Discovery and Exploration of Novel Swarm Behaviors Given Limited Robot Capabilities

Emergent collective behaviors have long interested researchers. These behaviors often result from complex interactions between many individuals following simple rules. However, knowing what collective behaviors are possible given a limited set of capabilities is difficult. Many emergent behaviors are counter-intuitive and unexpected even if the rules each agent follows are carefully constructed. While much work in swarm robotics has studied the problem of designing sets of rules and capabilities that result in a specific collective behavior, little work has examined the problem of exploring and describing the entire set of collective behaviors that can result from a limited set of capabilities. We take what we believe is the first approach to address this problem by presenting a general framework for discovering collective emergent behaviors that result from a specific capability model. Our approach uses novelty search to explore the space of possible behaviors in an objective-agnostic manner. Given this set of explored behaviors we use dimensionality reduction and clustering techniques to discover a finite set of behaviors that form a taxonomy over the behavior space. We apply our methodology to a single, binary-sensor capability model. Using our approach we are able to re-discover cyclic pursuit and aggregation, as well as discover several behaviors previously unknown to be possible with only a single binary sensor: wall following, dispersal, and a milling behavior often displayed by ants and fish.