{"title":"基于混合粒子群算法的多机器人编队轨迹规划","authors":"Jingwen Wang, X. Ren, Jun Liu","doi":"10.1109/IHMSC.2013.88","DOIUrl":null,"url":null,"abstract":"A hybrid particle swarm optimization (PSO) algorithm which consists of continuous and discrete PSO algorithm is introduced to solve the trajectory planning problem of multi-robot formation. The continuous PSO algorithm is utilized to optimize the center position and rotation angle of the desired formation, while the discrete PSO algorithm is to optimize the matching relationship between the initial and target positions. To demonstrate the correctness and validity of the proposed algorithm, simulation results of the trajectory planning for typical formation and another transformed formation are presented.","PeriodicalId":222375,"journal":{"name":"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Trajectory Planning for Multi-robot Formation by One Hybrid Particle Swarm Optimization Algorithm\",\"authors\":\"Jingwen Wang, X. Ren, Jun Liu\",\"doi\":\"10.1109/IHMSC.2013.88\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A hybrid particle swarm optimization (PSO) algorithm which consists of continuous and discrete PSO algorithm is introduced to solve the trajectory planning problem of multi-robot formation. The continuous PSO algorithm is utilized to optimize the center position and rotation angle of the desired formation, while the discrete PSO algorithm is to optimize the matching relationship between the initial and target positions. To demonstrate the correctness and validity of the proposed algorithm, simulation results of the trajectory planning for typical formation and another transformed formation are presented.\",\"PeriodicalId\":222375,\"journal\":{\"name\":\"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2013.88\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2013.88","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Trajectory Planning for Multi-robot Formation by One Hybrid Particle Swarm Optimization Algorithm
A hybrid particle swarm optimization (PSO) algorithm which consists of continuous and discrete PSO algorithm is introduced to solve the trajectory planning problem of multi-robot formation. The continuous PSO algorithm is utilized to optimize the center position and rotation angle of the desired formation, while the discrete PSO algorithm is to optimize the matching relationship between the initial and target positions. To demonstrate the correctness and validity of the proposed algorithm, simulation results of the trajectory planning for typical formation and another transformed formation are presented.