{"title":"基于群体智能的多机器人任务分配","authors":"Shuhua Liu, Tie-li Sun, C. Hung","doi":"10.5772/13106","DOIUrl":null,"url":null,"abstract":"The task allocation was studied based on the swarm inteeligence for the large-scale multi-robot system with loose-and tight-coupled tasks adopting the hierarchial architecture. In the high level,the ant colony algorithm was employed to find the optimal allocation of the loose-coupled tasks,namely,based on the reverse distribution idea,taking each ant to form a task,an undertaker was chosen for every task. In the low level,the coalition formation algorithms based on the ant colony optimization(ACO) ,the particle swarm and ant colomy optimization(PSACO) ,or the quantum-inspised ant colony optimization(QACO) was proposed respectively for performin a tight-coupled task.Simulations were performed and results showed that PSACO provides the best solution,but its running time is the largest;QACO is a little inferior in solution quality to PSACO,however,it needs only a half time of the 2 other methods. Therefore,QACO appears more suitable for the task allocation of the large-scale multi-robot system.","PeriodicalId":16277,"journal":{"name":"Journal of Jilin University","volume":"341 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2011-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Multi-Robot Task Allocation Based on Swarm Intelligence\",\"authors\":\"Shuhua Liu, Tie-li Sun, C. Hung\",\"doi\":\"10.5772/13106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The task allocation was studied based on the swarm inteeligence for the large-scale multi-robot system with loose-and tight-coupled tasks adopting the hierarchial architecture. In the high level,the ant colony algorithm was employed to find the optimal allocation of the loose-coupled tasks,namely,based on the reverse distribution idea,taking each ant to form a task,an undertaker was chosen for every task. In the low level,the coalition formation algorithms based on the ant colony optimization(ACO) ,the particle swarm and ant colomy optimization(PSACO) ,or the quantum-inspised ant colony optimization(QACO) was proposed respectively for performin a tight-coupled task.Simulations were performed and results showed that PSACO provides the best solution,but its running time is the largest;QACO is a little inferior in solution quality to PSACO,however,it needs only a half time of the 2 other methods. Therefore,QACO appears more suitable for the task allocation of the large-scale multi-robot system.\",\"PeriodicalId\":16277,\"journal\":{\"name\":\"Journal of Jilin University\",\"volume\":\"341 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Jilin University\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5772/13106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Jilin University","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/13106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Robot Task Allocation Based on Swarm Intelligence
The task allocation was studied based on the swarm inteeligence for the large-scale multi-robot system with loose-and tight-coupled tasks adopting the hierarchial architecture. In the high level,the ant colony algorithm was employed to find the optimal allocation of the loose-coupled tasks,namely,based on the reverse distribution idea,taking each ant to form a task,an undertaker was chosen for every task. In the low level,the coalition formation algorithms based on the ant colony optimization(ACO) ,the particle swarm and ant colomy optimization(PSACO) ,or the quantum-inspised ant colony optimization(QACO) was proposed respectively for performin a tight-coupled task.Simulations were performed and results showed that PSACO provides the best solution,but its running time is the largest;QACO is a little inferior in solution quality to PSACO,however,it needs only a half time of the 2 other methods. Therefore,QACO appears more suitable for the task allocation of the large-scale multi-robot system.