{"title":"几何约束下AUV对接时间最优路径规划方法","authors":"Zeyu Li, Weidong Liu, Li-e Gao, Le Li","doi":"10.1109/OCEANSE.2019.8867060","DOIUrl":null,"url":null,"abstract":"Challenges in ocean environment bring complexities for AUV docking, including ocean currents, obstacles and geometrical constraints. This paper proposed an evolutionary- based method, to optimize the docking path. First, the ocean environment and constraints are analysed and modelled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and mutation operator are introduced in Particle Swarm Optimization (PSO), to achieve the global time- optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A time-optimal path planning method for AUV docking under geometrical constraints\",\"authors\":\"Zeyu Li, Weidong Liu, Li-e Gao, Le Li\",\"doi\":\"10.1109/OCEANSE.2019.8867060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Challenges in ocean environment bring complexities for AUV docking, including ocean currents, obstacles and geometrical constraints. This paper proposed an evolutionary- based method, to optimize the docking path. First, the ocean environment and constraints are analysed and modelled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and mutation operator are introduced in Particle Swarm Optimization (PSO), to achieve the global time- optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.\",\"PeriodicalId\":375793,\"journal\":{\"name\":\"OCEANS 2019 - Marseille\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"OCEANS 2019 - Marseille\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OCEANSE.2019.8867060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"OCEANS 2019 - Marseille","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANSE.2019.8867060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A time-optimal path planning method for AUV docking under geometrical constraints
Challenges in ocean environment bring complexities for AUV docking, including ocean currents, obstacles and geometrical constraints. This paper proposed an evolutionary- based method, to optimize the docking path. First, the ocean environment and constraints are analysed and modelled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and mutation operator are introduced in Particle Swarm Optimization (PSO), to achieve the global time- optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.