# Warm-Started Optimized Trajectory Planning for ASVs

## TL;DR

This paper presents a hybrid trajectory planning approach for autonomous surface vehicles that combines fast shortest-path search with optimal control to generate energy-efficient, feasible trajectories more efficiently.

## Contribution

It introduces a novel warm-start method that integrates A* path planning with optimal control, improving planning speed and trajectory quality for ASVs.

## Key findings

- Reduced computation time for trajectory planning
- Generated energy-efficient and feasible trajectories
- Enhanced initial guess quality for optimal control

## Abstract

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.