Real-Time Communication Relay Planning With a Low-Complexity Network Quality Prediction Model in Dynamic Indoor Missions

Abstract

Relay robots are crucial for extending communication when a client robot performs long-range missions. However, existing network quality prediction models and relay planning methods often struggle with real-time operation due to their high computational cost and poor adaptability to frequently changing missions. To address this, we propose a real-time communication relay system featuring two key contributions. First, a low-complexity network quality prediction model using Kalman filter-based Gaussian process regression achieves efficient online inference with constant-time updates (similar to 0.02s). Second, a hierarchical relay planning strategy, employing a Monte Carlo tree search-based sequential planner, generates communication-aware trajectories satisfying network constraints at discrete steps. Real-world experiments validate our system's effectiveness, demonstrating near-continuous network availability (99.1% channel reliability) and boosting the packet delivery ratio from a baseline of 44.7% to 73.7% . Our integrated approach offers a practical and robust solution for dynamic indoor missions.