Dynamics-Aware Critical Neighbor Selection for Distributed Connectivity Maintenance in Multi-Agent Systems

Abstract

Maintaining connectivity in multi-agent systems often compromises task performance. Current strategies are frequently hampered by heavy communication loads and overly restrictive motion constraints. Furthermore, their local decision-making relies on static geometric information, neglecting agent dynamics. To address these shortcomings, this paper proposes a scalable, distributed framework centered on a novel dynamics-aware connection cost metric. This metric enables agents to prospectively select dynamically stable, task-compatible links, which are then enforced using control barrier functions (CBFs) within a cooperative optimization scheme. In multi-agent target-reaching tasks, simulations show our dynamics-aware metric reduces the final average goal distance by up to 26.1% compared to a static distance-based selection heuristic. Furthermore, our framework maintains persistent connectivity in highly dynamic scenarios, whereas a state-of-the-art algebraic connectivity-based method fails under limited communication bandwidth.