D. Withey, Katlego Mogokonyane, Mayur Tikam, Ross Holder, Mahalingam Veeraragoo, Mxolisi Gambushe
{"title":"小型移动机器人的环境感知行为","authors":"D. Withey, Katlego Mogokonyane, Mayur Tikam, Ross Holder, Mahalingam Veeraragoo, Mxolisi Gambushe","doi":"10.1109/SAUPEC/RobMech/PRASA48453.2020.9041114","DOIUrl":null,"url":null,"abstract":"Simultaneous advances in mobile GPU computing and real-time object recognition now enable machines to make decisions and take actions based on the detection of objects of interest in the environment. An implementation of a mobile robot system that combines autonomous exploration and mapping capabilities with a real-time object recognition method based on a deep neural network running on a mobile GPU, is described. The system is able to detect objects of interest and then take real-time actions to interact with the objects, in this case, by moving to acquire inspection-style images of the object, from multiple angles. The robot system is small, self-contained and runs on battery power. The system shows the potential for the development of robotic systems with context awareness, permitting advanced autonomy.","PeriodicalId":215514,"journal":{"name":"2020 International SAUPEC/RobMech/PRASA Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Context-Aware Action with a Small Mobile Robot\",\"authors\":\"D. Withey, Katlego Mogokonyane, Mayur Tikam, Ross Holder, Mahalingam Veeraragoo, Mxolisi Gambushe\",\"doi\":\"10.1109/SAUPEC/RobMech/PRASA48453.2020.9041114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Simultaneous advances in mobile GPU computing and real-time object recognition now enable machines to make decisions and take actions based on the detection of objects of interest in the environment. An implementation of a mobile robot system that combines autonomous exploration and mapping capabilities with a real-time object recognition method based on a deep neural network running on a mobile GPU, is described. The system is able to detect objects of interest and then take real-time actions to interact with the objects, in this case, by moving to acquire inspection-style images of the object, from multiple angles. The robot system is small, self-contained and runs on battery power. The system shows the potential for the development of robotic systems with context awareness, permitting advanced autonomy.\",\"PeriodicalId\":215514,\"journal\":{\"name\":\"2020 International SAUPEC/RobMech/PRASA Conference\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International SAUPEC/RobMech/PRASA Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041114\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International SAUPEC/RobMech/PRASA Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simultaneous advances in mobile GPU computing and real-time object recognition now enable machines to make decisions and take actions based on the detection of objects of interest in the environment. An implementation of a mobile robot system that combines autonomous exploration and mapping capabilities with a real-time object recognition method based on a deep neural network running on a mobile GPU, is described. The system is able to detect objects of interest and then take real-time actions to interact with the objects, in this case, by moving to acquire inspection-style images of the object, from multiple angles. The robot system is small, self-contained and runs on battery power. The system shows the potential for the development of robotic systems with context awareness, permitting advanced autonomy.