Underwater Caging and Capture for Autonomous Underwater Vehicles

TL;DR

This paper introduces algorithms for autonomous underwater vehicles to cage and capture moving underwater entities in 3D environments, considering bathymetry and sparse sensing, with applications in underwater surveillance and containment.

Contribution

It presents a novel multi-algorithm approach combining min-cut, set cover, and assignment problems for effective 3D underwater caging and capture.

Findings

Algorithms successfully contain and capture entities in simulated environments.

Bathymetry inclusion improves caging efficiency.

Performance demonstrated on diverse underwater scenarios.

Abstract

In this paper, we consider the problem of caging and eventual capture of an underwater entity using multiple Autonomous Underwater Vehicles (AUVs) in a 3D water volume We solve this problem both with and without taking bathymetry into account. Our proposed algorithm for range-limited sensing in 3D environments captures a finite-speed entity based on sparse and irregular observations. After an isolated initial sighting of the entity, the uncertainty of its whereabouts grows while deployment of the AUV system is underway. To contain the entity, an initial cage, or barrier of sensing footprints, is created around the initial sighting, using islands and other terrain as part of the cage if available. After the initial cage is established, the system waits for a second sighting, and the possible opportunity to create a smaller, shrinkable cage. This process continues until at some point it is possible to create this smaller cage, resulting in capture, meaning the entity is sensed directly and continuously. We present a set of algorithms for addressing the scenario above, and illustrate their performance on a set of examples. The proposed algorithm is a combination of solutions to the min-cut problem, the set cover problem, the linear bottleneck assignment problem and the Thomson problem.