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.
@article{arxiv.1907.02696,
title = {Warm-Started Optimized Trajectory Planning for ASVs},
author = {Glenn Bitar and Vegard N. Vestad and Anastasios M. Lekkas and Morten Breivik},
journal= {arXiv preprint arXiv:1907.02696},
year = {2019}
}
Comments
Accepted to the 12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS 2019)