基于RRT*FN的路径重规划算法

Baiming Tong, Qingbao Liu, Chaofan Dai
{"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}
引用次数: 7

摘要

针对机器人在线局部路径规划问题,提出了一种基于快速探索随机树固定节点(RRT*FN)的路径重规划算法。首先,我们提出了一个从上次规划中重用树的过程。其次,我们设计了一种策略来平衡对老树的开发和对当前环境的开发。最后,采用RRT*FN的策略控制树的大小。实证研究表明,当起点、目标和动态障碍物的位置在一定范围内发生变化时,与使用RRT*FN完全开始一个新的规划相比,所提出的算法可以在有限时间内显著提高解决方案的质量。我们还将提出的算法与两种相关的重规划算法ORRT*(在线快速探索随机树*)和RT-RRT*(实时快速探索随机树)进行了比较。本文提出的算法在寻找第一条可行路径的时间和第一条可行路径的代价方面都有较好的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A RRT*FN Based Path Replanning Algorithm
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信