Engineering topics
Publications and source records attributed to John Swatkowski.
Developed a data pipeline utilizing a commercial Machine Learning platform to potentially improve the speed in which recommendations are made for continued use of EMU gloves on ISS; demonstrating the use of ISS as a testbed for further, deep space applications.
The purpose of this Extravehicular Activity (EVA) Office specification is to define the types of Programmatic-level standards to be used for design, manufacturing, testing, and quality control for the Exploration EVA (xEVA) System. The xEVA system includes both space suits and EVA hardware elements (e. EVA Tools, Airlock hardware, etc.). Many of the standards referenced in this document can be substituted for similar standards used in industry; this document provides the scope of which standards need to be considered. This document is the EVA-centric implementation of Chapter 3 of NPR 7120.10, Technical Standards for National Aeronautics and Space Administration (NASA) Programs and Projects: Select technical standards for use as program/project requirements. This also addresses the intent of Section 2.2.6 of NPR 8705.2, Human-Rating Requirements for Space Systems.
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The Extravehicular Mobility Unit (EMU) Glove Machine Learning Inspection project utilizes machine learning to expedite the inspection, analysis, and recommendation for continued use of space suit gloves post spacewalks. Today, ISS glove photos are individually reviewed by a team of experts to determine the conditions of space suit gloves. For this project the Microsoft Azure platform is used to perform Automated Machine Learning (AutoML) to detect issues with tagged images from previous Extravehicular Activities (EVA’s) to build a predictive model. The model analyzes a test image and deems the glove GO or NO-GO for additional EVA’s. The goal for this ML project is to decrease the time spent reviewing images by ground personnel and crewmembers in high frequency EVA locations such as the Moon and Mars. For destinations such as the Moon and Mars the goal is to give crew autonomy in determining glove conditions with limited support from Earth. This paper will outline the results to date and future work needed to expand the capability for in-situ recommendations.