Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments. We integrate a Bayesian network into the POMDP observation space to manage the high-dimensional and uncertain measurements typical of astrobiology missions. This network compactly encodes dependencies among measurements and improves the interpretability and computational tractability of science data. Instrument operation policies are computed offline, allowing resource-aware plans to be generated and thoroughly validated prior to launch. We use the Enceladus Orbilander's proposed Life Detection Suite (LDS) as a case study, demonstrating how Bayesian network structure and reward shaping influence system performance. We compare our method against the mission's baseline Concept of Operations (ConOps), evaluating both misclassification rates and performance in off-nominal sample accumulation scenarios. Our approach reduces sample identification errors by nearly 40%

翻译：深空任务面临极端的通信延迟和环境不确定性，阻碍了实时地面操作。为支持通信受限环境下的自主科学操作，本文提出一种部分可观测马尔可夫决策过程（POMDP）框架，用于自适应编排航天器科学仪器的操作序列。我们将贝叶斯网络集成至POMDP观测空间，以处理天体生物学任务典型的高维不确定测量数据。该网络紧凑地编码了测量值间的依赖关系，提升了科学数据的可解释性与计算可处理性。仪器操作策略通过离线计算实现，使得资源感知规划方案可在发射前完成生成与全面验证。我们以土卫二轨道着陆器拟采用的生命探测套件（LDS）为案例，论证了贝叶斯网络结构与奖励塑形如何影响系统性能。通过对比任务基线操作概念（ConOps），我们在误判率与非标样本累积场景下评估了系统表现。本方法将样本识别错误率降低了近40%。