Coordinating Underwater Vehicle Teams to Conduct Large-Scale Geospatial Tasks

Consider large teams of unmanned underwater vehicles (UUVs) conducting large-scale geospatial tasks such as information gathering or coverage planning. 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. State-of-the-art techniques that coordinate underwater vehicles for path dependent rewards do not scale well to such large teams. Further, 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 investigated 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 coordinating large teams 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 large problems and is the most cost effective means of deploying large teams of agents by gathering 3%-8% more reward than other techniques.