  We consider warm-started optimized trajectory planning for autonomous surface
vehicles (ASVs) by combining the advantages of two types of planners: an A*
implementation that quickly finds the shortest piecewise linear path, and an
optimal control-based trajectory planner. A nonlinear 3-degree-of-freedom
underactuated model of an ASV is considered, along with an objective functional
that promotes energy-efficient and readily observable maneuvers. The A*
algorithm is guaranteed to find the shortest piecewise linear path to the goal
position based on a uniformly decomposed map. Dynamic information is
constructed and added to the A*-generated path, and provides an initial guess
for warm starting the optimal control-based planner. The run time for the
optimal control planner is greatly reduced by this initial guess and outputs a
dynamically feasible and locally optimal trajectory.
