仓库巡检机器人路径优化方法研究

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Artificial Intelligence Pub Date : 2023-09-14 DOI:10.1080/08839514.2023.2254048

Jianxian Liu, Hongyuan Liu

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引用次数: 0

摘要

随着智能化的日益普及，很多企业的仓库检验工作都是通过机器人来完成的。然而，由于仓库巡检的目标点多，智能机器人巡检路径规划效率低是一个亟待解决的问题。为了解决上述问题，本文提出了一种基于混合粒子群优化(HPSO)的HPSO-ACO算法，对蚁群优化(ACO)算法的参数进行优化，建立了智能巡检机器人在仓库管理中的路径优化模型。实验结果表明，与HPSO算法和蚁群算法相比，所提方法在相同条件下具有更快的收敛速度、更少的迭代次数和更短的最优路径，为巡检机器人路径优化提供了理论参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Research on Path Optimization Method for Warehouse Inspection Robot

With the increasing popularity of intelligence, many enterprises’ warehouse inspection work is completed through robots. However, due to the multiple target points of warehouse inspection, the low efficiency of planning intelligent robot inspection paths is a problem that needs to be solved. In order to solve the above problems, this paper proposes an HPSO-ACO algorithm based on hybrid particle swarm optimization (HPSO) to optimize the parameters of the ant colony optimization (ACO) algorithm, and establishes a path optimization model for intelligent inspection robots in warehouse management. Compared with HPSO algorithm and ACO algorithm, the experimental results show that the proposed method has faster convergence speed, fewer iterations, and shorter optimal path under the same conditions, which provides a theoretical reference for path optimization for inspection robot.

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来源期刊

Applied Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

5.20

自引率

3.60%

发文量

106

审稿时长

6 months

期刊介绍： Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.