Multi-Robot Consensus with Adaptive Learning-Based Dynamic Controller Updates

This paper addresses the problem of dy-namic controller updates if needed to achieve consensus in a Multi-robot system (MRS) framework while en-suring overall stability. The intermittent updates allow robots to follow the leader robot, maintaining a desired separation. The theoretical developments in this work have been validated through experiments, reinforcing the practical viability of our proposed methodology. The results show the efficiency of the proposed con-troller in removing transient errors and achieving consensus easily even for a large MRS framework.