Orientation Diversity for an Autonomous Underwater Vehicle Cluster

This paper presents a novel theory for the generation of vehicle heading sequences that simultaneously ensures orientation diversity both between vehicles and along a given vehicle's path, while at the same time bounding the cluster divergence, and translating the cluster. The theory is based on periodic sequences of polygonal paths. The paper presents the basic theory including performance functions for rating and ranking the "optimality" of different sequences. The paper also presents novel algorithms for generating "optimal" path sequences for any size cluster as well as simulation results of these algorithms