{"title":"使用CNN芯片原型系统的视觉反馈","authors":"P. Arena, A. Basile, L. Fortuna, A. Virzì","doi":"10.1109/CNNA.2002.1035063","DOIUrl":null,"url":null,"abstract":"Robot locomotion control passes through a series of sensors that, according to information from the environment, allow the robot to adapt, in real time, its locomotion scheme or trajectory. When the goal of the robot is to reach a target in a non-structured environment the best approach is visual control realized by a fast image processing system. Fast parallel image processing of the CNN-UM cP4000 chip prototype permits one to obtain good performance, even in a real time control problem. The robot controlled by the implemented CNN visual feedback has a hexapod configuration and its locomotion system is also implemented by a multi-layer CNN structure. In this paper a CNN approach for both locomotion generation and visual control of the bio-inspired robot is presented.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2002-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Visual feedback by using a CNN chip prototype system\",\"authors\":\"P. Arena, A. Basile, L. Fortuna, A. Virzì\",\"doi\":\"10.1109/CNNA.2002.1035063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Robot locomotion control passes through a series of sensors that, according to information from the environment, allow the robot to adapt, in real time, its locomotion scheme or trajectory. When the goal of the robot is to reach a target in a non-structured environment the best approach is visual control realized by a fast image processing system. Fast parallel image processing of the CNN-UM cP4000 chip prototype permits one to obtain good performance, even in a real time control problem. The robot controlled by the implemented CNN visual feedback has a hexapod configuration and its locomotion system is also implemented by a multi-layer CNN structure. In this paper a CNN approach for both locomotion generation and visual control of the bio-inspired robot is presented.\",\"PeriodicalId\":387716,\"journal\":{\"name\":\"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.2002.1035063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.2002.1035063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual feedback by using a CNN chip prototype system
Robot locomotion control passes through a series of sensors that, according to information from the environment, allow the robot to adapt, in real time, its locomotion scheme or trajectory. When the goal of the robot is to reach a target in a non-structured environment the best approach is visual control realized by a fast image processing system. Fast parallel image processing of the CNN-UM cP4000 chip prototype permits one to obtain good performance, even in a real time control problem. The robot controlled by the implemented CNN visual feedback has a hexapod configuration and its locomotion system is also implemented by a multi-layer CNN structure. In this paper a CNN approach for both locomotion generation and visual control of the bio-inspired robot is presented.