{"title":"Optimal multi-agent planning solution for a sample gathering problem","authors":"A. Burlacu, M. Kloetzer, F. Ostafi","doi":"10.1109/AQTR.2014.6857901","DOIUrl":null,"url":null,"abstract":"This research targets the problem of automatically planning a team of mobile agents such that they collect the samples scattered throughout an environment in minimum time. Each mobile agent can carry at most one sample at a time and it can travel a maximum total distance, given by agent's available energy. The environment is assumed already abstracted to a finite graph, where a node plays the role of the deposit where the samples should be gathered. Our solution consists in several steps that lead to a formulation of the initial problem as a Mixed Integer Linear Programming one. The solution yields a plan that imposes for each agent the samples and the order for collecting them. This result is optimal from the point of view of collecting all samples in minimum time.","PeriodicalId":297141,"journal":{"name":"2014 IEEE International Conference on Automation, Quality and Testing, Robotics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Automation, Quality and Testing, Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AQTR.2014.6857901","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This research targets the problem of automatically planning a team of mobile agents such that they collect the samples scattered throughout an environment in minimum time. Each mobile agent can carry at most one sample at a time and it can travel a maximum total distance, given by agent's available energy. The environment is assumed already abstracted to a finite graph, where a node plays the role of the deposit where the samples should be gathered. Our solution consists in several steps that lead to a formulation of the initial problem as a Mixed Integer Linear Programming one. The solution yields a plan that imposes for each agent the samples and the order for collecting them. This result is optimal from the point of view of collecting all samples in minimum time.