基于势场蚁群算法的机器人全局路径规划

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陈余庆,李桐训,于双和,沈智鹏.基于势场蚁群算法的机器人全局路径规划[J].,2019,59(3):316-322

基于势场蚁群算法的机器人全局路径规划

Global path planning of robots based on potential field ant colony algorithm

DOI：10.7511/dllgxb201903014

中文关键词:基本蚁群算法人工势场路径规划势场蚁群算法信息素启发因子信息素挥发系数

英文关键词:basic ant colony algorithm artificial potential field path planning potential field ant colony algorithm pheromone heuristic factor pheromone volatilization coefficient

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资金资助项目(3132016311).

作者	单位
陈余庆,李桐训,于双和,沈智鹏

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中文摘要:

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，提出一种势场蚁群算法．即在基本蚁群算法迭代初期，通过人工势场法影响蚂蚁的信息素量，从而提升寻找最优路径的效率．基于栅格模型，设计了算法的执行步骤．此外，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．最后仿真验证了该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:

The improvement of global path planning algorithm for intelligent mobile robots is studied. Combining the global character of basic ant colony algorithm and the deterministic advantage of artificial potential field, a potential field ant colony algorithm is proposed. In the initial iteration stage of basic ant colony algorithm, artificial potential field method is considered in the construction of the pheromone, 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 is obtained.

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基于势场蚁群算法的机器人全局路径规划

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