{"title":"基于voronoi的无人机编队部署与MPC重新配置","authors":"T. Chevet, C. Maniu, C. Vlad, Youmin Zhang","doi":"10.1109/ICUAS.2018.8453342","DOIUrl":null,"url":null,"abstract":"This paper presents a decentralized Voronoi-based linear model predictive control (MPC) technique for the deployment and reconfiguration of a multi-agent system composed of unmanned aerial vehicles (UAVs) in a bounded area. At each time instant, this area is partitioned into non-overlapping time-varying Voronoi cells associated to each UAV agent. The formation deployment objective is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. The proposed MPC-based formation reconfiguration algorithms allow not only faulty/non-cooperating agents to leave the formation, but also recovered/healthy agents to join in the current formation, while avoiding collisions. Simulation results validate the effectiveness of the proposed control algorithms.","PeriodicalId":246293,"journal":{"name":"2018 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Voronoi-based UAVs Formation Deployment and Reconfiguration using MPC Techniques\",\"authors\":\"T. Chevet, C. Maniu, C. Vlad, Youmin Zhang\",\"doi\":\"10.1109/ICUAS.2018.8453342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a decentralized Voronoi-based linear model predictive control (MPC) technique for the deployment and reconfiguration of a multi-agent system composed of unmanned aerial vehicles (UAVs) in a bounded area. At each time instant, this area is partitioned into non-overlapping time-varying Voronoi cells associated to each UAV agent. The formation deployment objective is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. The proposed MPC-based formation reconfiguration algorithms allow not only faulty/non-cooperating agents to leave the formation, but also recovered/healthy agents to join in the current formation, while avoiding collisions. Simulation results validate the effectiveness of the proposed control algorithms.\",\"PeriodicalId\":246293,\"journal\":{\"name\":\"2018 International Conference on Unmanned Aircraft Systems (ICUAS)\",\"volume\":\"152 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Unmanned Aircraft Systems (ICUAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICUAS.2018.8453342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Unmanned Aircraft Systems (ICUAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUAS.2018.8453342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Voronoi-based UAVs Formation Deployment and Reconfiguration using MPC Techniques
This paper presents a decentralized Voronoi-based linear model predictive control (MPC) technique for the deployment and reconfiguration of a multi-agent system composed of unmanned aerial vehicles (UAVs) in a bounded area. At each time instant, this area is partitioned into non-overlapping time-varying Voronoi cells associated to each UAV agent. The formation deployment objective is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. The proposed MPC-based formation reconfiguration algorithms allow not only faulty/non-cooperating agents to leave the formation, but also recovered/healthy agents to join in the current formation, while avoiding collisions. Simulation results validate the effectiveness of the proposed control algorithms.