  In match race sailing, competitors must steer their boats upwind in the
presence of unpredictably evolving weather. Combined with the tacking motion
necessary to make upwind progress, this makes it natural to model their
path-planning as a hybrid stochastic optimal control problem. Dynamic
programming provides the tools for solving these, but the computational cost
can be significant. We greatly accelerate a semi-Lagrangian iterative approach
of Ferretti and Festa (R. Ferretti and A. Festa, "Optimal Route Planning for
Sailing Boats: A Hybrid Formulation", J Optim Theory Appl (2019)) by reducing
the state space dimension and designing an adaptive timestep discretization
that is very nearly causal. We also provide a more accurate tack-switching
operator by integrating over potential wind states after the switch. The method
is illustrated through a series of simulations with varying stochastic wind
conditions.
