{"title":"Path planning in extended uncertain environments","authors":"M. Rendas, S. Rolfes","doi":"10.1109/IECON.1998.724262","DOIUrl":null,"url":null,"abstract":"This paper presents a new approach to the problem of planning the motion of a mobile robot in extended uncertain environments. All knowledge of the environment has been acquired by the robot during the current (or a previous) operation, such that the environment description reflects the accumulated error of the robot's pose during periods of dead-reckoning navigation. In this uncertain environment, the robot searches for trajectories that maximize the probability of attaining a desired target region. For that purpose we identify a discrete set of robot positions in order to construct a routing graph, whose arcs represent the probability of reaching a new position. In that way the search for an optimal trajectory is solved by searching for a minimum weight path in a routing graph. The method is based on a probabilistic model of all the errors/uncertainties affecting the reliability of the planned trajectory.","PeriodicalId":377136,"journal":{"name":"IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECON.1998.724262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a new approach to the problem of planning the motion of a mobile robot in extended uncertain environments. All knowledge of the environment has been acquired by the robot during the current (or a previous) operation, such that the environment description reflects the accumulated error of the robot's pose during periods of dead-reckoning navigation. In this uncertain environment, the robot searches for trajectories that maximize the probability of attaining a desired target region. For that purpose we identify a discrete set of robot positions in order to construct a routing graph, whose arcs represent the probability of reaching a new position. In that way the search for an optimal trajectory is solved by searching for a minimum weight path in a routing graph. The method is based on a probabilistic model of all the errors/uncertainties affecting the reliability of the planned trajectory.