Engineering topics
Marchetti, Yuliya
Publications and source records attributed to Marchetti, Yuliya.
Federated Scheduling of Model-Driven Observations for Earth Science
No abstract provided
Static and Dynamic Inputs to OCO-2/3 Calibration: Preflight & Inflight Combined, Coefficients Updated Weekly
No abstract provided
Barefoot Rover: A Sensor-Embedded Rover Wheel Demonstrating In-Situ Engineering and Science Extractions Using Machine Learning
No abstract provided
GOSAT - OCO - TROPOMI Virtual Joint Science Team Meeting at AGU - OCO-2 and OCO-3
UNKNOWN
POISE: Planned Observations and Intelligent Science Experimentation
No abstract provided
Barefoot Rover: a Sensor-Embedded Rover Wheel Demonstrating In-Situ Engineering and Science Extractions using Machine Learning
No abstract provided
Adaptive, Model-driven Observation for Earth Science: Planned Observations and Intelligent Science Experimentation (POISE)
No abstract provided
Oco-2 Calibration Refinement Across Versions And Plans For Oco-3
The third major reprocessing campaign for the Orbiting Carbon Observatory 2 mission is underway. With each release, several aspects of instrument calibration were improved. An onboard lamp tracks relative gain degradation, but ages too quickly to track absolute scaling. The most significant changes between builds were evolving assumptions about the stability of the solar calibrator. For the newest build, the trend in lunar measurements over a 4.5-year time series was applied. Another significant change was to expand a list of detector outliers via review of dark, lamp, and science measurements. More subtle adjustments to stray light and spectral dispersion were also performed. The techniques developed for OCO-2 are being applied to the Orbiting Carbon Observatory 3, which completed in-orbit checkout in August 2019. OCO-3 does not have a solar calibrator, and will need to constrain lamp aging via secondary lamps and comparison to other satellites.
Barefoot Rover: a Sensor-Infused Rover Wheel Demonstrating In-Situ Engineering and Science Extractions using Machine Learning
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.