Collective Group Behavior through Local Interaction

T he abundance of embedded computational resources in autonomous vehicles enables enhanced operational effectiveness through cooperative teamwork in civilian and military applications. Compared to autonomous vehicles that perform solo missions, greater efficiency and operational capability can be realized from teams of autonomous vehicles operating in a coordinated fashion. Potential applications for multivehicle systems include space-based interferometers, combat, surveillance, and reconnaissance systems, hazardous material handling, and distributed reconfigurable sensor networks. To enable these applications, various cooperative control capabilities need to be developed, including formation control, rendezvous, attitude alignment, flocking, foraging, task and role assignment, payload transport, air traffic control, and cooperative search. Execution of these capabilities requires that individual vehicles share a consistent view of the objectives and the world. For example, a cooperative rendezvous task requires that each vehicle know the rendezvous point. Information consensus guarantees that vehicles sharing information over a noisy time varying network topology have a consistent view of information that is critical to the coordination task [1]. The instantaneous value of that information is the information state. By necessity, consensus algorithms are designed to be distributed, assuming only neighbor-toneighbor interaction between vehicles. Vehicles update the value of their information state based on the information states of their neighbors. The goal is to design an update law so that the information states of all of the vehicles in the network converge to a common value. Examples of the information state include a local representation of the center and shape of a formation, the rendezvous time, the length of a perimeter being monitored, the direction of motion for a multivehicle swarm, and the probability that a military target has been destroyed. Consensus algorithms have applications in rendezvous [2]–[4], formation control [5]–[9], flocking [10]–[16], attitude alignment [17]–[19], and sensor networks [20]–[23]. The purpose of this article is to provide a tutorial overview of information consensus in multivehicle cooperative control. Theoretical results regarding consensus-seeking under both time invariant and dynamically changing communication topologies are summarized. Several specific applications of consensus algorithms to multivehicle coordination are described. WEI REN, RANDAL W. BEARD, and ELLA M. ATKINS