Towards an Intelligent Health and Mission Management Framework for Autonomous Systems

As the complexity and deployment of autonomous systems expand across sectors such as aerospace and defense, the need for robust and adaptive frameworks ensuring mission success and system reliability grows. Traditional fault detection and mission planning methods often fail to manage real-time anomalies and uncertainties, leading to potential mission failures and significant resource losses. Moreover, many machine learning models are opaque, offering predictions without the interpretability required for high-stakes decision-making environments. This paper discusses an Intelligent Health and Mission Management (IHMM) framework aimed at addressing these challenges by combining the interpretability of physics-based models with the predictive power of machine learning. The hybrid IHMM approach integrates physics-based models with machine learning algorithms trained on historical mission data, ensuring accurate and interpretable predictions. Optimization algorithms and robust control techniques help with real-time mission management, while machine learning components adapt dynamically to unforeseen events, continuously improving decision-making processes. Initial simulations demonstrate the efficacy of the framework, and promises it ability to handle diverse operational challenges, improve predictive accuracy, and provide clear explanations of model behavior. By prioritizing both accuracy and interpretability, an IHMM framework can help maintain reliability, transparency, and adaptability of systems, enabling successful missions across various sectors and fostering trust in their deployment in critical scenarios.