量子粒子群优化算法在移动机器人路径规划中的应用

曲靖师范学院学报 ›› 2022, Vol. 41 ›› Issue (6): 43-48.

量子粒子群优化算法在移动机器人路径规划中的应用

杨静

安徽文达信息工程学院艺术设计学院,安徽合肥 230032

收稿日期:2022-04-01 出版日期:2022-11-26 发布日期:2022-12-14
作者简介:杨静,安徽文达信息工程学院艺术设计学院讲师,主要从事计算机、数据库研究.
基金资助:
安徽省高校优秀拔尖人才培育项目“大数据环境下在线考试系统开发的拓展性研究”(gxyq2021240).

Research on Application of Quantum Particle Swarm Optimization Algorithm to Mobile Robot Path Planning

YANG Jing

School of Art and Design,Anhui Wenda Information Engineering College, Hefei Anhui 230032, China

Received:2022-04-01 Published:2022-11-26 Online:2022-12-14

摘要/Abstract

摘要： 为解决优化移动机器人在解空间盲目搜索的问题,对粒子群算法进行改进,提出一种基于量子粒子群优化(Quantum particle swarm optimization,QPSO)算法的移动机器人路径规划方法.针对粒子群中个体粒子收敛速度与群体离散度进行优化,以完成对惯性权重的动态调节,以解决传统粒子群算法存在过早收敛的缺点,可使 QPSO算法的权重具有可控性与自适应性.同时使用自然选择方法进行粒子群位置更新,可增加粒子群运动路径的多样性,可以极大提高粒子群的全局搜素能力和收敛速度.最后,通过路径规划软件仿真与实验测试结果分析可知:所提算法在移动机器人路径规划中的全局搜索能力与收敛速度上均优于PSO算法,其中,平均代价降低了约0.2 m,平均耗时降低了1.28 s.这验证了所提算法的有效性与可行性.

关键词: 位置更新, 量子粒子群优化算法, 最优路径, 全局搜索

Abstract: A mobile robot path planning method based on Quantum particle swarm optimization (QPSO) algorithm is proposed to solve the problem of optimizing the blind search of mobile robots in the solution space, by improving the particle swarm algorithm. Optimizing the convergence speed and population dispersion of individual particles in the particle swarm to complete the dynamic adjustment of the inertia weight to solve the shortcomings of the traditional particle swarm algorithm of premature convergence can make the weight of the QPSO algorithm controllable and adaptive sex. At the same time, the natural selection method is used to update the position of the particle swarm, increasing the diversity of the particle swarm's motion path, and greatly improving the global search ability and convergence speed of the particle swarm. Finally, through the analysis of path planning software simulation and experimental test results, it can be seen that the algorithm is better than the PSO algorithm in the global search ability and convergence speed in the path planning of mobile robots.Among them, the average cost is reduced by about 0.2m and the average time consumption is reduced by 1.28s. This verifies the effectiveness and feasibility of the proposed algorithm.

Key words: position update, Quantum particle swarm optimization algorithm, optimal path, global search

中图分类号:

TP242

杨静. 量子粒子群优化算法在移动机器人路径规划中的应用[J]. 曲靖师范学院学报, 2022, 41(6): 43-48.

YANG Jing. Research on Application of Quantum Particle Swarm Optimization Algorithm to Mobile Robot Path Planning[J]. Journal of Qujing Normal University, 2022, 41(6): 43-48.

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