An A2C Algorithm for Dynamic Locking-based Multi-satellite Collaborative Task Planning
A multi-satellite collaborative task planning method based on reinforcement learning algorithm is proposed.This method regards multi-satellite collaborative task planning as multiple dual-satellite collaborative plannings,and uses adjacent dual-satellite locking to share task planning result information.Based on the A2C(advantage actor-critic)reinforcement learning algorithm,the results of dual-satellite task planning are readjusted.To address the complex and changing state space of multi-satellite collaborative task planning,a two-level state space and value evaluation function decision-making method is designed to limit the dimensionality of the state space relied on by reinforcement learning,ensuring that the intelligent agent's task adjustment process does not affect the dimensionality of the state space.In the process of algorithm design,various constraints are considered,and adjustable parameters such as weather,imaging quality,and imaging priority are set as evaluation parameters for the A2C algorithm of reinforcement learning.These adjustable parameters help users customize the decision-making evaluation system.Finally,simulations are conducted to validate the feasibility of the algorithm.The simulation results show that the combination task abandonment rate of the algorithm for multi-satellite collaborative task planning is less than 10%.
Multi-satellite collaborative task planningDual-satellite lockingTask adjustmentTwo-level state spaceReinforcement learning
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