{"title":"拥挤和动态环境中的社会感知机器人导航框架:运动规划技术的比较","authors":"Hong Thai Le, Duy Thao Nguyen, Xuan-Tung Truong","doi":"10.1109/NICS54270.2021.9701496","DOIUrl":null,"url":null,"abstract":"We present a comparison of navigation capability for mobile robots in crowded environments between the hybrid reciprocal velocity obstacle (HRVO) model and the social force model (SFM). The SFM determines the velocities to drive a mobile robot to its goal destination by using information about the position of surrounding humans and obstacles; meanwhile, the HRVO model considers the current position and velocity to calculate the new velocity for the mobile robot. The comparison is evaluated by conducting experiments in simulation environment. The experimental results have demonstrated that using additional information help the mobile robot achieve better performance when avoiding obstacles in crowded environments.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Socially aware robot navigation framework in crowded and dynamic environments: A comparison of motion planning techniques\",\"authors\":\"Hong Thai Le, Duy Thao Nguyen, Xuan-Tung Truong\",\"doi\":\"10.1109/NICS54270.2021.9701496\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a comparison of navigation capability for mobile robots in crowded environments between the hybrid reciprocal velocity obstacle (HRVO) model and the social force model (SFM). The SFM determines the velocities to drive a mobile robot to its goal destination by using information about the position of surrounding humans and obstacles; meanwhile, the HRVO model considers the current position and velocity to calculate the new velocity for the mobile robot. The comparison is evaluated by conducting experiments in simulation environment. The experimental results have demonstrated that using additional information help the mobile robot achieve better performance when avoiding obstacles in crowded environments.\",\"PeriodicalId\":296963,\"journal\":{\"name\":\"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NICS54270.2021.9701496\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NICS54270.2021.9701496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Socially aware robot navigation framework in crowded and dynamic environments: A comparison of motion planning techniques
We present a comparison of navigation capability for mobile robots in crowded environments between the hybrid reciprocal velocity obstacle (HRVO) model and the social force model (SFM). The SFM determines the velocities to drive a mobile robot to its goal destination by using information about the position of surrounding humans and obstacles; meanwhile, the HRVO model considers the current position and velocity to calculate the new velocity for the mobile robot. The comparison is evaluated by conducting experiments in simulation environment. The experimental results have demonstrated that using additional information help the mobile robot achieve better performance when avoiding obstacles in crowded environments.