{"title":"Fuzzy reactive control for wheeled mobile robots","authors":"P. Chand","doi":"10.1109/ICARA.2015.7081142","DOIUrl":null,"url":null,"abstract":"A mobile robot's ability to negotiate obstacles is important for successful point-to-point navigation. Hence, this paper presents a two stage fuzzy reactive control method. The first stage consists of a direction sensor that employs a fuzzy objective function to compute a direction (heading angle) for a robot to travel. At the second stage, a fuzzy dynamic window method utilizes a fuzzy objective function to determine the target wheel velocities of the robot. Simulations with two heterogeneous robots are performed in multiple environments. Initial results indicate that the fuzzy methods improve path length and are better at reducing speed around obstacles than linear methods.","PeriodicalId":176657,"journal":{"name":"2015 6th International Conference on Automation, Robotics and Applications (ICARA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 6th International Conference on Automation, Robotics and Applications (ICARA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARA.2015.7081142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A mobile robot's ability to negotiate obstacles is important for successful point-to-point navigation. Hence, this paper presents a two stage fuzzy reactive control method. The first stage consists of a direction sensor that employs a fuzzy objective function to compute a direction (heading angle) for a robot to travel. At the second stage, a fuzzy dynamic window method utilizes a fuzzy objective function to determine the target wheel velocities of the robot. Simulations with two heterogeneous robots are performed in multiple environments. Initial results indicate that the fuzzy methods improve path length and are better at reducing speed around obstacles than linear methods.