跟踪地面：农业环境中机器人诱发土壤变形的在线激光雷达识别
Tracking the Ground: Online Lidar Identification of Robot-Induced Soil Deformation in Agricultural Environments
- Université Clermont Auvergne（克莱蒙奥弗涅大学）
- INRAE（法国国家农业、食品与环境研究院）
- Institut Pascal, Université Clermont Auvergne（帕斯卡研究所，克莱蒙奥弗涅大学）
- Clermont Auvergne INP（克莱蒙奥弗涅国立理工学院）
- CNRS（法国国家科学研究中心）
- National Agriculture and Food Research Organization（日本国立农业与食品研究机构）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对农业中车辆-土壤相互作用导致土壤变形的问题，提出基于激光雷达的降阶参数模型框架，在线量化并估计变形演变，实验验证其有效性，为土壤感知机器人作业奠定基础。
AI中文摘要：
农业面临诸多挑战，机器人系统通过提高田间作业的效率和可持续性，在应对这些挑战中可以发挥重要作用。在这些挑战中，保护土壤健康是一个关键问题，因为车辆与土壤的相互作用会破坏土壤结构并产生不必要的表面变形。迈向土壤感知机器人的关键一步是明确考虑车辆通行如何使地面变形，然而土壤状态通常不被视为一个变量。我们通过提出一个框架来解决这一差距，该框架用于量化交通诱发的土壤变形，并从激光雷达观测中在线估计其演变。该方法依赖于一个降阶参数模型，该模型通过物理可解释的参数表示土壤行为，从而产生一个持续更新且可观测的土壤状态表示。在不同土壤条件下进行的实验证明了该方法捕捉机器人诱发变形的能力。通过使土壤响应在作业过程中可测量和可解释，所提出的框架为土壤感知机器人作业奠定了基础，其中估计的状态可用于调整机器人行为以减少土壤退化。
英文摘要：
Agriculture faces many challenges, and robotic systems can play an important role in addressing them by improving the efficiency and sustainability of field operations. Among these challenges, preserving soil health is a critical concern, as vehicle-soil interactions can degrade the soil structure and produce unwanted surface deformation. A key step toward soil-aware robotics is to explicitly account for how vehicle traffic deforms the ground, yet soil state is typically not treated as a variable. We address this gap by proposing a framework to quantify traffic-induced soil deformation and estimate its evolution online from lidar observations. The method relies on a reduced-order parametric model that represents the soil behavior via physically interpretable parameters, yielding a continuously updated and observable representation of soil state. Experiments conducted in different soil conditions demonstrate the ability of the approach to capture deformation induced by the robot. By making soil response measurable and interpretable during operation, the proposed framework establishes a basis for soil-aware robotic operation, in which the estimated state can be exploited to adapt robotic behaviors in order to reduce soil degradation.
