Model-Free Subsurface Anomaly Detection Using Subspace Analysis Techniques for Sparse Telemetry for Extraterrestrial Drilling Robots

Abstract

In extraterrestrial planetary environments, computing, energy, and environmental constraints require robotic agents to complete tasks unsupervised. For specialized extraterrestrial robotic drilling agents there is no broadly applicable solution to detect drilling faults as they happen, before the fault escalates to hardware failure. We build upon previous work with time-series subspace analysis methods to to estimate drilling faults using drill avionics telemetry. This work introduces a subsurface anomaly detection method for planetary drilling robots and further evaluates the robustness of our time-series subspace analysis method. We implemented this novel fault and anomaly detection method on an extraterrestrial drilling robot and evaluated it first in a controlled lab environment with composite materials and then in a Mars planetary analog site in the Canadian High Arctic.