Satellite Autonomous Task Decision-Making Method Based on Attention Pointer Network

Large-scale satellite constellation on-orbit missions are characterized by multiple objectives, multiple constraints, and strong dynamism. Traditional ground control and management modes have become difficult to meet the requirements for autonomous real-time decision-making by satellites. This paper addresses the need for autonomous and rapid decision-making capabilities on-board satellites, and proposes a satellite autonomous task planning and decision-making method based on an attention pointer network, addressing issues such as limited on-orbit computing resources and insufficient generalization of planning methods. Firstly, by analyzing and mathematically describing the characteristics and constraints of satellite tasks, a Markov model for satellite autonomous task planning problems is constructed. Secondly, the pointer network is combined with a reinforcement learning framework, and an attention mechanism is introduced to perform probabilistic learning on the execution selection of satellite tasks, generating an optimized sequence of satellite task planning results. Finally, simulation experiments show that after training, this method exhibits certain generalization capabilities and significantly superior computational efficiency compared to traditional intelligent optimization algorithms such as genetic algorithms. It can adapt to the single-satellite autonomous dynamic planning requirements under large-scale constellation control and management, providing technical support for autonomous operation and online intelligent task planning of satellites in orbit.