Understanding Drill Data for Autonomous Application

In high-risk, high-cost environments like Mars it is necessary for robotic agents to either respond to or reason through potential indicators of trouble before they escalate to problems that effectively mean mission failure. Currently, no broadly applicable solution exists to give a complicated and specialized agent like The Regolith and Ice Drill for Exploring New Terrain (TRIDENT) the ability to understand the rate of its progress on a task or the situational awareness to know when a situation might escalate to a drilling fault. We examined logged data from previous field experiences to better understand potential drilling faults TRIDENT will have to reason through. We applied time series analysis techniques to determine what trends in the data exist during faults and if change point analysis or other machine learning methods could be used to predict faults of the drill.