东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (7): 923-928.DOI: 10.12068/j.issn.1005-3026.2015.07.003

• 信息与控制 • 上一篇    下一篇

基于蚁群几何优化算法的全局路径规划

刘杰1， 闫清东1， 马越1， 唐正华2   

  1. (1.北京理工大学 机械与车辆学院， 北京100086； 2.装甲兵学院 模拟训练中心， 安徽 蚌埠233050)
  • 收稿日期:2014-06-27 修回日期:2014-06-27 出版日期:2015-07-15 发布日期:2015-07-15
  • 通讯作者: 刘杰
  • 作者简介:刘杰(1981-)，女，山东蓬莱人，北京理工大学博士研究生; 闫清东(1964-)，男，河南济源人，北京理工大学教授，博士生导师.
  • 基金资助:
    国防基础预研基金资助项目(k0904010502).

Global Path Planning Based on Improved Ant Colony Optimization Algorithm for Geometry

LIU Jie1， YAN Qing-dong1， MA Yue1， TANG Zheng-hua2   

  1. 1.School of Mechanical Engineering， Beijing Institute of Technology， Beijing 100081， China； 2.Simulation Training Center， Academy of Armored Forces， Bengbu 233050， China.
  • Received:2014-06-27 Revised:2014-06-27 Online:2015-07-15 Published:2015-07-15
  • Contact: LIU Jie
  • About author:-
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摘要： 将改进的蚁群算法与路径几何优化相结合，用于解决移动机器人的全局路径规划问题.算法结合机器人的越障性能对移动机器人的环境空间进行建模.通过设置初始信息素加快蚂蚁的搜索速度，同时设置自适应信息素挥发机制，解决特定地图中初始信息素的干扰问题；设置自适应路径长度，筛选规划路径的优劣；提出由路径优劣程度决定的信息素散播策略，并从几何原理出发，对规划路径进行优化处理，加快最优解的收敛速度.仿真结果验证了该算法的有效性和普遍应用性，在随机给定的环境地图中，该算法能够迅速规划出最优路径.

关键词: 栅格法, 路径规划, 蚁群算法, 几何优化, 移动机器人

Abstract: The improved ant colony algorithm and path geometry optimization were applied to solve the global path planning problem of mobile robot. The obstacle performance was combined in the proposed algorithm to establish the workspace model of the robot. By setting the initial pheromone， the ant searching speed was accelerated， and through the adaptive pheromone mechanism， the interference problem of initial pheromone to the specific map was solved. In addition， the pros and cons of the path planning were screened by setting the adaptive path length. It was also proposed that the pheromone spreading strategy was decided by the path length. Meanwhile， according to the principle of geometry， the planning path was optimized to accelerate the convergence speed of the optimal solution. The effectiveness and universal application of the proposed algorithm was demonstrated by the simulation results. In the random environment map， the optimal path could be rapidly obtained with the proposed algorithm.

Key words: grid method, path planning, ant colony algorithm, geometry optimization, mobile robot

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