  Over eleven years into its mission, the Mars Science Laboratory remains vital
to NASA's Mars exploration. Safeguarding the rover's long-term functionality is
a top mission priority. In this study, we introduce and test undercomplete
autoencoder models for detecting drive anomalies, using telemetry data from
wheel actuators, the Rover Inertial Measurement Unit (RIMU), and the suspension
system. Our approach enhances post-drive data analysis during tactical downlink
sessions. We explore various model architectures and input features to
understand their impact on performance. Evaluating the models involves testing
them on unseen data to mimic real-world scenarios. Our experiments demonstrate
the undercomplete autoencoder model's effectiveness in detecting drive
anomalies within the Curiosity rover dataset. Remarkably, the model even
identifies subtle anomalous telemetry patterns missed by human operators.
Additionally, we provide insights into optimal design choices by comparing
different model architectures and input features. The model's ability to
capture inconspicuous anomalies, potentially indicating early-stage failures,
holds promise for the field, by improving the reliability and safety of future
planetary exploration missions through early anomaly detection and proactive
maintenance.
