Barefoot Rover: A Sensor-Embedded Rover Wheel Demonstrating In-Situ Engineering and Science Extractions Using Machine Learning
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Engineering topics
Publications and source records attributed to Lightholder, Jack.
No abstract provided
In this work, we 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.
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We present a collection of techniques designed for mitigating limited pointing stability for target acquisition, onboard image calibration and decision making, in a low bandwidth environment. These technologies will fly on the Near Earth Asteroid Scout (NEA Scout) interplanetary CubeSat mission in 2019. To support verification and validation, the flight software implementations of these algorithms have been run on New Horizons, Rosetta and terrestrially acquired data sets. These experiments validate the capability to detect the target, through noise and target location uncertainty.
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