{"title":"通过归纳式机器学习实现机器人弧焊的空间视觉反馈","authors":"Goran D. Putnik, Petar B. Petrovic, Vaibhav Shah","doi":"10.1115/1.4064156","DOIUrl":null,"url":null,"abstract":"An intelligent system for spatial visual feedback is presented, that enables the robot's autonomy for a range of robotic assembly tasks, in particular for arc-welding, in an unstructured and ‘fixtureless’ environment. The robot's autonomy is empowered by embedded inductive inference based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (3-Dimensional) visual sensor in order to autonomously execute the objective task. The innovative result is that the recognition of the geometric primitives is done without a predefined Computer Aided Design (CAD) model, significantly improving the system's autonomy and robustness. The system is validated on real-world welding tasks.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial Visual Feedback for Robotic Arc-Welding Enforced by Inductive Machine Learning\",\"authors\":\"Goran D. Putnik, Petar B. Petrovic, Vaibhav Shah\",\"doi\":\"10.1115/1.4064156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An intelligent system for spatial visual feedback is presented, that enables the robot's autonomy for a range of robotic assembly tasks, in particular for arc-welding, in an unstructured and ‘fixtureless’ environment. The robot's autonomy is empowered by embedded inductive inference based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (3-Dimensional) visual sensor in order to autonomously execute the objective task. The innovative result is that the recognition of the geometric primitives is done without a predefined Computer Aided Design (CAD) model, significantly improving the system's autonomy and robustness. The system is validated on real-world welding tasks.\",\"PeriodicalId\":507815,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4064156\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4064156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatial Visual Feedback for Robotic Arc-Welding Enforced by Inductive Machine Learning
An intelligent system for spatial visual feedback is presented, that enables the robot's autonomy for a range of robotic assembly tasks, in particular for arc-welding, in an unstructured and ‘fixtureless’ environment. The robot's autonomy is empowered by embedded inductive inference based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (3-Dimensional) visual sensor in order to autonomously execute the objective task. The innovative result is that the recognition of the geometric primitives is done without a predefined Computer Aided Design (CAD) model, significantly improving the system's autonomy and robustness. The system is validated on real-world welding tasks.