Synergizing Deep Reinforcement Learning and Sensor Fusion for Enhanced Reliability and Environmental Perception in Spacecraft Systems

This paper proposes a novel integration of deep reinforcement learning (DRL) and multi-modal sensor fusion to address critical challenges in spacecraft autonomy, including real-time decision-making, fault tolerance, and environmental perception in dynamic space environments. By combining DRL's adaptive control capabilities with probabilistic sensor fusion techniques, the framework enhances spacecraft reliability during missions such as orbital debris avoidance, precise docking, and anomaly recovery. The operation of spacecraft and satellites takes place in dynamic and high-risk environments, necessitating robust perception and decision-making capabilities. This paper proposes a novel framework that integrates Deep Reinforcement Learning (DRL) with a particular emphasis on the Deep Deterministic Policy Gradient (DDPG) algorithm, in conjunction with advanced sensor fusion techniques to enhance the reliability and operational performance of satellites and spacecraft. By leveraging and amalgamating multi-modal heterogeneous sensor data (e.g., Inertial Measurement Units (IMU), star trackers, LiDAR) through a Kalman filter, we utilize the resultant fused state estimate as input for a DDPG agent. This approach enables the attainment of adaptive control policies aimed at optimizing trajectory correction, obstacle avoidance, and fault recovery. Furthermore, by applying DDPG for adaptive sensor management and control, we demonstrate significant enhancements in environmental perception and operational robustness, particularly in the context of challenging space conditions. This methodology facilitates improved tracking, navigation, and overall mission success through optimized data utilization and adaptive decision-making. The goal is to improve reliability, environmental perception, and performance in tough conditions.

Keywords:

Spacecraft Machine Learning , Deep Reinforcement Learning , Sensor Fusion