In this project, we combine the objectives of locational coverage problems and persistent coverage problems to enable a group of agents to cooperatively achieve persistent coverage over a domain.
In particular, a locational optimization criterion provides the multi-robot system (MRS) with coordination of configuration over the entire domain. An awareness maximization criterion leads the MRS to patrol the environment for persistent coverage. We achieve this in a decentralized fashion by allowing each agent to have its own opinion of awareness of the environment, which we encode as a time-varying density function. To leverage the awareness gained by neighboring agents, awareness levels are exchanged with Delaunay graph neighbors, and these are combined with agents’ measurements through a dynamic
consensus filter. In this project, local exchanges of awareness levels between neighbors are shown to allow awareness information over the entire domain to propagate across the entire team with performance guarantees. Moreover, a gradient-based control strategy is proposed, and a bound on the size of the MRS in terms of the rate of gain and loss of awareness and the size of the domain for guaranteed loss of awareness is provided.