  We propose a novel technique for guidance of buoyancy-controlled vehicles in
uncertain under-ice ocean flows. In-situ melt rate measurements collected at
the grounding zone of Antarctic ice shelves, where the ice shelf meets the
underlying bedrock, are essential to constrain models of future sea level rise.
Buoyancy-controlled vehicles, which control their vertical position in the
water column through internal actuation but have no means of horizontal
propulsion, offer an affordable and reliable platform for such in-situ data
collection. However, reaching the grounding zone requires vehicles to traverse
tens of kilometers under the ice shelf, with approximate position knowledge and
no means of communication, in highly variable and uncertain ocean currents. To
address this challenge, we propose a partially observable MDP approach that
exploits model-based knowledge of the under-ice currents and, critically, of
their uncertainty, to synthesize effective guidance policies. The approach uses
approximate dynamic programming to model uncertainty in the currents, and QMDP
to address localization uncertainty. Numerical experiments show that the policy
can deliver up to 88.8% of underwater vehicles to the grounding zone -- a 33%
improvement compared to state-of-the-art guidance techniques, and a 262%
improvement over uncontrolled drifters. Collectively, these results show that
model-based under-ice guidance is a highly promising technique for exploration
of under-ice cavities, and has the potential to enable cost-effective and
scalable access to these challenging and rarely observed environments.
