基于人工势场法的机器人路径规划研究

doi:10.3969/j.issn.2095-2198.2024.02.013

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摘要

针对人工势场法在机器人路径规划中存在的目标不可达、搜索效率低等问题，提出了一种新的改进人工势场优化算法.为了确保在真实环境中足以实现，此算法没有计算环境中所有的障碍物，而只在障碍物表面进行近距离的感知测量，再通过传感器每个固定角度检测出一定范围内存在的障碍.该算法不仅在传统人工势场法基础上提出了新的斥力与引力函数，还对势场法的计算原理进行了改进，使其在不增加计算量的情况下解决了目标不可达的问题.实验结果充分验证了改进人工势场算法能够解决机器人路径规划中目标不可达的问题，体现了该算法相较于传统人工势场法更高的效率和优越性.

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关键词: 路径规划')" href="#">

路径规划人工势场法移动机器人感知测量法

Abstract:

A new improved artificial potential field optimization algorithm is proposed to solve the problems of inaccessibility and low search efficiency in robot path planning.In order to ensure sufficient implementation in the real environment,this algorithm does not calculate all obstacles in the environment,but only carries out a close sensing measurement on the surface of the obstacles,and then detects the obstacles in a certain range through each fixed angle of the sensor.The algorithm not only proposes new repulsion and gravity functions based on the traditional artificial potential field method,but also improves the calculation principle of the potential field method to solve the problem that the target cannot be reached without increasing the amount of calculation.The experimental results fully verify that the improved artificial potential field algorithm can solve the problem that the target is not reachable in robot path planning,which shows that the algorithm is more efficient and superior than the traditional artificial potential field method.

Key words: path planning')" href="#">

path planning artificial potential field method mobile robot sensing measurement method

出版日期: 2024-04-30 发布日期: 2025-01-02 整期出版日期: 2024-04-30

通讯作者: 符秀辉

作者简介: 李朵朵（1992—），男，河南周口人，硕士研究生在读，主要从事机器人控制的研究.

引用本文:

李朵朵1, 符秀辉1, 王美婷2.

基于人工势场法的机器人路径规划研究 [J]. 沈阳化工大学学报, 2024, 38(2): 173-177.
LI Duoduo1, FU Xiuhui1, WANG Meiting2.

Research on Robot Path Planning Based on Artificial Potential Field Method . Journal of Shenyang University of Chemical Technology, 2024, 38(2): 173-177.

链接本文:

https://xuebao.syuct.edu.cn/CN/10.3969/j.issn.2095-2198.2024.02.013 或 https://xuebao.syuct.edu.cn/CN/Y2024/V38/I2/173

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