计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 281-287.doi: 10.19678/j.issn.1000-3428.0049070

• 开发研究与工程应用 • 上一篇    下一篇

一种基于改进QPSO的机器人路径规划算法

胡章芳,孙林,张毅,鲍合章   

  1. 重庆邮电大学 信息无障碍工程与机器人技术研发中心,重庆 400065
  • 收稿日期:2017-10-25 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:胡章芳(1969—),女,副教授、硕士,主研方向为信息处理;孙林,硕士研究生;张毅,教授、博士生导师;鲍合章,硕士研究生。
  • 基金项目:

    国家自然科学基金(51604056);重庆市科学技术委员会项目(CSTC2015jcyjBX0066)。

A Robot Path Planning Algorithm Based on Improved QPSO

HU Zhangfang,SUN Lin,ZHANG Yi,BAO Hezhang   

  1. Research and Development Center of Information Accessibility Engineering and Robotics, Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2017-10-25 Online:2019-04-15 Published:2019-04-15

摘要:

针对量子行为粒子群优化(QPSO)算法在移动机器人路径规划中出现早熟收敛的问题,提出一种基于聚集度因子和阶段变异策略的改进QPSO算法。根据目标函数计算粒子的适应度值,在压缩扩张因子中引入改进聚集度因子划分搜索阶段,利用分阶段变异策略更新个体位置,并对算法进行性能测试。实验结果表明,与FE-PSO算法相比,该算法具有较高的收敛精度与较好的稳定性。

关键词: 路径规划, 群智能算法, 量子行为粒子群优化, 聚集度因子, 早熟收敛

Abstract:

Aiming at the problem of premature convergence in the path planning of mobile robot based on Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,an improved QPSO algorithm based on aggregation factor and phase mutation strategy is proposed.According to the objective function,the fitness value of the particle is calculated.By introducing the improved aggregation degree factor into the compression expansion factor,the search phase is divided,the individual position is updated by the phased mutation strategy,and the performance of the algorithm is tested.Experimental results show that compared with the FE-PSO algorithm,this algorithm has higher convergence accuracy and better stability.

Key words: path planning, swarm intelligence algorithms, Quantum-behaved Particle Swarm Optimization(QPSO), aggregation degree factor, premature convergence

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