Double-Layer RRT* Objective Bias Anytime Motion Planning Algorithm

Robotics Pub Date : 2024-03-01 DOI:10.3390/robotics13030041
Hamada Esmaiel, Guolin Zhao, Zeyad A. H. Qasem, Jie Qi, Haixin Sun
{"title":"Double-Layer RRT* Objective Bias Anytime Motion Planning Algorithm","authors":"Hamada Esmaiel, Guolin Zhao, Zeyad A. H. Qasem, Jie Qi, Haixin Sun","doi":"10.3390/robotics13030041","DOIUrl":null,"url":null,"abstract":"This paper proposes a double-layer structure RRT* algorithm based on objective bias called DOB-RRT*. The algorithm adopts an initial path with an online optimization structure for motion planning. The first layer of RRT* introduces a feedback-based objective bias strategy with segment forward pruning processing to quickly obtain a smooth initial path. The second layer of RRT* uses the heuristics of the initial tree structure to optimize the path by using reverse maintenance strategies. Compared with conventional RRT and RRT* algorithms, the proposed algorithm can obtain the initial path with high quality, and it can quickly converge to the progressive optimal path during the optimization process. The performance of the proposed algorithm is effectively evaluated and tested in real experiments on an actual wheeled robotic vehicle running ROS Kinetic in a real environment.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/robotics13030041","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This paper proposes a double-layer structure RRT* algorithm based on objective bias called DOB-RRT*. The algorithm adopts an initial path with an online optimization structure for motion planning. The first layer of RRT* introduces a feedback-based objective bias strategy with segment forward pruning processing to quickly obtain a smooth initial path. The second layer of RRT* uses the heuristics of the initial tree structure to optimize the path by using reverse maintenance strategies. Compared with conventional RRT and RRT* algorithms, the proposed algorithm can obtain the initial path with high quality, and it can quickly converge to the progressive optimal path during the optimization process. The performance of the proposed algorithm is effectively evaluated and tested in real experiments on an actual wheeled robotic vehicle running ROS Kinetic in a real environment.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
双层 RRT* 目标偏差随时运动规划算法
本文提出了一种基于目标偏置的双层结构 RRT* 算法,称为 DOB-RRT*。该算法采用在线优化结构的初始路径进行运动规划。RRT* 的第一层引入了基于反馈的目标偏置策略,并进行分段前向剪枝处理,以快速获得平滑的初始路径。RRT* 的第二层利用初始树结构的启发式方法,通过反向维护策略来优化路径。与传统的 RRT 算法和 RRT* 算法相比,所提出的算法能获得高质量的初始路径,并能在优化过程中快速收敛到渐进最优路径。在真实环境下运行 ROS Kinetic 的实际轮式机器人车辆上,对所提出算法的性能进行了有效评估和实际实验测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Learning to Walk with Adaptive Feet Nonbinary Voices for Digital Assistants—An Investigation of User Perceptions and Gender Stereotypes The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare A Reconfigurable UGV for Modular and Flexible Inspection Tasks in Nuclear Sites Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1