They comprise one of the largest and most diversified classes of machines that are able to autonomously interact in an industrial environment. Additional challenges arise when programming for complex or specific processes between multiple robotic systems, for which many solutions exist modeled after both high-level human-interactive robotics and biological systems. Observing and controlling disturbance in this varied group of robots provides a unique challenge for engineers and researchers.Understanding cooperative control in current industrial robotics and in prototypes that may become part of robotics technology in the future is paramount to addressing disturbance in these systems. Though taxonomies in the field of cooperative robotics are rapidly changing, Dudek provides a convenient classification and metrics scheme for these systems by classifying robotic collectives by seven different characteristics: collective size, the range of communication, communication topology, and communication bandwidth, collective reconfigurability, the processing ability of each agent, and collective composition.Most cooperative control systems are designed as either swarm-type or independently intelligent. That is, the agents in cooperative systems may be designed to operate with low-level instructions intact or to operate with no low-level instruction at all, transmitting instead to a base station for planning and organizational control. The two types of systems can be distinguished by application of Matarić’s definitions of explicit and implicit cooperation in which explicit cooperation occurs when on agent takes action to benefit another agents goal, whereas in implicit cooperation each agent acts selfishly to complete its own goal, which modifies the environment in ways beneficial to goal completion for other agents.