🤖 AI Summary
This work addresses the challenge of coordination collapse in bandwidth-constrained drone swarms caused by sparse communication and information staleness. To this end, we propose a Predictive Lightweight Multi-Agent Reinforcement Learning framework (PL-MARL), which innovatively integrates a kinematics-aware active inference mechanism into a lightweight MARL architecture. By leveraging physical priors to proactively reconstruct neighboring agents’ trajectories, PL-MARL achieves an efficient trade-off between computation and communication under extremely low bandwidth overhead, effectively decoupling system structural resilience from communication frequency. Experimental results demonstrate that PL-MARL maintains high coverage performance and task continuity even under extreme communication scarcity and node failures, significantly enhancing robustness against disturbances while conserving spectral resources for payload operations.
📝 Abstract
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.