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Barefoot Rover MINS30 Material Data

<p>This archive contains test data of the Barefoot Rover wheel on MINS30&nbsp;material.</p> <p>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.</p> <p>More detailed information about data structures, material properties&nbsp;and test configurations can be found here:&nbsp;https://github.com/JPLMLIA/Barefoot_Rover/tree/master/data</p>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4

Topics

Dataset details

Repository
zenodo
Published
June 28, 2021
License
cc-by-4.0
DOI
10.5281/zenodo.5043995

Scored with release 0.9.0

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