{"title":"缆索驱动机器人挠度图的人工神经网络预测","authors":"L. Notash","doi":"10.1115/detc2020-22513","DOIUrl":null,"url":null,"abstract":"\n In this paper, the learning models of cable-driven robots are developed applying the artificial neural network (ANN). For known input and output data and known relationships (regression problem), the deflection maps of cable-driven parallel robots are predicted utilizing a multi-layer ANN. Two cable robots, a planar robot and a translational spatial robot, are examined to evaluate their models. The deflection maps of these cable robots are generated using the ANN and a non-linear optimization method. The predicted deflections of the ANN models, using much less number of poses for training, are highly satisfactory and comparable to the results obtained by a nonlinear optimization method throughout the pertinent discretized workspaces. In addition, ANN models could predict the deflections for poses that the nonlinear optimization methods may not. Moreover, with variations in robot/task parameters, such as payload, ANN models may predict accurate deflections.","PeriodicalId":365283,"journal":{"name":"Volume 10: 44th Mechanisms and Robotics Conference (MR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Artificial Neural Network Prediction of Deflection Maps for Cable-Driven Robots\",\"authors\":\"L. Notash\",\"doi\":\"10.1115/detc2020-22513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In this paper, the learning models of cable-driven robots are developed applying the artificial neural network (ANN). For known input and output data and known relationships (regression problem), the deflection maps of cable-driven parallel robots are predicted utilizing a multi-layer ANN. Two cable robots, a planar robot and a translational spatial robot, are examined to evaluate their models. The deflection maps of these cable robots are generated using the ANN and a non-linear optimization method. The predicted deflections of the ANN models, using much less number of poses for training, are highly satisfactory and comparable to the results obtained by a nonlinear optimization method throughout the pertinent discretized workspaces. In addition, ANN models could predict the deflections for poses that the nonlinear optimization methods may not. Moreover, with variations in robot/task parameters, such as payload, ANN models may predict accurate deflections.\",\"PeriodicalId\":365283,\"journal\":{\"name\":\"Volume 10: 44th Mechanisms and Robotics Conference (MR)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 10: 44th Mechanisms and Robotics Conference (MR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/detc2020-22513\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 10: 44th Mechanisms and Robotics Conference (MR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/detc2020-22513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial Neural Network Prediction of Deflection Maps for Cable-Driven Robots
In this paper, the learning models of cable-driven robots are developed applying the artificial neural network (ANN). For known input and output data and known relationships (regression problem), the deflection maps of cable-driven parallel robots are predicted utilizing a multi-layer ANN. Two cable robots, a planar robot and a translational spatial robot, are examined to evaluate their models. The deflection maps of these cable robots are generated using the ANN and a non-linear optimization method. The predicted deflections of the ANN models, using much less number of poses for training, are highly satisfactory and comparable to the results obtained by a nonlinear optimization method throughout the pertinent discretized workspaces. In addition, ANN models could predict the deflections for poses that the nonlinear optimization methods may not. Moreover, with variations in robot/task parameters, such as payload, ANN models may predict accurate deflections.