{"title":"A Multi-Robot Task Allocation Method Based on Multi-Objective Optimization","authors":"Jianping Chen, Jianbin Wang, Q. Xiao, Chang-Huang Chen","doi":"10.1109/ICARCV.2018.8581110","DOIUrl":null,"url":null,"abstract":"Time consumption and energy consumption are essential indicators for evaluating the effectiveness of task completion in multi-robot systems. On the basis of considering these two indicators, a Multi-Robot task allocation method based on multi-objective (time utility and energy utility) optimization (MOO-MRTA) is proposed. This method gives the definition and construction of the robotic energy utility function. It also establishes a model of task allocation based on multi-objective and discusses the problem of solving the model and so on. The RoboCup Rescue Simulation experiment shows that this method has the advantages of strong searching capacity, fast convergence rate, and that it can quickly find the Pareto optimal task allocation scheme and help the rescue team get an ideal result.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2018.8581110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Time consumption and energy consumption are essential indicators for evaluating the effectiveness of task completion in multi-robot systems. On the basis of considering these two indicators, a Multi-Robot task allocation method based on multi-objective (time utility and energy utility) optimization (MOO-MRTA) is proposed. This method gives the definition and construction of the robotic energy utility function. It also establishes a model of task allocation based on multi-objective and discusses the problem of solving the model and so on. The RoboCup Rescue Simulation experiment shows that this method has the advantages of strong searching capacity, fast convergence rate, and that it can quickly find the Pareto optimal task allocation scheme and help the rescue team get an ideal result.
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