Dynamics-Aware Critical Neighbor Selection for Distributed Connectivity Maintenance in Multi-Agent Systems
Wei Tan, Xin Chen
AI summary
Problem
Existing distributed connectivity strategies rely on static geometric metrics or heavy global estimations that ignore agent dynamics, causing excessive communication loads, restricted mobility, and poor task performance in dynamic environments.
Approach
Agents proactively select communication links using a predictive cost metric that evaluates link stability under intended motion, then enforce these connections via control barrier functions solved through a cooperative distributed optimization scheme.
Key results
- Dynamics-aware connection cost metric for predicting link stability
- CBF-based control architecture enforcing minimal critical neighbor connectivity
- Violation-free distributed optimization for coupled safety constraints
- 26.1% reduction in average goal distance versus static heuristics
Why it matters
Enables reliable, task-efficient coordination for distributed robotic teams operating in dynamic, bandwidth-constrained environments.
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, ne- glecting 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.