Teng Zhang , Fangyu Peng , Rong Yan , Xiaowei Tang , Runpeng Deng , Jiangmiao Yuan
{"title":"量化机器人姿态误差的不确定性并校准可靠的补偿值","authors":"Teng Zhang , Fangyu Peng , Rong Yan , Xiaowei Tang , Runpeng Deng , Jiangmiao Yuan","doi":"10.1016/j.rcim.2024.102765","DOIUrl":null,"url":null,"abstract":"<div><p>Due to their inherent characteristics, robots inevitably suffer from pose errors, and accurate prediction is the key to error compensation, which facilitates the application of robots in high-precision scenarios. Existing studies almost follow the points-view, and the compensation effect depends entirely on the accuracy of the point prediction, which leads to overconfident prediction results. In order to quantify the pose errors uncertainty and achieve more accurate prediction, a method to quantify of uncertainty in robot pose errors and calibration of reliable compensation values is proposed in this paper. In the proposed method, a distribution-free joint prediction model is designed to realize the simultaneous prediction of points and uncertainty intervals. Based on this, the reliable compensation value calibration strategy is innovatively proposed. The proposed method is verified on five tasks including spatial motions, constant load and milling processing, showing accurate joint prediction capability and reliable accuracy improvement. In addition, through online compensation experiments, the pose errors are reduced by 90 %, which promotes the application of robots in higher-precision scenarios.</p></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":9.1000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantification of uncertainty in robot pose errors and calibration of reliable compensation values\",\"authors\":\"Teng Zhang , Fangyu Peng , Rong Yan , Xiaowei Tang , Runpeng Deng , Jiangmiao Yuan\",\"doi\":\"10.1016/j.rcim.2024.102765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Due to their inherent characteristics, robots inevitably suffer from pose errors, and accurate prediction is the key to error compensation, which facilitates the application of robots in high-precision scenarios. Existing studies almost follow the points-view, and the compensation effect depends entirely on the accuracy of the point prediction, which leads to overconfident prediction results. In order to quantify the pose errors uncertainty and achieve more accurate prediction, a method to quantify of uncertainty in robot pose errors and calibration of reliable compensation values is proposed in this paper. In the proposed method, a distribution-free joint prediction model is designed to realize the simultaneous prediction of points and uncertainty intervals. Based on this, the reliable compensation value calibration strategy is innovatively proposed. The proposed method is verified on five tasks including spatial motions, constant load and milling processing, showing accurate joint prediction capability and reliable accuracy improvement. In addition, through online compensation experiments, the pose errors are reduced by 90 %, which promotes the application of robots in higher-precision scenarios.</p></div>\",\"PeriodicalId\":21452,\"journal\":{\"name\":\"Robotics and Computer-integrated Manufacturing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2024-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Computer-integrated Manufacturing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0736584524000516\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"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":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524000516","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Quantification of uncertainty in robot pose errors and calibration of reliable compensation values
Due to their inherent characteristics, robots inevitably suffer from pose errors, and accurate prediction is the key to error compensation, which facilitates the application of robots in high-precision scenarios. Existing studies almost follow the points-view, and the compensation effect depends entirely on the accuracy of the point prediction, which leads to overconfident prediction results. In order to quantify the pose errors uncertainty and achieve more accurate prediction, a method to quantify of uncertainty in robot pose errors and calibration of reliable compensation values is proposed in this paper. In the proposed method, a distribution-free joint prediction model is designed to realize the simultaneous prediction of points and uncertainty intervals. Based on this, the reliable compensation value calibration strategy is innovatively proposed. The proposed method is verified on five tasks including spatial motions, constant load and milling processing, showing accurate joint prediction capability and reliable accuracy improvement. In addition, through online compensation experiments, the pose errors are reduced by 90 %, which promotes the application of robots in higher-precision scenarios.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.