Automatic Identification of the Leader in a Swarm using an Optimized Clustering and Probabilistic Approach

Collective behavior appearing abundantly in nature at various scales, such as swarming of insects, flocking of birds or schooling of fish, has motivated an interdisciplinary research thrust towards artificial swarms thanks to its numerous advantages. Although a lot of work has been done on coming up with controllers for these multi-agent systems towards optimizing coordination and planning, the inverse problem of identifying behaviors and models from observing a swarm is still not explored enough. Efficient tools for the analysis of the complex spatial-temporal dataset an observed swarm generates, are still missing. In this paper, we propose a methodology to solve the problem of identifying the leader in a swarm governed by leader-follower control framework using only the macroscopic view of the entire swarm. A methodology that combines clustering and probability methods is used to analyze the spatial-temporal data of a swarm of robots to narrow down on a subset of the robots that includes the leader. The clustering parameters are optimized over multiple simulated behaviors. The results show that this automated system can narrow down on a subset of agents (cluster) that includes the leader of the swarm with high accuracy. Applications of the proposed methodology include automatic identification of leader and swarm dynamics in both artificial and biological swarms, which can lead to a better understanding of collective behaviors and predictions of future behaviors.