An Information Processing System Design Approach to Underwater Robotic Swarms

Abstract

In some instances, intelligent, autonomous robotic systems (IARS) are transforming how the private and public sectors provide emergency services, deliver products, protect national interests, and accomplish their missions. Some futures, however, envision missions performed by cooperative teams of lARS-commonly described as swarms-making the purposeful design of swarms as information processing and communication systems an imperative. Furthermore, more optimal swarm performance depends on the degree to which its design fits its environment. In the context of organizational information processing and transactive memory theories, this paper explores the degree to which the centralization of decision-making and employment of distributed expertise may impact mission performance. Against the backdrop of collecting information on grey whale behaviors and migration routes, three swarm designs, starling, hive, and wolf-pack, are modeled and their simulated performance compared. The wolf-pack design, which is characterized by a largely decentralized decision-making structure and differentiated transactive memory system, out-performed the other designs based upon total work volumes and mission durations. Future studies should empirically investigate the impacts of cognitive slack and other swarm characteristics on mission accomplishment in a broader spectrum of scenarios.