  • 基金资助:
    国家自然科学基金(11704243);国家自然科学基金(61603242)

Research on Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

LIU Xuefang,ZENG Guohui,HUANG Bo,LU Dunke   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2018-08-30 Online:2019-09-15 Published:2019-09-19
  • Supported by:
    National Natural Science Foundation of China(11704243);National Natural Science Foundation of China(61603242)

摘要:

为解决传统蚁群算法收敛速度慢、极易陷入局部最优解的问题,文中提出了一种改进蚁群算法,并将其应用于移动机器人路径规划问题。蚁群算法的路径规划采用栅格法建立环境模型,并对障碍物进行扩大处理,从而有效降低了移动机器人在运动过程中与障碍物相碰撞的可能性;构造启发函数以降低蚁群搜索路径的长度;引入信息素扩散算法,并提高算法在初期的全局搜索能力,从而加快了算法的后期收敛速度。仿真结果表明,所提出的算法在收敛速度上比传统蚁群算法提高近一倍,可以规划出最优路径。

关键词: 栅格地图, 蚁群算法, 信息素扩散, 启发函数, 路径规划, 移动机器人

Abstract:

In order to solve the problem that the traditional Ant Colony (AC) algorithm had a slow convergence speed and was easy to fall into a local optimal solution, an improved AC algorithm was proposed and applied to the path planning problem of mobile robots. This method built the environment using the grid model, and the obstacles were expanded to effectively reduce the possibility of the moving mobile robot colliding with obstacles. By constructing the heuristic function, the length of the ant colony search path was reduced. The pheromone diffusion algorithm was used to improve the global search ability of the algorithm in the initial stage, so as to accelerate the late convergence speed of the algorithm. The simulation results showed that the proposed improved AC algorithm could plan the optimal path. Besides, compared to the traditional method, the proposed improved AC algorithm doubled convergence speed.

Key words: grid map, ant colony algorithm, pheromone diffusion, heuristic function, path planning, move robot

中图分类号: 

  • TP242.6