Underwater Caging and Capture for Autonomous Underwater Vehicles
This addresses the challenge of autonomous underwater capture for applications like marine research or security, but it appears incremental as it combines existing optimization methods without claiming major breakthroughs.
The paper tackles the problem of caging and capturing an underwater entity using multiple Autonomous Underwater Vehicles (AUVs) in 3D environments, with and without bathymetry, by developing algorithms that create and shrink sensing cages based on sparse observations until direct, continuous sensing is achieved.
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.