  The success of a multi-kilometre drive by a solar-powered rover at the lunar
south pole depends upon careful planning in space and time due to highly
dynamic solar illumination conditions. An additional challenge is that the
rover may be subject to random faults that can temporarily delay long-range
traverses. The majority of existing global spatiotemporal planners assume a
deterministic rover-environment model and do not account for random faults. In
this paper, we consider a random fault profile with a known, average spatial
fault rate. We introduce a methodology to compute recovery policies that
maximize the probability of survival of a solar-powered rover from different
start states. A recovery policy defines a set of recourse actions to reach a
safe location with sufficient battery energy remaining, given the local solar
illumination conditions. We solve a stochastic reach-avoid problem using
dynamic programming to find an optimal recovery policy. Our focus, in part, is
on the implications of state space discretization, which is required in
practical implementations. We propose a modified dynamic programming algorithm
that conservatively accounts for approximation errors. To demonstrate the
benefits of our approach, we compare against existing methods in scenarios
where a solar-powered rover seeks to safely exit from permanently shadowed
regions in the Cabeus area at the lunar south pole. We also highlight the
relevance of our methodology for mission formulation and trade safety analysis
by comparing different rover mobility models in simulated recovery drives from
the LCROSS impact region.
