无人驾驶电动铲车自主路径规划与跟踪控制方法研究

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-12-21 DOI:10.1111/mice.13402

Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue Huang

{"title":"无人驾驶电动铲车自主路径规划与跟踪控制方法研究","authors":"Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue Huang","doi":"10.1111/mice.13402","DOIUrl":null,"url":null,"abstract":"Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on autonomous path planning and tracking control methods for unmanned electric shovels\",\"authors\":\"Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue Huang\",\"doi\":\"10.1111/mice.13402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2024-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer-Aided Civil and Infrastructure Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1111/mice.13402\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13402","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

在大型挖掘机械中实现完全无人操作依赖于强大的自动驾驶能力。与较轻的高速履带车辆相比，电动铲车的转向限制和倒车困难带来了独特的挑战。本文探讨了这些操作和技术挑战，并介绍了一种结合制导-混合a *算法和动态窗口方法的轨迹规划方案。研制了一种具有可调因子的高精度跟踪控制器。仿真结果表明，该方法提高了路径搜索效率，防止了路径反转，航向误差控制在5°以内。样机实验证实了该控制器在计算响应速度和控制稳定性方面的优越性，在0.1 m时保持了较高的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Research on autonomous path planning and tracking control methods for unmanned electric shovels

Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.