Enhancing Rover Mobility Monitoring: Autoencoder-driven Anomaly Detection for Curiosity

TL;DR

This paper presents an undercomplete autoencoder approach for detecting drive anomalies in the Curiosity rover, improving early failure detection and enhancing mission safety through analysis of telemetry data.

Contribution

It introduces and evaluates autoencoder models tailored for anomaly detection in rover telemetry, demonstrating their effectiveness in real-world Mars exploration data.

Findings

Autoencoder models effectively detect drive anomalies in rover telemetry.

Models identify subtle anomalies missed by human operators.

Insights into optimal model architectures and features for anomaly detection.

Abstract

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.