计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 242-247.doi: 10.11896/jsjkx.210500093
彭双, 伍江江, 陈浩, 杜春, 李军
PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun
摘要： 星上自主任务规划是对地观测卫星自主运行的关键技术之一,近年来得到了研究人员的高度关注。考虑到星上计算资源有限,以及星上任务、资源动态变化等特点与挑战,现有研究主要采用启发式搜索算法对卫星星上自主任务规划问题进行求解,但这类算法还有待进一步优化。文中首先构建了一种新的观测任务序贯决策框架。基于该框架,对地观测卫星可以实时决策要执行的观测任务,而无须预先生成任何观测方案。然后,将注意力机制和循环神经网络相结合,设计了观测任务决策模型、任务特征表示方法以及模型训练方法,提出了一种基于注意力神经网络的观测任务序贯算法;最后,基于多组随机数据对所提算法、两种深度学习算法以及两种启发式在线搜索算法进行了比较。实验结果表明,所提方法的平均响应时间不到已有深度学习算法的1/5,收益误差远低于启发式搜索算法,证实了所提方法的可行性和有效性。
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