Limited bandwidth recognition of collective behaviors in bio-inspired swarms
Daniel S. Brown, Michael A. Goodrich · 2014
Models of swarming and modes of controlling them are nu-merous; however, to date swarm researchers have mostly ig-nored a fundamental problem that impedes scalable human interaction with large bio-inspired robot swarms, namely, how do you know what the swarm is doing if you can’t ob-serve every agent in the collective? We examine swarm mod-els that exhibit multiple collective motion patterns from the same parameters. These multiple emergent behaviors pro-vide increased expressivity, but at the cost of uncertainty about the swarm’s actual behavior. Because bandwidth and time constraints limit the number of agents that can be ob-served in a swarm, it is desirable to be able to recognize and monitor the collective behavior of a swarm through limited samples from a small subset of agents. We present a novel framework for classifying the collective behavior of a bio-inspired robot swarm using locally-based approximations of a swarm’s global features. We apply this framework to two bio-inspired models of swarming that exhibit a flock and torus behavior and a swarm, torus, and flock behavior, re-spectively. We present both a formal metric of expressivity and a classifier that leverages local agent-level features to accurately recognize these collective swarm behaviors while sampling from only a small number of agents.