B. K. S. Prasad, A. Manjunath, Hariharan Ramasangu
{"title":"Multi-agent trajectory control under faulty leader: Energy-level based leader election under constant velocity","authors":"B. K. S. Prasad, A. Manjunath, Hariharan Ramasangu","doi":"10.1109/ICACCI.2016.7732370","DOIUrl":null,"url":null,"abstract":"A multi-agent flocking control algorithm consisting of leader and agents moving at constant velocity with a method for leader election is proposed in this paper. Due to a faulty leader, connectivity lost between itself and the agents, leading to divergence from trajectory path. Hence, leader election algorithm is developed to replace a faulty leader, thereby regain control of the path of the agents. This leader election algorithm utilizes energy level of each agent. All agents and leader have a certain energy based on theoretical and measured control inputs. This energy represents the degree of deviation from desired path, hence, the agent with least utilised energy, will be elected leader.","PeriodicalId":371328,"journal":{"name":"2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACCI.2016.7732370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
A multi-agent flocking control algorithm consisting of leader and agents moving at constant velocity with a method for leader election is proposed in this paper. Due to a faulty leader, connectivity lost between itself and the agents, leading to divergence from trajectory path. Hence, leader election algorithm is developed to replace a faulty leader, thereby regain control of the path of the agents. This leader election algorithm utilizes energy level of each agent. All agents and leader have a certain energy based on theoretical and measured control inputs. This energy represents the degree of deviation from desired path, hence, the agent with least utilised energy, will be elected leader.