{"title":"基于改进A*的特殊环境下移动机器人路径规划研究","authors":"Ren Yiyue, Xiaoru Song, Gao Song","doi":"10.1109/ISASS.2019.8757721","DOIUrl":null,"url":null,"abstract":"Aiming at the problems of A* algorithm in a large environment with density of obstacles, such as large memory overheads, long calculation times, an novel method based on the fusion of static weight method and jump point search is proposed in this paper. Static weight method improves heuristic function, then the search space is limited and brute force search is reduced. Jump point search filters key points, then unnecessary nodes are reduced and search speed is accelerated The experimental results demonstrate that compared with traditional A* algorithm, the number of invisited nodes is reduced, and the search speed is accelerated. Therefore, the proposed algorithm can meet the requirements of fast path finding in special environments.","PeriodicalId":359959,"journal":{"name":"2019 3rd International Symposium on Autonomous Systems (ISAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Research on Path Planning of Mobile Robot Based on Improved A* in Special Environment\",\"authors\":\"Ren Yiyue, Xiaoru Song, Gao Song\",\"doi\":\"10.1109/ISASS.2019.8757721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problems of A* algorithm in a large environment with density of obstacles, such as large memory overheads, long calculation times, an novel method based on the fusion of static weight method and jump point search is proposed in this paper. Static weight method improves heuristic function, then the search space is limited and brute force search is reduced. Jump point search filters key points, then unnecessary nodes are reduced and search speed is accelerated The experimental results demonstrate that compared with traditional A* algorithm, the number of invisited nodes is reduced, and the search speed is accelerated. Therefore, the proposed algorithm can meet the requirements of fast path finding in special environments.\",\"PeriodicalId\":359959,\"journal\":{\"name\":\"2019 3rd International Symposium on Autonomous Systems (ISAS)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 3rd International Symposium on Autonomous Systems (ISAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISASS.2019.8757721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Symposium on Autonomous Systems (ISAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISASS.2019.8757721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Path Planning of Mobile Robot Based on Improved A* in Special Environment
Aiming at the problems of A* algorithm in a large environment with density of obstacles, such as large memory overheads, long calculation times, an novel method based on the fusion of static weight method and jump point search is proposed in this paper. Static weight method improves heuristic function, then the search space is limited and brute force search is reduced. Jump point search filters key points, then unnecessary nodes are reduced and search speed is accelerated The experimental results demonstrate that compared with traditional A* algorithm, the number of invisited nodes is reduced, and the search speed is accelerated. Therefore, the proposed algorithm can meet the requirements of fast path finding in special environments.