{"title":"A Multi-Objective Evolutionary Approach to Optimize the Morphology of a Six Articulated-Wheeled Robot","authors":"S. Lim, Jason Teo","doi":"10.1109/ICAIET.2014.14","DOIUrl":null,"url":null,"abstract":"This paper proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to minimize the morphology design while the second objective is to maximize the performance of the SAWR in performing the climbing motion. Results show that the proposed MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance. The Pareto set of optimal solutions provide users a choice of solutions for trade-off between the two objectives.","PeriodicalId":225159,"journal":{"name":"2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIET.2014.14","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 proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to minimize the morphology design while the second objective is to maximize the performance of the SAWR in performing the climbing motion. Results show that the proposed MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance. The Pareto set of optimal solutions provide users a choice of solutions for trade-off between the two objectives.