{"title":"基于JetBot的道路跟随场景分析","authors":"Che-Cheng Chang, Yu-Heng Juan, Cheng-Ling Huang, Hsuan-Ju Chen","doi":"10.1109/ECICE50847.2020.9302017","DOIUrl":null,"url":null,"abstract":"Recently, the application of Unmanned Ground Vehicle (UGV) has been increasingly prevalent. It can be used in different applications both indoors and outdoors. However, for some scenarios, the Global Positioning System (GPS) may not be always precise and available due to signal attenuation and multi-path propagation. Hence, in this research, we use a novel vision-based architecture, including machine learning and edge computing, to analyze the geometric features to find the path. Furthermore, we also intend to perform a scenario analysis for obtaining the setting with better performance. According to the experimental results, the prototype demonstrates the appropriate performance and properties.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Scenario Analysis for Road Following Using JetBot\",\"authors\":\"Che-Cheng Chang, Yu-Heng Juan, Cheng-Ling Huang, Hsuan-Ju Chen\",\"doi\":\"10.1109/ECICE50847.2020.9302017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, the application of Unmanned Ground Vehicle (UGV) has been increasingly prevalent. It can be used in different applications both indoors and outdoors. However, for some scenarios, the Global Positioning System (GPS) may not be always precise and available due to signal attenuation and multi-path propagation. Hence, in this research, we use a novel vision-based architecture, including machine learning and edge computing, to analyze the geometric features to find the path. Furthermore, we also intend to perform a scenario analysis for obtaining the setting with better performance. According to the experimental results, the prototype demonstrates the appropriate performance and properties.\",\"PeriodicalId\":130143,\"journal\":{\"name\":\"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECICE50847.2020.9302017\",\"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 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECICE50847.2020.9302017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recently, the application of Unmanned Ground Vehicle (UGV) has been increasingly prevalent. It can be used in different applications both indoors and outdoors. However, for some scenarios, the Global Positioning System (GPS) may not be always precise and available due to signal attenuation and multi-path propagation. Hence, in this research, we use a novel vision-based architecture, including machine learning and edge computing, to analyze the geometric features to find the path. Furthermore, we also intend to perform a scenario analysis for obtaining the setting with better performance. According to the experimental results, the prototype demonstrates the appropriate performance and properties.