Stochastic Optimal Control of a Sailboat
Authors
- Cole Miles, Cornell University
- Alexander Vladimirsky, Cornell University
Abstract
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 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.
Paper
Source Code
Examples
Error Scaling Analysis
Acknowledgements
The first author is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under Award Number DE-SC0020347. The second author is partially supported by the NSF DMS (awards 1738010 and 2111522).
We thank Elliot Cartree for providing a template for this website, originally used to host his work on Time-Dependent Surveillance-Evasion Games