An Online Coverage Path Planning Algorithm for Curvature-Constrained AUVs

The paper presents an algorithm for online coverage path planning of unknown environments using curvature-constrained AUVs. Unlike point vehicles, which can make quick maneuvers in any direction towards any goal, curvature-constrained AUVs need significant time to accelerate, decelerate, or turn towards the goal. Therefore, finding a feasible collision-free path to the waypoint in the presence of obstacles is a nontrivial task for curvature-constrained AUVs. In order to overcome this challenge, we develop a new algorithm that dynamically selects the shortest Dubins path from its current state to a neighboring region in a locally optimal manner while providing efficient global coverage. The proposed new algorithm is an extension of our recently developed algorithm called $\epsilon^{\star}$, which utilizes an Exploratory Turing Machine (ETM) as a supervisor to guide the vehicle with adaptive navigation decisions. The performance of the proposed algorithm is validated on a high-fidelity underwater simulator called UWSim, where the collected terrain data is used offline for 3-D reconstruction of the seabed. The simulations show that the proposed algorithm generates feasible and safe coverage paths for curvature-constrained AUVs for accurate reconstruction of the underwater terrain.