融合人工势场蚁群算法的移动机器人路径规划

通过对人工势场法与蚁群算法进行融合, 给出了一种融合人工势场蚁群算法的移动机器人路径规划算法。一方面, 引入目标点距离影响因子, 改善势场力对移动机器人路径搜索的影响, 通过改进斥力场函数, 避免移动机器人因受到较大的斥力而无法规划出最优路径; 另一方面, 构造势场力启发函数, 同时考虑距离启发信息和势场启发信息, 初始化信息素的差异化分配方式有利于提高算法的收敛速度。实验结果表明, 融合人工势场蚁群算法相比于文献［15］算法, 在最优路径长度、路径转折次数、收敛速度三方面分别提高了2.6%,25%和66.7%, 表明了该算法在路径规划方面的优越性。

Abstract

By combining the artificial potential field method with ant colony algorithm,a path planning method of mobile robot based on artificial potential field and ant colony algorithm is presented.On the one hand,the influence factor of target point distance is introduced to improve the influence of potential field force on mobile robot path search.By improving the repulsion field function,the mobile robot is prevented from being unable to plan the optimal path due to large repulsion.On the other hand,constructing the potential field force heuristic function,taking into account the distance heuristic information and the potential field heuristic information at the same time,initializing the differential allocation of pheromones is conducive to improving the convergence speed of the algorithm.The experimental results show that compared with that of the algorithm in Reference ［15］,the optimal path length,the number of path turns and the convergence speed of the proposed algorithm has been improved by 2.6%,25% and 66.7% respectively,which shows the superiority of the algorithm in path planning.

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李志锟, 赵倩楠. 融合人工势场蚁群算法的移动机器人路径规划[J]. 电光与控制, 2022, 29(11): 118. LI Zhikun, ZHAO Qiannan. Path Planning of Mobile Robot Based on Artificial Potential Field and Ant Colony Algorithm[J]. Electronics Optics & Control, 2022, 29(11): 118.

融合人工势场蚁群算法的移动机器人路径规划

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