用离线信念规划让深空探测器自主选科学仪器，误判率降40%。
Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
- 构建基于贝叶斯网络的局部可观马尔可夫决策模型，处理深空测量不确定性。
- 在土卫二探测任务中，相比原方案降低近40%样本识别错误率。
- 适合通信受限的深空探测任务，尤其适用于生命迹象搜寻场景。
深空任务面临极端通信延迟与环境不确定性，难以实现地面实时操作。为支持通信受限环境下的自主科学作业，本文提出一种部分可观马尔可夫决策过程（POMDP）框架，用于自适应地调度航天器科学仪器。通过将贝叶斯网络集成至POMDP观测空间，有效管理类天体生物学任务中高维且不确定的测量数据。该网络紧凑编码测量间的依赖关系，提升科学数据的可解释性与计算可行性。仪器操作策略在离线阶段生成，使资源感知的计划可在发射前充分验证。以拟议的土卫二环绕着陆器生命探测套件（LDS）为案例研究，揭示贝叶斯网络结构与奖励塑造对系统性能的影响。与任务基准运行模式（ConOps）对比，评估了误分类率及非正常采样条件下的表现。结果表明，该方法将样本识别误差降低近40%。
原文摘要 · Abstract (English)
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%
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