未知环境下无碰撞导航的弹性定时弹性带规划器

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob, Hariharan Arunachalam, Leonardo Guevara, Alexandr Klimchik, Grzegorz Cielniak, Marc Hanheide
{"title":"未知环境下无碰撞导航的弹性定时弹性带规划器","authors":"Geesara Kulathunga,&nbsp;Abdurrahman Yilmaz,&nbsp;Zhuoling Huang,&nbsp;Ibrahim Hroob,&nbsp;Hariharan Arunachalam,&nbsp;Leonardo Guevara,&nbsp;Alexandr Klimchik,&nbsp;Grzegorz Cielniak,&nbsp;Marc Hanheide","doi":"10.1002/rob.22602","DOIUrl":null,"url":null,"abstract":"<p>In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modeled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30% or higher obstacle density, the ratio of free space before and after placing new obstacles, the RESILIENT TIMED ELASTIC BAND (RTEB) planner achieves approximately 20% reduction in traverse distance, traverse time, and control effort compared to the timed elastic band (TEB) planner and nonlinear model predictive control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where efficient and resilient navigation is crucial.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3902-3917"},"PeriodicalIF":5.2000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22602","citationCount":"0","resultStr":"{\"title\":\"Resilient Timed Elastic Band Planner for Collision-Free Navigation in Unknown Environments\",\"authors\":\"Geesara Kulathunga,&nbsp;Abdurrahman Yilmaz,&nbsp;Zhuoling Huang,&nbsp;Ibrahim Hroob,&nbsp;Hariharan Arunachalam,&nbsp;Leonardo Guevara,&nbsp;Alexandr Klimchik,&nbsp;Grzegorz Cielniak,&nbsp;Marc Hanheide\",\"doi\":\"10.1002/rob.22602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modeled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30% or higher obstacle density, the ratio of free space before and after placing new obstacles, the RESILIENT TIMED ELASTIC BAND (RTEB) planner achieves approximately 20% reduction in traverse distance, traverse time, and control effort compared to the timed elastic band (TEB) planner and nonlinear model predictive control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where efficient and resilient navigation is crucial.</p>\",\"PeriodicalId\":192,\"journal\":{\"name\":\"Journal of Field Robotics\",\"volume\":\"42 7\",\"pages\":\"3902-3917\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22602\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Field Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rob.22602\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Field Robotics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rob.22602","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

摘要

在自主导航中,轨迹重规划、细化和控制命令生成是实现有效运动规划的关键。本文提出了一种弹性的轨迹重新规划方法,以解决初始规划者的解决方案变得不可行的情况。该方法采用混合a *算法,在主规划器失效时生成可行轨迹,并采用基于软约束的平滑技术对轨迹进行细化,以确保轨迹的连续性、避障性和运动可行性。障碍物约束使用动态Voronoi地图建模,以改善通过狭窄通道的导航。该方法增强了轨迹规划的一致性,加快了收敛速度,满足了实时性的计算要求。在障碍物密度约为30%或更高的环境中,设置新障碍物前后的自由空间比,弹性定时弹性带(RTEB)规划器与定时弹性带(TEB)规划器和非线性模型预测控制(NMPC)规划器相比,可减少约20%的穿越距离、穿越时间和控制工作量。这些改进证明了RTEB规划器在现场机器人领域的应用潜力,特别是在农业和工业环境中,高效和有弹性的导航至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Resilient Timed Elastic Band Planner for Collision-Free Navigation in Unknown Environments

Resilient Timed Elastic Band Planner for Collision-Free Navigation in Unknown Environments

In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modeled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30% or higher obstacle density, the ratio of free space before and after placing new obstacles, the RESILIENT TIMED ELASTIC BAND (RTEB) planner achieves approximately 20% reduction in traverse distance, traverse time, and control effort compared to the timed elastic band (TEB) planner and nonlinear model predictive control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where efficient and resilient navigation is crucial.

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
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
×
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:604180095
Book学术官方微信