Workspace Partitioning and Topology Discovery Algorithms for Heterogeneous Multiagent Networks

In this article, we consider a class of workspace partitioning problems that arise in the context of area coverage for spatially distributed heterogeneous multiagent networks. It is assumed that each agent has certain directions of motion or directions for sensing that are preferable to others. These preferences are measured by means of convex and anisotropic (direction-dependent) quadratic proximity metrics, which can be different for each agent. These proximity metrics induce Voronoi-like partitions of the network's workspace, whose cells may not always be convex (or even connected) sets but are necessarily contained in a priori known ellipsoids. The main contributions of this article are as follows: 1) a distributed algorithm for the computation of a Voronoi-like partition of the workspace of a heterogeneous multiagent network and 2) a systematic process to discover the network topology induced by the latter partition. The distributed implementation of the proposed algorithms is enabled by the utilization of a hypothetical agent which determines when the performance of each agent is acceptable. Numerical simulations that illustrate the efficacy of the proposed algorithms are also presented.