当障碍物弯曲：野外机器人场景下的植被变形建模
When Obstacles Bend: Modeling Vegetation Deformation in the context of Field Robotics
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中文总结 AI 辅助
针对现有可通行性评估绑定机器人动力学、难以跨平台迁移的局限，该研究提出通过植被固有力学特性建模，结合变形与力数据实现植被感知导航。
中文摘要 AI 辅助
在自然环境中运行的自主机器人往往需要与植被互动，而非仅仅避开它。在此场景下，可通行性通常从机器人的视角定义，通过测量特定平台在环境中移动时的响应来确定。这种方法虽实用，却将环境评估与机器人自身动力学特性绑定，导致所得特性难以在不同平台间迁移。更重要的是，它无法直接反映植被本身的属性，而这些属性才是农业、环境监测等应用中互动与潜在损害的真正来源。为解决这一局限，我们提出通过植被的固有力学特性对其进行表征，且该表征与任何特定机器人无关。将变形测量与接触力数据相结合，我们估算出潜在的力学参数，并重建植被对互动的响应。这使得基于固有环境特性而非依赖平台的指标，实现感知植被的导航成为可能。
英文摘要
Autonomous robots operating in natural environments must often interact with vegetation rather than simply avoid it. In this context, traversability is typically defined from the robot's perspective, by measuring how a specific platform responds when moving through the environment. While practical, this viewpoint entangles the assessment of the environment with the robot's own dynamics, making the resulting characterization difficult to transfer across different platforms. More importantly, it does not directly reflect the properties of the vegetation itself, which are the true source of interaction and potential damage in applications such as agriculture and environmental monitoring. To address this limitation, we propose to characterize vegetation through its intrinsic mechanical properties, independently of any specific robot. By combining deformation measurements with contact force data, we estimate the underlying mechanical parameters and reconstruct the vegetation's response to interaction. This enables vegetation-aware navigation based on intrinsic environmental properties rather than platform-dependent metrics.
发表机构
- LIMOS（利摩日信息学与优化实验室）
- Université Clermont Auvergne（克莱蒙奥弗涅大学）
- Clermont Auvergne INP（克莱蒙奥弗涅国立综合理工学院）
- CNRS（法国国家科学研究中心）
- INRAE（法国国家农业食品与环境研究院）
- Institut Pascal（帕斯卡尔研究院）
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