Online Leader Selection for Improved Collective Tracking and Formation Maintenance

The goal of this paper is to propose an extension of the popular leader-follower framework for multiagent collective tracking and formation maintenance in the presence of a time-varying leader. In particular, the leader is persistently selected online in order to optimize the tracking performance of an exogenous collective velocity command while also maintaining a desired formation via a (possibly time-varying) communication-graph topology. The effects of a change in the leader identity are theoretically analyzed and exploited for defining a suitable error metric that is able to capture the tracking performance of the multiagent group. Both the group performance and the metric design are found to depend upon the spectral properties of a special directed graph induced by the identity of the chosen leader. By exploiting these results, as well as distributed estimation techniques, we are then able to detail a fully decentralized adaptive strategy that is able to periodically select online the best leader among the neighbors of the current leader. Numerical simulations show that the application of the proposed technique results in an improvement of the overall performance of the group behavior with regard to other possible strategies.