{"title":"基于参数化SOM的机器人轨迹动力学建模","authors":"A. C. Padoan, A. Araujo, G. Barreto","doi":"10.1109/SBRN.2002.1181471","DOIUrl":null,"url":null,"abstract":"Planning and control of robotic trajectories is an important and open issue. This paper uses an unsupervised neural network model to construct the dynamical modelling of trajectories. A neural network with a short term memory mechanism, was designed to provide the associated joint angles when it receives as input the present and some past states of the robot spatial position. The model uses the self-organizing map (SOM) to approximate the mapping using just some states of the trajectory.","PeriodicalId":157186,"journal":{"name":"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.","volume":"122 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic modeling of robotic trajectories using the parametrized SOM\",\"authors\":\"A. C. Padoan, A. Araujo, G. Barreto\",\"doi\":\"10.1109/SBRN.2002.1181471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Planning and control of robotic trajectories is an important and open issue. This paper uses an unsupervised neural network model to construct the dynamical modelling of trajectories. A neural network with a short term memory mechanism, was designed to provide the associated joint angles when it receives as input the present and some past states of the robot spatial position. The model uses the self-organizing map (SOM) to approximate the mapping using just some states of the trajectory.\",\"PeriodicalId\":157186,\"journal\":{\"name\":\"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.\",\"volume\":\"122 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBRN.2002.1181471\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBRN.2002.1181471","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic modeling of robotic trajectories using the parametrized SOM
Planning and control of robotic trajectories is an important and open issue. This paper uses an unsupervised neural network model to construct the dynamical modelling of trajectories. A neural network with a short term memory mechanism, was designed to provide the associated joint angles when it receives as input the present and some past states of the robot spatial position. The model uses the self-organizing map (SOM) to approximate the mapping using just some states of the trajectory.