Real-Time Communication Relay Planning with a Low-Complexity Network Quality Prediction Model in Dynamic Indoor Missions
Jaemin Seo, Jongyun Kim, Seunghwan Kim, Changseung Kim, Woojae Shin, Hyondong Oh
AI summary
Problem
Existing network prediction and relay planning methods struggle with real-time operation due to high computational costs and poor adaptability to frequently changing missions in complex indoor environments.
Approach
The method combines a low-complexity Kalman filter-based Gaussian process regression model for constant-time network prediction with a hierarchical relay planning strategy that uses Monte Carlo tree search to generate communication-aware trajectories.
Key results
- Low-complexity KF-GPR model enabling constant-time (~0.02s) online network prediction
- Hierarchical relay planner using Monte Carlo tree search for communication-aware trajectory generation
- Achieved 99.1% channel reliability and increased packet delivery ratio from 44.7% to 73.7% in real-world experiments
- Demonstrated robust connectivity maintenance under dynamically changing mission points
Why it matters
Enables reliable, real-time multi-hop communication for mobile robots operating in complex, infrastructure-limited indoor environments.
Abstract
Relay robots are crucial for extending communica- tion 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 (∼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 effective- ness, 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.