Artificial Intelligence for Trusted Autonomous Satellite Operations

Recent advances in Artificial Intelligence (AI) and Cyber-Physical Systems (CPS) for aerospace applications have brought about new opportunities for the fast-growing satellite industry. The progressive introduction of connected satellite systems and associated mission concepts is stimulating the development of intelligent CPS (iCPS) architectures, which can support high levels of flexibility and resilience in an increasingly congested near-Earth space environment. The need for higher levels of automation and autonomy in satellite operations has stimulated numerous research initiatives in recent years, focusing on the progressive enhancement of systemic performance (e.g., addressing safety, integrity and cyber-physical security metrics) and associated monitoring/augmentation approaches that can support Trusted Autonomous Satellite Operations (TASO). Despite these advances, in most contemporary satellite platforms, autonomy is restricted to a specific set of rules and cases, while the transition to TASO requires a paradigm shift in the design of both space vehicles and ground-based systems. In particular, the use of AI is seen as an essential enabler for TASO as it enhances system performance/adaptability and supports both predictive and reactive integrity augmentation capabilities, especially in Distributed Satellite Systems (DSS). This article provides a critical review of AI for satellite operations, with a special focus on current and likely future DSS architectures for communication, navigation and remote sensing missions. The aim is to identify key contemporary challenges and opportunities associated with space iCPS design methodologies to enhance the performance and resilience of satellite systems, supporting the progressive transition to TASO. A comprehensive review of relevant AI techniques is presented to critically assess the potential benefits and challenges of each method for different space applications. After describing the specificities of DSS and the opportunities offered by iCPS architectures, the co-evolution of space and control (ground and on-board) segments is highlighted as an essential next step towards enabling TASO. As an integral part of this evolutionary approach, the most important legal and regulatory challenges associated with the adoption of AI in TASO are also discussed.