This work focuses on autonomous contingency planning for scientific missions
by enabling rapid policy computation from any off-nominal point in the state
space in the event of a delay or deviation from the nominal mission plan.
Successful contingency planning involves managing risks and rewards, often
probabilistically associated with actions, in stochastic scenarios. Markov
Decision Processes (MDPs) are used to mathematically model decision-making in
such scenarios. However, in the specific case of planetary rover traverse
planning, the vast action space and long planning time horizon pose
computational challenges. A bi-level MDP framework is proposed to improve
computational tractability, while also aligning with existing mission planning
practices and enhancing explainability and trustworthiness of AI-driven
solutions. We discuss the conversion of a mission planning MDP into a bi-level
MDP, and test the framework on RoverGridWorld, a modified GridWorld environment
for rover mission planning. We demonstrate the computational tractability and
near-optimal policies achievable with the bi-level MDP approach, highlighting
the trade-offs between compute time and policy optimality as the problem's
complexity grows. This work facilitates more efficient and flexible contingency
planning in the context of scientific missions.

该研究侧重于科学任务的自主应变规划，通过允许从状态空间的任何非正常点快速计算决策，以应对与正常任务计划的延误或偏离。通过提出双层马尔可夫决策过程（MDP）框架，改进了计算可处理性，同时与现有任务规划实践相吻合并增强了基于人工智能的解决方案的可解释性和可信度，通过将任务规划 MDP 转化为双层 MDP 来讨论了这一框架，并在 RoverGridWorld 上对其进行了测试，这是一个用于漫游任务规划的改进的 GridWorld 环境。我们展示了使用双层 MDP 方法实现的计算可处理性和近似最佳策略，突出了问题复杂性增加时计算时间和政策最优性之间的权衡。这项研究有助于更高效和灵活地应对科学任务的应变规划。