Adaptive Leader-Follower Control for Multi-Robot Teams with Uncertain Network Structure

Traditionally-designed, centralized or decentralized control architectures typically rely on the availability of communication channels between neighboring robots as well as a known, static network structure to tightly coordinate their actions in order to achieve global consensus. Unfortunately, communication constraints and network disconnectivity are key bottlenecks in such approaches, leading to the failure of conventional centralized or decentralized networked controllers in achieving stability and global consensus. To overcome these limitations, we develop a centralized, coordinated-control structure for multi-robot teams with uncertain network structure. Our novel approach enables multi-robot teams to achieve consensus even with disconnected communication graphs. Leveraging model reference adaptive control framework and networked control architectures, we develop a coordinated leader-follower consensus controller capable of overcoming communication losses within the team, handling non-communicative robots, and compensating for environmental noise. We prove the stability of our controller and empirically validate our approach by analyzing the effects of reference graph structures and environmental noise on the performance of robot team for navigation tasks. Finally, we demonstrate our novel controller in a multi-robot testbed.