利用离线信念状态规划实现深空任务的自适应科学操作
Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
AI总结：
本文提出基于POMDP的离线信念状态规划方法，用于深空任务中自适应科学仪器调度，通过整合贝叶斯网络提升数据可解释性与计算效率，并在Enceladus Orbilander任务中验证了其有效性。
摘要：
深空任务面临极端的通信延迟和环境不确定性，这使得实时地面操作变得困难。为了支持在通信受限环境中自主进行科学操作，我们提出了一种部分可观测马尔可夫决策过程（POMDP）框架，能够自适应地安排航天器科学仪器。我们将贝叶斯网络整合到POMDP的观测空间中，以管理天体生物学任务中典型的高维和不确定的测量数据。该网络能够紧凑地编码测量之间的依赖关系，并提高科学数据的可解释性和计算可行性。仪器操作策略是在线计算的，允许在发射前生成并彻底验证资源感知的计划。我们以Enceladus Orbilander提出的生命检测套件（LDS）作为案例研究，展示了贝叶斯网络结构和奖励塑造如何影响系统性能。我们比较了我们的方法与任务的基准概念操作（ConOps），评估了误分类率以及在非典型样本积累场景中的性能。我们的方法将样本识别误差减少了近40%。
英文摘要：
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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