{"title":"Coevolution of form and function in the design of micro air vehicles","authors":"M. Bugajska, A. Schultz","doi":"10.1109/EH.2002.1029881","DOIUrl":null,"url":null,"abstract":"This paper discusses approaches to cooperative coevolution of for and function for autonomous vehicles, specifically evolving morphology and control for an autonomous micro air vehicle (MAV). The evolution of a sensor suite with minimal size, weight, and power requirements, and reactive strategies for collision-free navigation for the simulated MAV is described. Results are presented for several different coevolutionary approaches to evolution of form and junction (single- and multiple-species models) and for two different control architectures (a rulebase controller based on the SAMUEL learning system and a neural network controller implemented and evolved using ECkit).","PeriodicalId":322028,"journal":{"name":"Proceedings 2002 NASA/DoD Conference on Evolvable Hardware","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 2002 NASA/DoD Conference on Evolvable Hardware","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EH.2002.1029881","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 26
Abstract
This paper discusses approaches to cooperative coevolution of for and function for autonomous vehicles, specifically evolving morphology and control for an autonomous micro air vehicle (MAV). The evolution of a sensor suite with minimal size, weight, and power requirements, and reactive strategies for collision-free navigation for the simulated MAV is described. Results are presented for several different coevolutionary approaches to evolution of form and junction (single- and multiple-species models) and for two different control architectures (a rulebase controller based on the SAMUEL learning system and a neural network controller implemented and evolved using ECkit).