Tracking and formation control of multiple autonomous agents: A two-level consensus approach

Simultaneous tracking and formation control is addressed for a team of autonomous agents that evolve dynamically in a space containing a measurable vector field. Each agent measures the local value of the field along its trajectory and occasionally shares relevant information with other agents, in order to estimate the spatial average obtained from averaging measurements across all agents. Using shared information, agents control their trajectories in a cooperative manner, with the dual goals of driving the average field measurement to a specified value and maintaining a desired formation about the average. Two approaches to virtual leader estimation are considered. The first involves the synthesis of a common virtual leader state, whereas the second involves decentralized estimation of the virtual leader by individual agents. Under the second approach, control is posed as a two-level consensus problem, where agents reach agreement on the virtual leader state at one level and reach formation about the virtual leader at the other level. The decentralized approach is effective even when communication among agents is limited, in the sense that the associated network graph can be disconnected in frozen time.