{"title":"利用外部计算机视觉的自动导引车导航方法研究","authors":"Yingbo Zhao, Xiu Shichao, Hong Yuan, Bu Xinyu","doi":"10.1177/09544054241245476","DOIUrl":null,"url":null,"abstract":"Automated guided vehicle (AGV) navigation is extensively used in industrial manufacturing. Existing AGV navigation methods have high accuracy but usually require expensive positioning sensors. This paper proposes a novel method for AGV navigation based on external computer vision (NECV). No matter how many AGVs are in the workshop, the proposed NECV method uses only an external camera mounted on the top of the roof to detect and track AGVs, and all the AGVs don’t need to be equipped with any positioning sensors. Because there is no need to equip positioning sensors on AGVs, and also don’t need to arrange positioning signs, NECV significantly reduces the positioning cost of navigation. YOLOv8 was selected as the detector for NECV, and the training was completed using a prepared dataset. We improved the structure of the StrongSORT algorithm and used it as the tracker. The improved StrongSORT algorithm is the core of NECV. The imaging coordinates of the AGVs are detected by the detector, transformed into global coordinates through inverse perspective mapping, and passed to the master console. Experimental results indicated that the NECV detection deviation q of the AGV and the experimental accuracy metrics of the NECV after compensating q were considerably improved, close to those of the popular Quick Response (QR) code navigation method. Statistically, NECV can reduce the cost of AGV positioning detection by 90%.","PeriodicalId":20663,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on automated guided vehicle navigation method with external computer vision\",\"authors\":\"Yingbo Zhao, Xiu Shichao, Hong Yuan, Bu Xinyu\",\"doi\":\"10.1177/09544054241245476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated guided vehicle (AGV) navigation is extensively used in industrial manufacturing. Existing AGV navigation methods have high accuracy but usually require expensive positioning sensors. This paper proposes a novel method for AGV navigation based on external computer vision (NECV). No matter how many AGVs are in the workshop, the proposed NECV method uses only an external camera mounted on the top of the roof to detect and track AGVs, and all the AGVs don’t need to be equipped with any positioning sensors. Because there is no need to equip positioning sensors on AGVs, and also don’t need to arrange positioning signs, NECV significantly reduces the positioning cost of navigation. YOLOv8 was selected as the detector for NECV, and the training was completed using a prepared dataset. We improved the structure of the StrongSORT algorithm and used it as the tracker. The improved StrongSORT algorithm is the core of NECV. The imaging coordinates of the AGVs are detected by the detector, transformed into global coordinates through inverse perspective mapping, and passed to the master console. Experimental results indicated that the NECV detection deviation q of the AGV and the experimental accuracy metrics of the NECV after compensating q were considerably improved, close to those of the popular Quick Response (QR) code navigation method. Statistically, NECV can reduce the cost of AGV positioning detection by 90%.\",\"PeriodicalId\":20663,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/09544054241245476\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544054241245476","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Study on automated guided vehicle navigation method with external computer vision
Automated guided vehicle (AGV) navigation is extensively used in industrial manufacturing. Existing AGV navigation methods have high accuracy but usually require expensive positioning sensors. This paper proposes a novel method for AGV navigation based on external computer vision (NECV). No matter how many AGVs are in the workshop, the proposed NECV method uses only an external camera mounted on the top of the roof to detect and track AGVs, and all the AGVs don’t need to be equipped with any positioning sensors. Because there is no need to equip positioning sensors on AGVs, and also don’t need to arrange positioning signs, NECV significantly reduces the positioning cost of navigation. YOLOv8 was selected as the detector for NECV, and the training was completed using a prepared dataset. We improved the structure of the StrongSORT algorithm and used it as the tracker. The improved StrongSORT algorithm is the core of NECV. The imaging coordinates of the AGVs are detected by the detector, transformed into global coordinates through inverse perspective mapping, and passed to the master console. Experimental results indicated that the NECV detection deviation q of the AGV and the experimental accuracy metrics of the NECV after compensating q were considerably improved, close to those of the popular Quick Response (QR) code navigation method. Statistically, NECV can reduce the cost of AGV positioning detection by 90%.
期刊介绍:
Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed.
Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing.
Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.