Barefoot Rover MMINTR Material Data

This archive contains test data of the Barefoot Rover wheel on MMINTR material. The Barefoot Rover wheel was used to demonstrate an instrumented wheel concept which utilizes a 2D pressure grid, an electrochemical impedance spectroscopy (EIS) sensor and machine learning (ML) to extract meaningful metrics from the interaction between the wheel and surface terrain. These include continuous slip/skid estimation, balance, and sharpness for engineering applications. Estimates of surface hydration, texture, terrain patterns, and regolith physical properties such as cohesion and angle of internal friction are additionally calculated for science applications. Traditional systems rely on post-processing of visual images and vehicle telemetry to estimate these metrics. Through in-situ sensing, these metrics can be calculated in near real time and made available to onboard science and engineering autonomy applications. This work aims to provide a deployable system for future planetary exploration missions to increase science and engineering capabilities through increased knowledge of the terrain. More detailed information about data structures, material properties and test configurations can be found here: https://github.com/JPLMLIA/Barefoot_Rover/tree/master/data本数据集档案包含裸足漫游者（Barefoot Rover）车轮在MMINTR材料上的测试数据。该裸足漫游者车轮用于演示一款传感轮概念，该传感轮借助二维压力网格、电化学阻抗光谱（EIS）传感器与机器学习（ML）技术，从车轮与地表地形的交互过程中提取有价值的性能指标。此类指标涵盖面向工程应用的连续滑转/滑移估算、轮体平衡状态及轮面抓附锐度；此外，还针对科学研究场景计算得到地表湿度、质地、地形形态以及风化层物理特性（如内聚强度与内摩擦角）的估算值。传统系统需依托视觉图像与车辆遥测数据的后处理流程来估算此类指标。通过原位传感技术，上述指标可近乎实时地完成计算，并可供车载科学与工程自主应用调用。本研究旨在为未来行星探测任务提供一套可部署系统，通过深化对地形的认知以提升科学与工程作业能力。有关数据结构、材料特性与测试配置的更多详细信息，请访问：https://github.com/JPLMLIA/Barefoot_Rover/tree/master/data