{"title":"少计算时间的强电流场水下航行器路径规划及改进不完备性问题","authors":"Feng Wang, Chenlong Li, Wenliang Chen","doi":"10.1109/IFEEA57288.2022.10037978","DOIUrl":null,"url":null,"abstract":"This paper solves the time-optimal path planning for autonomous underwater vehicles (AUVs) in ocean environment with cluttered currents. In this problem, the planner may not find a path in the search space with discrete motion directions, because it leads to a decrease in the available direction. But the search space with the continuous motion directions will greatly increase the computation. To avoid this, the paper presents an approach to improve the lack of discrete motion model by placing Steiner points on each edge. Combining with the ant colony algorithm, the path planner finds the time-optimal path in search space. The effectiveness is verified through simulations using a set of randomly generated current fields.","PeriodicalId":304779,"journal":{"name":"2022 9th International Forum on Electrical Engineering and Automation (IFEEA)","volume":"47 10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AUV Path Planning in Strong Current Fields with Less Computing Time and Improving Incompleteness Problem\",\"authors\":\"Feng Wang, Chenlong Li, Wenliang Chen\",\"doi\":\"10.1109/IFEEA57288.2022.10037978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper solves the time-optimal path planning for autonomous underwater vehicles (AUVs) in ocean environment with cluttered currents. In this problem, the planner may not find a path in the search space with discrete motion directions, because it leads to a decrease in the available direction. But the search space with the continuous motion directions will greatly increase the computation. To avoid this, the paper presents an approach to improve the lack of discrete motion model by placing Steiner points on each edge. Combining with the ant colony algorithm, the path planner finds the time-optimal path in search space. The effectiveness is verified through simulations using a set of randomly generated current fields.\",\"PeriodicalId\":304779,\"journal\":{\"name\":\"2022 9th International Forum on Electrical Engineering and Automation (IFEEA)\",\"volume\":\"47 10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 9th International Forum on Electrical Engineering and Automation (IFEEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IFEEA57288.2022.10037978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 9th International Forum on Electrical Engineering and Automation (IFEEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IFEEA57288.2022.10037978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AUV Path Planning in Strong Current Fields with Less Computing Time and Improving Incompleteness Problem
This paper solves the time-optimal path planning for autonomous underwater vehicles (AUVs) in ocean environment with cluttered currents. In this problem, the planner may not find a path in the search space with discrete motion directions, because it leads to a decrease in the available direction. But the search space with the continuous motion directions will greatly increase the computation. To avoid this, the paper presents an approach to improve the lack of discrete motion model by placing Steiner points on each edge. Combining with the ant colony algorithm, the path planner finds the time-optimal path in search space. The effectiveness is verified through simulations using a set of randomly generated current fields.