Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning

2025-10-09

AI Summary

Deep-space exploration faces significant challenges due to prolonged communication delays and highly uncertain environments, rendering real-time ground-based control infeasible. To address this, we propose a POMDP-based autonomous scientific operations framework. Our approach innovatively embeds a Bayesian network into the POMDP’s observation space to compactly model dependencies among high-dimensional, heterogeneous measurements—enhancing both interpretability and computational efficiency. Integrating offline belief-state planning, reward shaping, and resource-constrained optimization, the framework enables adaptive scheduling of scientific payloads. Evaluated on a simulated Enceladus Orbilander mission, our method reduces sample identification error rate by 39.7% compared to baseline approaches and demonstrates markedly improved robustness under uncertainty, including non-standard sampling scenarios.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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%

Problem

• Autonomous science operations under extreme communication delays
• Managing high-dimensional uncertain measurements in astrobiology missions
• Offline planning for resource-aware spacecraft instrument sequencing

Innovation

• POMDP framework for autonomous instrument sequencing
• Bayesian network integration for measurement uncertainty management
• Offline policy computation enabling pre-launch plan validation