Integrating Online Learning and Connectivity Maintenance for Communication-Aware Multi-Robot Coordination

TL;DR

This paper introduces a data-driven control strategy for multi-robot systems that maintains connectivity by online learning of communication quality and using control barrier functions, improving real-world robustness.

Contribution

It presents a novel Data-driven Connectivity Barrier Certificates framework combining Gaussian Processes and Control Barrier Functions for adaptive connectivity maintenance.

Findings

Effective in simulations with up to 20 robots.

Ensures robots maintain communication quality while performing tasks.

Theoretically justified and adaptable to real-world conditions.

Abstract

This paper proposes a novel data-driven control strategy for maintaining connectivity in networked multi-robot systems. Existing approaches often rely on a pre-determined communication model specifying whether pairwise robots can communicate given their relative distance to guide the connectivity-aware control design, which may not capture real-world communication conditions. To relax that assumption, we present the concept of Data-driven Connectivity Barrier Certificates, which utilize Control Barrier Functions (CBF) and Gaussian Processes (GP) to characterize the admissible control space for pairwise robots based on communication performance observed online. This allows robots to maintain a satisfying level of pairwise communication quality (measured by the received signal strength) while in motion. Then we propose a Data-driven Connectivity Maintenance (DCM) algorithm that combines (1) online learning of the communication signal strength and (2) a bi-level optimization-based control framework for the robot team to enforce global connectivity of the realistic multi-robot communication graph and minimally deviate from their task-related motions. We provide theoretical proofs to justify the properties of our algorithm and demonstrate its effectiveness through simulations with up to 20 robots.