Risk-Aware Task Allocation for LEO Satellite Constellations Using Deep Reinforcement Learning, Fuzzy Inference, and K-Means Clustering

Abstract

The explosive expansion of Low Earth Orbit (LEO) satellite mega-constellations presents critical challenges in operational risk management, real-time task allocation, and dynamic resource management due to their inherent time-varying topology and systemic uncertainties. To overcome the limitations of traditional optimization methods, this paper proposes a novel, hybrid artificial intelligence framework integrating K-means Clustering, Fuzzy Logic (FL), and Deep Reinforcement Learning for Task Allocation (DRL-TA). First, an adaptive K-means clustering mechanism periodically segments the constellation based on real-time channel quality and traffic load, effectively reducing network state dimensionality and overhead for the DRL agent. Second, a Fuzzy Inference System is employed to model non-deterministic operational elements (e.g., temperature, orbital deviation) and predict the real-time Safety Risk Score 〖Fuzzy〗_risk. This interpretable risk score is integrated as a penalty term within the DRL agent's reward function. Finally, the DRL-TA algorithm learns the optimal policy for computationally offloading and resource allocation by jointly analyzing the clustered network state and the fuzzy-predicted risk. Validated on a simulated 1,000-satellite LEO constellation over 10,000 training episodes, the integrated DRL-TA framework demonstrates significant performance gains: achieving a 25% reduction in average task completion delay and a 15% improvement in overall task success rate compared to conventional load-balancing and pure DRL baseline methods. The DRL policy exhibited stable convergence within 8,500 episodes, with a final average episodic return accuracy exceeding 97% of the theoretical maximum. This demonstrates the framework's efficacy in creating a reliable, high-performance, and risk-aware LEO edge computing environment.