空间控制技术与应用2025，Vol.51Issue(4)：78-87,10.DOI:10.3969/j.issn.1674-1579.2025.04.007
面向轨道威胁规避的对地观测卫星自主任务调度
EOS Autonomous Mission Scheduling with Orbital Threat Avoidance
摘要
Abstract
This paper investigates the autonomous scheduling problem for Earth observation satellite(EOS)imaging missions under orbital threats.First,within the increasingly complex space environment,typical scenarios of EOS encountering orbital threats are analyzed.Based on the urgency level of threats and their avoidance deadlines,a scheduling optimization model is established to minimize the impact of avoidance maneuvers on imaging mission execution.Then,a reinforcement learning(RL)-based self-learning genetic algorithm(GA)is proposed.In GA,decision variables of mission are encoded as the chromosomes.The solution space is effectively explored through the crossover,mutation,and selection operations of the GA to approximate the optimal solution.The performance of GA is seriously affected by the probabilities of crossover and mutation that are difficult to tune manually.To address this problem,an RL-based framework is introduced to adaptively adjust these parameters.The state representation,action selection strategy,and reward function are designed within the framework.Additionally,a two-stage learning strategy intergrating state-action-reward-state-action(SARSA)and Q-learning algorithm is proposed via predefined transition conditions.Finally,simulation results demonstrate the algorithm's effectiveness in balancing the conflicts between imaging and avoidance missions.The algorithm successfully achieves a trade-off between the two types of mission.关键词
对地观测卫星/轨道威胁/任务调度/遗传算法/强化学习Key words
Earth observation satellite/orbital threat/mission scheduling/genetic algorithm/reinforcement learning分类
航空航天引用本文复制引用
陈兴文,杨佳鸣,邱剑彬,王桐..面向轨道威胁规避的对地观测卫星自主任务调度[J].空间控制技术与应用,2025,51(4):78-87,10.基金项目
国家自然科学基金资助项目(U21B6001) National Natural Science Foundation of China(U21B6001). （U21B6001）
