T. Krajník, Tomáš Vintr, G. Broughton, Filip Majer, Tomáš Rouček, Jirí Ulrich, Jan Blaha, Veronika Pěčonková, Martin Rektoris
{"title":"CHRONOROBOTICS","authors":"T. Krajník, Tomáš Vintr, G. Broughton, Filip Majer, Tomáš Rouček, Jirí Ulrich, Jan Blaha, Veronika Pěčonková, Martin Rektoris","doi":"10.1145/3440084.3441195","DOIUrl":null,"url":null,"abstract":"Chronorobotics is the investigation of scientific methods allowing robots to adapt to and learn from the perpetual changes occurring in natural and human-populated environments. We present methods that can introduce the notion of dynamics into spatial environment models, resulting in representations which provide service robots with the ability to predict future states of changing environments. Several long-term experiments indicate that the aforementioned methods gradually improve the efficiency of robots' autonomous operations over time. More importantly, the experiments indicate that chronorobotic concepts improve robots' ability to seamlessly merge into human-populated environments, which is important for their integration and acceptance in human societies.","PeriodicalId":250100,"journal":{"name":"Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent Control","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3440084.3441195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Chronorobotics is the investigation of scientific methods allowing robots to adapt to and learn from the perpetual changes occurring in natural and human-populated environments. We present methods that can introduce the notion of dynamics into spatial environment models, resulting in representations which provide service robots with the ability to predict future states of changing environments. Several long-term experiments indicate that the aforementioned methods gradually improve the efficiency of robots' autonomous operations over time. More importantly, the experiments indicate that chronorobotic concepts improve robots' ability to seamlessly merge into human-populated environments, which is important for their integration and acceptance in human societies.