Barefoot Rover MM2MM Material Data

This archive contains test data of the Barefoot Rover wheel on MM2MM 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本存档包含MM2MM材质下的赤足漫游者（Barefoot Rover）车轮测试数据。赤足漫游者（Barefoot Rover）车轮被用于演示一种智能化测力车轮概念，该概念借助二维压力网格、电化学阻抗谱（electrochemical impedance spectroscopy, EIS）传感器与机器学习（machine learning, ML）技术，从车轮与地表地形的交互过程中提取有效量化指标。针对工程应用场景，该技术可输出连续滑移/打滑量估算、平衡状态及地形锐度指标；针对科学应用场景，还可额外估算地表湿度、纹理、地形模式，以及风化层的内聚力、内摩擦角等物理属性。传统系统需依靠视觉图像后处理与车辆遥测数据来估算上述量化指标，而通过原位传感技术，上述指标可实现近乎实时的计算，并可向车载科学与工程自主应用模块开放调用。本研究旨在为未来行星探测任务提供一套可部署系统，通过深化对地况的认知，提升科学与工程作业能力。有关数据结构、材质属性与测试配置的更多详细信息，请参阅：https://github.com/JPLMLIA/Barefoot_Rover/tree/master/data