Network-Aware Path Planning for Autonomous Mobile Robots in Industrial Environments

Autonomous Mobile Robots (AMRs) in industrial environments require reliable wireless connectivity for coordination, control, and safety operations. Traditional path planning algorithms focus solely on geometric constraints, often leading robots through areas with poor network coverage that can compromise mission-critical operations. This paper presents a comprehensive framework for network-Aware path planning that incorporates wireless network quality metrics as path constraints. We validate our Sionna-based ray-Tracing simulations against real-world measurements from the Hernangómez et al. iV2I+ dataset, achieving strong correlation between simulated and real-world measurements (R2 = 0.87 for SNR, 0.82 for throughput). Using this validated simulation framework, we implement three novel path planning algorithms: A∗ with network constraints, conditional variational autoencoder (CVAE)-based neural path planning, and Graph neural network-based multi-path (GraphMP) planning. Our evaluation demonstrates trade-offs between network quality requirements and path efficiency, with CVAE achieving 95.2% constraint satisfaction and GraphMP showing 23% shorter planning times. We provide practical guidelines for selecting appropriate algorithms and network quality thresholds based on application requirements, enabling more reliable AMR operations in industrial settings.