Organization and reorganization of autonomous oceanographic sampling networks

Abstract

As robots and other autonomous agents become more widespread, there will be a greater need for them to work cooperatively to accomplish complex, that is, agents will enter or leave the system while the task is being performed. This presents a formidable problem for organizing and controlling the agents. We present an approach to this problem for autonomous oceanographic sampling networks, which are groups of robots and sensor platforms that cooperate to sample an area over a long period of time. The approach uses two organizations to control the agents. A task-level organization (TLO) controls the system during the actual mission, and a meta-level organization (MLO) is responsible for self-organization of the system, design of the TLO to fit the situation, and reorganization as necessary. This approach allows the TLO to be highly efficient, while allowing the MLO to give the overall system a great deal of flexibility in adapting to change.