{"title":"Request and Share then Assign (RASTA): Task Assignment for Networked Multi-Robot Teams","authors":"Sam Friedman, Qi Han","doi":"10.1109/MASS50613.2020.00058","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an improvement of the Hungarian method to optimally solve the task assignment problem for a multi-robot team. Our proposed method involves all robots collaboratively working together to disseminate cost information and then individually computing an assignment that optimizes a particular global goal. Through theoretical analysis, we show that our approach is able to produce a common optimal assignment, sending significantly fewer messages and resulting in faster convergence than other approaches based on the Hungarian method. Our experimental results back up this claim, demonstrating that, even in the worst case, our approach sends a fraction of the messages required by other assignment methods and as a result scales better as team size increases.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MASS50613.2020.00058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose an improvement of the Hungarian method to optimally solve the task assignment problem for a multi-robot team. Our proposed method involves all robots collaboratively working together to disseminate cost information and then individually computing an assignment that optimizes a particular global goal. Through theoretical analysis, we show that our approach is able to produce a common optimal assignment, sending significantly fewer messages and resulting in faster convergence than other approaches based on the Hungarian method. Our experimental results back up this claim, demonstrating that, even in the worst case, our approach sends a fraction of the messages required by other assignment methods and as a result scales better as team size increases.