Distributed control of multi-agent networks

Motivated by the challenges that arise in controlling mobile agents operating in areas with nonuniform time-varying densities, in this paper we propose a distributed steering control framework for a network of autonomous mobile multi-agents whose members have to deploy and allocate themselves in critical positions over a given region in accordance with a time-varying coverage density function. Our method is based on a two-level description of the multi-agent network. The second level reflects the macroscopic description of the network which corresponds to the probability distribution of the agents' locations over a given region in which the network of multi-agents is treated as one unit. The second level reflects the microscopic description of the network which is described in terms of the collection of all individual positions of all of its agents. Thus, the goal of the multi-agent network is to attain a spatial distribution that matches the reference coverage density function (macroscopic high-level control problem) through the local interactions of the agents at the individual level (microscopic low-level control problem). The high-level control problem is addressed by associating it with a desired reference Gaussian probability density. Moreover, the low control problem is addressed by utilizing the Lloyd's algorithm with a time-varying coverage density function. Therefore, the control laws provided would allow the agents to achieve a desired macroscopic behavior of the network, using only distributed algorithms and local information. Each agent will control its own velocity, based only on knowledge of a few neighboring agents, but in such a way that a desired probability distribution is obtained. Finally, a set of simulation results are provided to show the convergence of the mobile agents to their critical locations and to show the effectiveness of the proposed approach.