{"title":"基于RRT*FN的路径重规划算法","authors":"Baiming Tong, Qingbao Liu, Chaofan Dai","doi":"10.1109/IAEAC47372.2019.8997746","DOIUrl":null,"url":null,"abstract":"A path replanning algorithm based on RRT*FN(Rapidly-exploring Random Tree Fixed Nodes) is proposed for online local path planning of robot. First, we propose a procedure to reuse the tree from the last planning. Second we design a strategy to balance the exploitation of the old tree and the exploration of the current environment. Finally, the RRT*FN’s strategy is adopted to control the size of the tree. Empirical studies have shown that when the positions of the starting point, the goal and the dynamic obstacles change within a certain range, the proposed algorithm can significantly improve the quality of the solution within a limited time compared to totally starting a new planning using RRT*FN. We also compared the proposed algorithm with the two related replanning algorithms, ORRT* (Online Rapidly-exploring Random Tree*) and RT-RRT* (Real Time Rapidly-exploring Random Tree). The proposed algorithm is better with respect to the time used to find the first feasible path and the cost of the first feasible path.","PeriodicalId":164163,"journal":{"name":"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"58 1-2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A RRT*FN Based Path Replanning Algorithm\",\"authors\":\"Baiming Tong, Qingbao Liu, Chaofan Dai\",\"doi\":\"10.1109/IAEAC47372.2019.8997746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A path replanning algorithm based on RRT*FN(Rapidly-exploring Random Tree Fixed Nodes) is proposed for online local path planning of robot. First, we propose a procedure to reuse the tree from the last planning. Second we design a strategy to balance the exploitation of the old tree and the exploration of the current environment. Finally, the RRT*FN’s strategy is adopted to control the size of the tree. Empirical studies have shown that when the positions of the starting point, the goal and the dynamic obstacles change within a certain range, the proposed algorithm can significantly improve the quality of the solution within a limited time compared to totally starting a new planning using RRT*FN. We also compared the proposed algorithm with the two related replanning algorithms, ORRT* (Online Rapidly-exploring Random Tree*) and RT-RRT* (Real Time Rapidly-exploring Random Tree). The proposed algorithm is better with respect to the time used to find the first feasible path and the cost of the first feasible path.\",\"PeriodicalId\":164163,\"journal\":{\"name\":\"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"volume\":\"58 1-2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAEAC47372.2019.8997746\",\"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 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC47372.2019.8997746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A path replanning algorithm based on RRT*FN(Rapidly-exploring Random Tree Fixed Nodes) is proposed for online local path planning of robot. First, we propose a procedure to reuse the tree from the last planning. Second we design a strategy to balance the exploitation of the old tree and the exploration of the current environment. Finally, the RRT*FN’s strategy is adopted to control the size of the tree. Empirical studies have shown that when the positions of the starting point, the goal and the dynamic obstacles change within a certain range, the proposed algorithm can significantly improve the quality of the solution within a limited time compared to totally starting a new planning using RRT*FN. We also compared the proposed algorithm with the two related replanning algorithms, ORRT* (Online Rapidly-exploring Random Tree*) and RT-RRT* (Real Time Rapidly-exploring Random Tree). The proposed algorithm is better with respect to the time used to find the first feasible path and the cost of the first feasible path.