LSF-planner: a visual local planner for legged robots based on ground structure and feature information

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Teng Zhang, Xiangji Wang, Fusheng Zha, Fucheng Liu
{"title":"LSF-planner: a visual local planner for legged robots based on ground structure and feature information","authors":"Teng Zhang,&nbsp;Xiangji Wang,&nbsp;Fusheng Zha,&nbsp;Fucheng Liu","doi":"10.1007/s10514-025-10195-7","DOIUrl":null,"url":null,"abstract":"<div><p>Three-dimensional navigation of legged robots is crucial for field exploration and post-disaster rescue. Existing optimization-based local trajectory planners predominantly focus on obstacle avoidance, neglecting negative obstacles (e.g., pits) and varying ground features (e.g., different terrain types). Additionally, non-overlapping areas between the planned space in three-dimensional trajectory planning and the robot’s actual reachable space lead to decision-making issues between crossing and obstacle avoidance, making it challenging to differentiate between passable and hazardous areas, thus impacting navigation safety and stability. To address these limitations, we propose a novel visual local planner, LSF-Planner (Visual Local Planner for Legged Robots Based on Ground Structure and Feature Information). The LSF-Planner employs a multi-layer local perception map that integrates ground feature semantics, sensor range, and negative obstacles (e.g., voids detected by depth sensors) to construct a ground reliability representation. The Label2Grad method is introduced to convert this representation into gradient layers, incorporating a ground reliability penalty function into trajectory optimization. By incorporating constraints on the center of mass height and crossing angles, LSF-Planner effectively differentiates between traversable and hazardous areas. Experimental results show that LSF-Planner significantly outperforms existing methods in 3D trajectory planning, enhancing the navigation performance of legged robots in unstructured environments.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"49 2","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Robots","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10514-025-10195-7","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Three-dimensional navigation of legged robots is crucial for field exploration and post-disaster rescue. Existing optimization-based local trajectory planners predominantly focus on obstacle avoidance, neglecting negative obstacles (e.g., pits) and varying ground features (e.g., different terrain types). Additionally, non-overlapping areas between the planned space in three-dimensional trajectory planning and the robot’s actual reachable space lead to decision-making issues between crossing and obstacle avoidance, making it challenging to differentiate between passable and hazardous areas, thus impacting navigation safety and stability. To address these limitations, we propose a novel visual local planner, LSF-Planner (Visual Local Planner for Legged Robots Based on Ground Structure and Feature Information). The LSF-Planner employs a multi-layer local perception map that integrates ground feature semantics, sensor range, and negative obstacles (e.g., voids detected by depth sensors) to construct a ground reliability representation. The Label2Grad method is introduced to convert this representation into gradient layers, incorporating a ground reliability penalty function into trajectory optimization. By incorporating constraints on the center of mass height and crossing angles, LSF-Planner effectively differentiates between traversable and hazardous areas. Experimental results show that LSF-Planner significantly outperforms existing methods in 3D trajectory planning, enhancing the navigation performance of legged robots in unstructured environments.

LSF-planner:基于地面结构和特征信息的有腿机器人视觉局部规划器
有腿机器人的三维导航对于野外勘探和灾后救援至关重要。现有的基于优化的局部轨迹规划主要关注避障,忽略了负面障碍物(如坑)和不同的地面特征(如不同的地形类型)。此外,三维轨迹规划中的规划空间与机器人实际可达空间之间的非重叠区域导致了穿越和避障的决策问题,使得区分可通过区域和危险区域变得困难,从而影响了导航的安全性和稳定性。为了解决这些限制,我们提出了一种新的视觉局部规划器,LSF-Planner(基于地面结构和特征信息的有腿机器人视觉局部规划器)。LSF-Planner采用多层局部感知图,该图集成了地面特征语义、传感器范围和负面障碍物(例如,深度传感器检测到的空洞),以构建地面可靠性表示。引入Label2Grad方法将此表示转换为梯度层,并将地面可靠性惩罚函数纳入轨迹优化。通过结合质心高度和交叉角度的约束,LSF-Planner可以有效区分可穿越区域和危险区域。实验结果表明,LSF-Planner在三维轨迹规划方面明显优于现有方法,提高了有腿机器人在非结构化环境中的导航性能。
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 求助全文
来源期刊
Autonomous Robots
Autonomous Robots 工程技术-机器人学
CiteScore
7.90
自引率
5.70%
发文量
46
审稿时长
3 months
期刊介绍: Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.
×
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