文章编号：1004－5422(2021)04－0379－05

DOI:10.3969/j.issn.1004-5422.2021.04.008

基于改进智能算法的机器人路径规划问题研究

代婷婷

（昭通学院  数学与统计学院,云南  昭通  657000）

摘  要：为了探究更高效地解决机器人路径规划问题的方法，将相关文献中对基本蚁群算法的启发函数和信息素更新方式做了改进融合而得到了一种新的改进蚁群算法.为验证本文融合方法的良好的效果，利用MATLAB在20×20的栅格环境下，测试比较了基本蚁群算法、改进启发函数、改进信息素浓度的更新方式这3种算法和本文改进蚁群算法的性能，结果表明，改进蚁群算法与基本蚁群算法相比，虽然在相同的栅格环境中寻找的最优路径完全相同，但需要迭代次数.与另外两种算法相比，本算法在寻找最优解和收敛速度方面都有比较明显的提升.

关键词：蚁群算法；路径规划；启发函数；信息素更新；改进智能算法

中图分类号：TP242.6;TP18             文献标识码：A

Research on Robot Path Planning Based on  Improved Intelligent Algorithm

DAI  Tingting

（School of Mathematics and Statistics,Zhaotong University,Zhaotong 657000,China）

Abstract：In order to explore a more efficient way to solve the robot path planning problem,the heuristic function improvement of the basic ant colony algorithm and the improvement of the pheromone update method in the reference literature are combined to obtain a new improved ant colony algorithm.In order to verify the good effect of the fusion method in this paper,MATLAB was used in the grid environment to test and compare the performance of the basic ant colony algorithm,the improved heuristic function,the update method of the pheromone concentration,and the improved ant colony algorithm in this paper.The results show that when the improved ant colony algorithm in this paper is compared with the basic ant colony algorithm,although the optimal path is exactly the same in the same grid environment,the number of iterations is required.Compared with the other two algorithms,the algorithm in this paper shows a significant improvement in finding the optimal solution and convergence speed.

Key words：ant colony algorithm;path planning;heuristic function;pheromone update；improvement in smart algorithm