{"title":"基于时空高效改进A*算法的移动机器人路径规划","authors":"H. Zhao, Shanmei Liu","doi":"10.1109/ISPDS56360.2022.9874234","DOIUrl":null,"url":null,"abstract":"Aiming to solve the problems of low search efficiency, more redundant nodes and path turning points in the traditional A* algorithm for mobile robot path planning, an improved time and space efficient A* algorithm called $\\mathbf{TSE}_{-}\\mathbf{A}^{\\ast}$ algorithm is proposed. Firstly, an adaptive heuristic function is designed according to the number of environmental obstacles, the starting point and the ending point of the path, which makes the algorithm perform well in different environments. Then, by optimizing the node selection strategy, we can improve the efficiency of the algorithm and reduce the running time of the algorithm, reduce redundant nodes and optimize the path, so as to make the path more smooth. The results show that the proposed $\\mathbf{TSE}_{-}\\mathbf{A}^{\\ast}$ algorithm is much better than the traditional $\\mathbf{A}^{\\ast}$ and the Time-Efficient $\\mathbf{A}^{\\ast}$ algorithm not only in path length and the number of turning points, but also in search time.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Path planning for mobile robots based on the Time&Space-Efficient improved A* algorithm\",\"authors\":\"H. Zhao, Shanmei Liu\",\"doi\":\"10.1109/ISPDS56360.2022.9874234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming to solve the problems of low search efficiency, more redundant nodes and path turning points in the traditional A* algorithm for mobile robot path planning, an improved time and space efficient A* algorithm called $\\\\mathbf{TSE}_{-}\\\\mathbf{A}^{\\\\ast}$ algorithm is proposed. Firstly, an adaptive heuristic function is designed according to the number of environmental obstacles, the starting point and the ending point of the path, which makes the algorithm perform well in different environments. Then, by optimizing the node selection strategy, we can improve the efficiency of the algorithm and reduce the running time of the algorithm, reduce redundant nodes and optimize the path, so as to make the path more smooth. The results show that the proposed $\\\\mathbf{TSE}_{-}\\\\mathbf{A}^{\\\\ast}$ algorithm is much better than the traditional $\\\\mathbf{A}^{\\\\ast}$ and the Time-Efficient $\\\\mathbf{A}^{\\\\ast}$ algorithm not only in path length and the number of turning points, but also in search time.\",\"PeriodicalId\":280244,\"journal\":{\"name\":\"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPDS56360.2022.9874234\",\"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 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPDS56360.2022.9874234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Path planning for mobile robots based on the Time&Space-Efficient improved A* algorithm
Aiming to solve the problems of low search efficiency, more redundant nodes and path turning points in the traditional A* algorithm for mobile robot path planning, an improved time and space efficient A* algorithm called $\mathbf{TSE}_{-}\mathbf{A}^{\ast}$ algorithm is proposed. Firstly, an adaptive heuristic function is designed according to the number of environmental obstacles, the starting point and the ending point of the path, which makes the algorithm perform well in different environments. Then, by optimizing the node selection strategy, we can improve the efficiency of the algorithm and reduce the running time of the algorithm, reduce redundant nodes and optimize the path, so as to make the path more smooth. The results show that the proposed $\mathbf{TSE}_{-}\mathbf{A}^{\ast}$ algorithm is much better than the traditional $\mathbf{A}^{\ast}$ and the Time-Efficient $\mathbf{A}^{\ast}$ algorithm not only in path length and the number of turning points, but also in search time.