基于PGWO算法的移动机器人路径规划
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作者单位:

1.上海电力大学自动化工程学院;2.上海合时智能科技有限公司

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中图分类号:

TP242

基金项目:

国家自然科学基金资助项目(52075316),上海市2021年度“科技创新行动计划”(21DZ1207502),国网浙江省电力有限公司杭州供电公司(5211HZ17000F)


Path Planning of Mobile Robot Based on PGWO Algorithm
Author:
Affiliation:

1.College of Automation Engineering, Shanghai University of Electric Power;2.Shanghai HRSTEK Co, Ltd

Fund Project:

Project 52075316 supported by National Natural Science Foundation of China,Shanghai 2021 “Science and Technology Innovation Action Plan”(21DZ1207502),This research work is supported by the science & technology project of State Grid Zhejiang Electric Power Corporation (5211HZ17000F)

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    摘要:

    针对传统灰狼优化算法(GWO)在移动机器人路径规划任务中经常遭遇局部最优的困境,并且收敛效率不尽人意,故提出一种基于Piecewise混沌映射的改进灰狼优化算法(PGWO)。PGWO算法首先采用Piecewise混沌映射初始化灰狼规模,提高种群分布的多样性;其次将GWO算法中收敛因子a由线性调整为非线性控制参数,调整后的收敛因子a在早期迭代中迅速减少,提高全局搜索能力,避免陷入局部最优,同时在后期迭代中逐渐减少,增加局部搜索能力;最后将GWO算法中灰狼趋于猎物的位置更新公式采用基于步长欧氏距离的比例权重进行更新,以提高灰狼独立搜索能力。为验证改进后算法性能,本文选取6个标准测试函数对PGWO算法与GWO算法,以及2个不同改进后的灰狼算法进行对比实验,结果表明PGWO算法有较好的收敛性和稳定性。将PGWO算法应用于3种不同复杂度的栅格地图中进行全局路径规划仿真对比实验,结果表明PGWO算法相较于GWO算法在20×20,30×30,50×50的栅格地图中,最短路径分别缩短了22.09%,34.12%,47.75%。

    Abstract:

    Aiming at the traditional Gray Wolf Optimization (GWO) algorithm which often encounters the dilemma of local optimum and unsatisfactory convergence efficiency in the mobile robot path planning task, an improved Gray Wolf Optimization (PGWO) algorithm based on Piecewise chaotic mapping is proposed. The PGWO algorithm first initializes the size of gray wolves by using the Piecewise chaotic map to improve the diversity of population distribution. Secondly, the convergence factor a in GWO algorithm is adjusted from linear to nonlinear control parameter, and the adjusted convergence factor a decreases rapidly in the early iteration, which improves the global search ability and avoids falling into local optimum, and gradually decreases in the later iteration to increase the local search ability. Finally, the formula for updating the position of gray wolf approaching prey in GWO algorithm is updated by proportional weight based on step Euclidean distance to improve the independent search ability of gray wolf. In order to verify the performance of the improved algorithm, this paper selects six standard test functions to compare PGWO algorithm with GWO algorithm and two different improved gray wolf algorithms. The results show that PGWO algorithm has good convergence and stability. In this paper, the PGWO algorithm is applied to three kinds of grid maps with different complexity for global path planning simulation and comparative experiments. The results show that the shortest path of PGWO algorithm is shortened by 22.09%, 34.12% and 47.75% respectively compared with GWO algorithm in 20×20, 30×30 and 50×50 grid maps.

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历史
  • 收稿日期:2024-05-30
  • 最后修改日期:2024-09-24
  • 录用日期:2024-09-25
  • 在线发布日期: 2024-10-16
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