Subsurface Planetary Drilling Fault and Anomaly Detection

Sarah Boelter, Greta Brown, Rene Mai, Ebasa Temesgen, Lucas Weber, Thomas Stucky, Brian Glass, Maria Gini

Keywords: Field Robotics, Planetary Drilling, Subsurface State Estimation, Fault Detection, Space Robotics

TL;DR: Methods to make planetary drills more aware of their subsurface environment to better enable future drilling autonomy, tested in a lab, the Canadian Arctic, and Antarctica.

Abstract: In extraterrestrial planetary environments, environmental, computing, energy, and operational constraints require robotic agents to complete tasks without human supervision. Specialized extraterrestrial robotic drilling agents currently lack autonomous drilling capabilities, primarily because research into subsurface environment representation for autonomous drilling remains in its early stages. We validate work using online model-free time-series subspace analysis methods for noisy and sparse data to estimate drilling faults from drill avionics telemetry in Haughton Crater in the Canadian High Arctic. We also introduce a multivariate subsurface anomaly detection method that leverages sensor fusion of avionics telemetry evaluated on planetary analogs in Antarctica.

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Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Paper Acceptance: Yes

Submission Number: 3