Classifying swarm behavior via compressive subspace learning

Abstract

Bio-inspired robot swarms encompass a rich space of dynamics and collective behaviors.Given some agent measurements of a swarm at a particular time instance, an important problem is the classification of the swarm behavior.This is challenging in practical scenarios where information from only a small number of agents may be available, resulting in limited agent samples for classification.Another challenge is recognizing emerging behavior: the prediction of swarm behavior prior to convergence of the attracting state.In this paper we address these challenges by modeling a swarm's collective motion as a low-dimensional linear subspace.We illustrate that for both synthetic and real data, these behaviors manifest as low-dimensional subspaces, and that these subspaces are highly discriminative.We also show that these subspaces generalize well to predicting emerging behavior, highlighting that there exists low-dimensional structure in transient agent behavior.In order to learn distinct behavior subspaces, we extend previous work on subspace estimation and identification from missing data to that of compressive measurements, where compressive measurements arise due to agent positions scattered throughout the domain.We demonstrate improvement in performance over prior works with respect to limited agent samples over a wide range of agent models and scenarios.