Uncertainty-Aware GNN for Collaborative Robot Mapping Towards 6G-Enabled Smart Warehouses

Smart warehouses face rapid layout reconfigurations and frequent process adaptations, especially when using a Cyber-Physical Production Systems (CPPS) setup. These dynamic conditions introduce environmental uncertainty, making real-time navigation and obstacle avoidance a significant challenge for robot fleets. Limited sensing range and frequent occlusions further hinder local robot perception. Classic methods rely on centralized planning or vision-based systems, which struggle in low-visibility and cluttered environments. To address these gaps, we propose a graph-based collaborative perception network for the RoboFUSE (Framework for Unified Sensing and Exploration) system. Each robot operates onboard RoboFUSE with a dual-purpose waveform for sensing and communication (S&C) that emulates 6G Integrated Sensing and Communication (ISAC). This setup supports real-time data sharing across the fleet. On top of this platform, we develop RoboFUSE-Graph Neural Network (GNN), an uncertainty-aware GNN that fuses multi-robot radar data into a global spatial-semantic map. The model captures spatial relations and temporal dependencies using sliding window graphs. Experiments reveal an F1 score of 0.91 with a five-sliding window. The proposed approach enhances situational awareness, enables safe, scalable navigation, and represents a stride towards 6G-enabled smart warehouses.