Collaborating Underwater Vehicles Conducting Large-Scale Geospatial Tasks

Abstract

Consider large groups of unmanned underwater vehicles (UUVs) conducting large-scale geospatial tasks, such as information gathering or area sensing. Major costs of long duration missions include expensive underwater positioning systems and propulsion, which consumes energy. Exploiting the ocean currents can increase endurance, but requires accounting for forecast uncertainty, which lies beyond the scope of this article. State-of-the-art techniques, such as Monte Carlo tree search or cross entropy method that coordinate underwater vehicles for path-dependent rewards, do not scale well to such large groups. Furthermore, solving the mentioned tasks requires accounting for overlaps in the areas each vehicle searches, increasing the complexity of the problem. We therefore investigate planning techniques that can evaluate path-dependent rewards, account for the ocean forecast, and efficiently coordinate plans for many agents. Two formulations are investigated, which either search the space of action sequences or the space of feedback policies to find dynamically feasible trajectories. We present what we believe to be the first application of the cross entropy method to create joint plans for large groups of 8-128 UUVs. We also develop a novel iterative greedy method that further refines the best discovered constant action sequences to improve other greedy techniques. The iterative greedy method gathers the most information on average, scales well to deploying large groups of agents, and gathers 3%-8% more reward than other techniques.