{"title":"网络机器人无速度测量的任务空间单方位编队控制","authors":"Kun Li, Kai Zhao, Zhi Li, Yongduan Song","doi":"10.1002/rnc.7977","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In this article, two novel bearing-only formation control schemes based on the passivity property are proposed for networked robotic manipulators, which are capable of achieving end-effector formation utilizing onboard vision-based sensors. In contrast to the existing methods, the developed strategy exhibits the following features. Firstly, it eliminates the requirement for velocity measurements and communication among manipulators, which improves the flexibility and maneuverability of the system while significantly reducing computational overhead. Secondly, we establish the exponential stability of the desired formation and possess the duality property if the manipulator is non-redundant. Furthermore, by incorporating an approximate differentiation filter to compensate for unavailable velocity measurements, the measurement conditions for formation are further reduced, making the approach applicable to both redundant and non-redundant manipulators. This modification enables manufacturers to eliminate the need for an additional sensor on each manipulator, thereby making the formation system more cost-effective, reducing load, and facilitating implementation. Two simulations involving a group of two-link robotic manipulators are conducted to validate the efficiency of the theoretical results.</p>\n </div>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"35 12","pages":"5227-5237"},"PeriodicalIF":3.2000,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Task-Space Bearing-Only Formation Control for Networked Robotic Manipulators Without Velocity Measurement\",\"authors\":\"Kun Li, Kai Zhao, Zhi Li, Yongduan Song\",\"doi\":\"10.1002/rnc.7977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>In this article, two novel bearing-only formation control schemes based on the passivity property are proposed for networked robotic manipulators, which are capable of achieving end-effector formation utilizing onboard vision-based sensors. In contrast to the existing methods, the developed strategy exhibits the following features. Firstly, it eliminates the requirement for velocity measurements and communication among manipulators, which improves the flexibility and maneuverability of the system while significantly reducing computational overhead. Secondly, we establish the exponential stability of the desired formation and possess the duality property if the manipulator is non-redundant. Furthermore, by incorporating an approximate differentiation filter to compensate for unavailable velocity measurements, the measurement conditions for formation are further reduced, making the approach applicable to both redundant and non-redundant manipulators. This modification enables manufacturers to eliminate the need for an additional sensor on each manipulator, thereby making the formation system more cost-effective, reducing load, and facilitating implementation. Two simulations involving a group of two-link robotic manipulators are conducted to validate the efficiency of the theoretical results.</p>\\n </div>\",\"PeriodicalId\":50291,\"journal\":{\"name\":\"International Journal of Robust and Nonlinear Control\",\"volume\":\"35 12\",\"pages\":\"5227-5237\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Robust and Nonlinear Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7977\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7977","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Task-Space Bearing-Only Formation Control for Networked Robotic Manipulators Without Velocity Measurement
In this article, two novel bearing-only formation control schemes based on the passivity property are proposed for networked robotic manipulators, which are capable of achieving end-effector formation utilizing onboard vision-based sensors. In contrast to the existing methods, the developed strategy exhibits the following features. Firstly, it eliminates the requirement for velocity measurements and communication among manipulators, which improves the flexibility and maneuverability of the system while significantly reducing computational overhead. Secondly, we establish the exponential stability of the desired formation and possess the duality property if the manipulator is non-redundant. Furthermore, by incorporating an approximate differentiation filter to compensate for unavailable velocity measurements, the measurement conditions for formation are further reduced, making the approach applicable to both redundant and non-redundant manipulators. This modification enables manufacturers to eliminate the need for an additional sensor on each manipulator, thereby making the formation system more cost-effective, reducing load, and facilitating implementation. Two simulations involving a group of two-link robotic manipulators are conducted to validate the efficiency of the theoretical results.
期刊介绍:
Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.