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.

翻译：自任务启动十一年以来，火星科学实验室（火星车）始终是美国国家航空航天局（NASA）火星探索计划的核心装备。保障火星车长期运行能力是任务的首要优先事项。本研究提出并测试了欠完备自编码器模型，利用车轮执行器、火星车惯性测量单元（RIMU）及悬挂系统的遥测数据，用于驱动异常检测。该方法优化了战术下行链路会话中的驱动后数据分析流程。我们探索了不同模型架构与输入特征对性能的影响，并通过在未知数据上测试模型以模拟真实场景来评估其有效性。实验表明，欠完备自编码器模型能有效检测“好奇号”火星车数据集中的驱动异常。值得注意的是，该模型甚至能识别人类操作员遗漏的细微遥测异常模式。此外，通过比较不同模型架构与输入特征，我们提出了最优设计方案的见解。模型对隐蔽异常（可能指示早期故障）的捕获能力，通过实现早期异常检测与主动维护，有望提升未来行星探测任务的可靠性与安全性。