Online Leader Selection for Improved Collective Tracking and Formation Maintenance

TL;DR

This paper introduces an online leader selection method for multi-agent systems that enhances collective tracking and formation maintenance by dynamically choosing the optimal leader based on spectral graph properties and distributed estimation.

Contribution

It presents a novel decentralized adaptive strategy for online leader selection that improves group performance in multi-agent systems with time-varying leaders.

Findings

Improved collective tracking performance with the proposed leader selection.

Theoretical analysis links leader changes to spectral properties of the communication graph.

Numerical simulations demonstrate performance gains over other strategies.

Abstract

The goal of this work is to propose an extension of the popular leader-follower framework for multi-agent collective tracking and formation maintenance in presence of a time- varying leader. In particular, the leader is persistently selected online so as 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 able to capture the tracking performance of the multi- agent 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 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 w.r.t. other possible strategies.