文章摘要
基于势场蚁群法的机器人全局路径规划
Global Path Planning of Robots Based on Potential Field Ant Colony Method
投稿时间:2019-01-28  修订日期:2019-02-18
DOI:
中文关键词: 蚁群算法,人工势场,路径规划,势场蚁群法,信息素启发因子
英文关键词: ant colony optimization algorithm  artificial potential field  path planning  potential ant colony method  pheromone heuristic factor
基金项目:国家自然科学基金资助项目(61203082),辽宁省自然科学基金资助项目(20180520036),中央高校基本科研业务费专项基金资助项目(3132016311)
作者单位E-mail
陈余庆 大连海事大学 cyqb@163.com 
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中文摘要:
      本文研究了智能移动机器人的全局路径规划算法改进问题. 结合蚁群算法的全局性与人工势场的确定性优势,提出一种分段式势场蚁群算法. 即在蚁群算法迭代初期,通过人工势场法影响蚂蚁的信息素矩阵,从而提升寻找最优路径的效率. 分段式势场蚁群算法基于栅格环境模型,设计了算法的执行步骤. 此外, 本文分析了不同的信息素启发因子和信息素挥发系数对算法路径长度、迭代次数和收敛速度的影响. 最后仿真验证了本文算法优于基本蚁群算法, 也得出了信息素启发因子参数选择的合理范围.
英文摘要:
      This paper studies the improvement of global path planning algorithm for intelligent mobile robots. Combining the global character of ant colony algorithm and the deterministic advantage of artificial potential field, a piecewise potential field ant colony algorithm is proposed. In the initial iteration stage of ant colony algorithm, artificial potential field method is considered in the construction of the pheromone matrix, so as to improve the efficiency of finding the optimal path. Based on the grid model, the implementation steps of the algorithm are designed. In addition, the effects of different pheromone heuristic factors and pheromone volatilization coefficients on the path length, iteration times and convergence speed of the algorithm are analyzed. Finally, the simulation results show that the algorithm is superior to the basic ant colony algorithm, and the reasonable range of pheromone heuristic factor parameter selection is obtained.
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