Javier V. Gómez, David Álvarez, S. Garrido, L. Moreno
{"title":"快速行军广场的动觉教学","authors":"Javier V. Gómez, David Álvarez, S. Garrido, L. Moreno","doi":"10.1109/IROS.2012.6385497","DOIUrl":null,"url":null,"abstract":"This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"25 1","pages":"1305-1310"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Kinesthetic teaching via Fast Marching Square\",\"authors\":\"Javier V. Gómez, David Álvarez, S. Garrido, L. Moreno\",\"doi\":\"10.1109/IROS.2012.6385497\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.\",\"PeriodicalId\":6358,\"journal\":{\"name\":\"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems\",\"volume\":\"25 1\",\"pages\":\"1305-1310\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IROS.2012.6385497\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2012.6385497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.