Workspace Partitioning and Topology Discovery Algorithms for Heterogeneous Multi-Agent Networks
In this paper, we consider a class of workspace partitioning problems that arise in the context of area coverage and spatial load balancing for spatially distributed heterogeneous multi-agent networks. It is assumed that each agent has certain directions of motion or directions for sensing and exploration that are more preferable than others. These preferences are measured by means of convex and anisotropic (direction-dependent) quadratic proximity metrics which are, in general, different for each agent. These proximity metrics induce Voronoi-like partitions of the network's workspace that are comprised of cells which may not always be convex (or even connected) sets but are necessarily contained in ellipsoids that are known to their corresponding agents. The main contributions of this work are 1) a distributed algorithm for the computation of a Voronoi-like partition of the workspace of a heterogeneous multi-agent network and 2) a systematic process to discover the network topology induced by the latter Voronoi-like partition. Numerical simulations that illustrate the efficacy of the proposed algorithms are also presented.