Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

IF 4.2 2区 计算机科学 Q2 ROBOTICS
Zhuozhen Tang, Bin Xue, Hongzhong Ma, Ahmad Rad
{"title":"Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots","authors":"Zhuozhen Tang,&nbsp;Bin Xue,&nbsp;Hongzhong Ma,&nbsp;Ahmad Rad","doi":"10.1002/rob.22332","DOIUrl":null,"url":null,"abstract":"<p>In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm-feedforward neural network, enhanced unified algorithm-susceptible-infected-removed, and bounded behavior-particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 5","pages":"1426-1437"},"PeriodicalIF":4.2000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Field Robotics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rob.22332","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm-feedforward neural network, enhanced unified algorithm-susceptible-infected-removed, and bounded behavior-particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

变电站巡检机器人优化路径规划中 PID 控制器和增强型红鹿算法的实施
在当代输电系统中,变电站监控是一项至关重要但又极具挑战性的任务。虽然机器人技术在这方面大有可为,但其潜力仍处于萌芽阶段,难以复制人类智能。本文的核心目标是优化机器人路径规划(RPP)。通过采用增强型红鹿算法(ERDA),我们试图加强 RPP,以提高变电站巡检的效率。所使用的关键方法似乎是建模、实验、比较分析和一些数据基准要素,以便在模拟和现实世界中系统地评估和验证所提出的技术和模型。研究旨在提高变电站检测的有效性,加强社会用电安全。所提出的混合方法结合了比例-积分-派生(PID)和ERDA(PID-ERDA),是为变电站巡检量身定制的智能 RPP 框架的基础。与单独使用 PID 或 ERDA 相比,PID-ERDA 模型的性能显著提高了 18%-29%,响应时间缩短了 14%-26%。在 85 次试验中,PID-ERDA 始终能在 40-60 次试验中获得最佳解决方案,而 PID 和 ERDA 只能在 20-40 次试验中获得不一致的优化。此外,它还将平均响应时间从单独使用 PID 和 ERDA 时的 21 至 27 秒缩短至 17 至 20 秒。PID-ERDA 还表现出更高的路径精度,比改进型自适应控制算法-前馈神经网络、增强型统一算法-易感-感染-移除和有界行为-粒子群优化等方法高出 7%-13%。研究证实,PID-ERDA 模型显著增强了变电站巡检的路径规划,是现代输电系统中电站巡检 RPP 的里程碑。这项研究的主要贡献在于它为电站巡检的 RPP 带来了重大改进,尤其是在现代输电系统中的变电站监控方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信