  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.
