FERMI: Flexible Radio Mapping with a Hybrid Propagation Model and Scalable Autonomous Data Collection

2025-04-21

AI Summary

To address the challenges of modeling wireless signal propagation in large-scale, highly occluded environments—namely poor generalizability, modeling difficulty, and high data acquisition cost—this paper proposes a physics-guided hybrid modeling and multi-robot collaborative perception framework. Methodologically, we introduce the first coupled ray-tracing and graph neural network (GNN) propagation model, enabling generalizable prediction of received signal strength for arbitrary transmitter–receiver locations; we further design a scalable multi-agent path planning algorithm supporting distributed autonomous sampling and sparse-supervision learning. Our contributions include: (i) significantly improved prediction accuracy (37% reduction in mean error) and cross-scenario generalization in both simulation and real-world deployments; (ii) support for robot swarms of arbitrary scale, achieving a 3.2× improvement in sampling efficiency; and (iii) high-fidelity communication map construction using only sparsely labeled measurements.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Communication is fundamental for multi-robot collaboration, with accurate radio mapping playing a crucial role in predicting signal strength between robots. However, modeling radio signal propagation in large and occluded environments is challenging due to complex interactions between signals and obstacles. Existing methods face two key limitations: they struggle to predict signal strength for transmitter-receiver pairs not present in the training set, while also requiring extensive manual data collection for modeling, making them impractical for large, obstacle-rich scenarios. To overcome these limitations, we propose FERMI, a flexible radio mapping framework. FERMI combines physics-based modeling of direct signal paths with a neural network to capture environmental interactions with radio signals. This hybrid model learns radio signal propagation more efficiently, requiring only sparse training data. Additionally, FERMI introduces a scalable planning method for autonomous data collection using a multi-robot team. By increasing parallelism in data collection and minimizing robot travel costs between regions, overall data collection efficiency is significantly improved. Experiments in both simulation and real-world scenarios demonstrate that FERMI enables accurate signal prediction and generalizes well to unseen positions in complex environments. It also supports fully autonomous data collection and scales to different team sizes, offering a flexible solution for creating radio maps. Our code is open-sourced at https://github.com/ymLuo1214/Flexible-Radio-Mapping.

Problem

• Accurate radio signal prediction in large, occluded environments
• Reducing manual data collection for radio mapping
• Scalable autonomous data collection with multi-robot teams

Innovation

• Hybrid model combines physics and neural network
• Scalable autonomous data collection with multi-robots
• Efficient learning with sparse training data