Warm-Started Optimized Trajectory Planning for ASVs
This work addresses efficient trajectory planning for autonomous surface vehicles, but it is incremental as it builds on existing methods like A* and optimal control.
The paper tackles trajectory planning for autonomous surface vehicles by combining A* for initial pathfinding with optimal control for refinement, resulting in a dynamically feasible and locally optimal trajectory with greatly reduced run time for the optimal control planner.
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.