{"title":"Multiple Waypoint Path Planning for a Mobile Robot using Genetic Algorithms","authors":"T. Davies, A. Jnifene","doi":"10.1109/CIMSA.2006.250741","DOIUrl":null,"url":null,"abstract":"This investigation developed a MATLAB program, based on genetic algorithms that generated an optimal (shortest distance) path plan for a mobile robot to visit all of the specified waypoints without colliding with the known obstacles. The designed genetic algorithm path planner was shown to accomplish this task and produce superior results when compared against a full search path planner. Next, it was shown that the choice of search parameters for the genetic algorithm effected the time to execute the search and the quality of the solution (length of the chosen path). Having proven the genetic algorithm path planner in simulation, the genetic algorithm path planner then successfully guided an actual X80 mobile robot to all its waypoints without colliding with any obstacles in a test environment","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIMSA.2006.250741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
This investigation developed a MATLAB program, based on genetic algorithms that generated an optimal (shortest distance) path plan for a mobile robot to visit all of the specified waypoints without colliding with the known obstacles. The designed genetic algorithm path planner was shown to accomplish this task and produce superior results when compared against a full search path planner. Next, it was shown that the choice of search parameters for the genetic algorithm effected the time to execute the search and the quality of the solution (length of the chosen path). Having proven the genetic algorithm path planner in simulation, the genetic algorithm path planner then successfully guided an actual X80 mobile robot to all its waypoints without colliding with any obstacles in a test environment