Motif-based Topology Design for Effective Performance by Networks of Mobile Autonomous Vehicles

To design efficient local connectivity patterns, we have used the idea of network motifs, which was first proposed in the context of biological networks. Network motifs are task specific local connectivity patterns, which exist with much higher frequency in real biological networks compared to those in random networks. These are sub-networks of a low number of nodes (usually 3-4) whose persistence in networks performing similar tasks in different contexts, imply their efficiency in the sense that they optimize certain performance metric in a local scale. We use a simulation test bed to find network motifs for local level communication structures in a collaborative vehicles framework. Here, the group mission consists of several tasks that the agents participate in. The tasks include search operation, data gathering/processing, target finding and leader-follower explorations. Each task gives rise to certain motifs that are specific to that task and the partial knowledge of the environment specifications that the agents operate in. In this way, given the subtasks that are necessary for the mission accomplishment, the most efficient task-specific local topologies are extracted. Switching suitable graphs when the mode of operation is changing can be handled by solving the resulting reachability problem using methods for symbolic planning such as graph grammars. Based on such switching, we also address the effects of split/merge operations on the spectral characteristics of the resulting connectivity graphs.