Unveiling the Undersea: A Collaborative Approach to Monitoring Underwater Objects

We consider a network of distributed underwater drones for monitoring industrially relevant underwater structures, such as renewable energy storage. The task is to cover and map a three-dimensional marine environment by identifying in the field objects of interest that prevent navigation, structure deployment or monitoring applications. Each drone has a noisy sensor that perform measurements of the absolute position of the underwater objects. To guide the drones, we use the 3D Voronoi tessellation for static coverage and estimation. We consider a distributed iterative scheme, based on distributed average consensus, to compute the maximum-likelihood estimate of the quantities of interest. This scheme does not involve explicit point-to-point message passing or routing; instead, it diffuses the measurements across the network by updating each node estimate with a weighted average of its neighbours estimates. At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme, robust to unreliable communication links, is shown working in a network with dynamically changing topology, which is a customary condition for moving platforms.