SFG-ROS: A Resource-Aware Framework for Dense Multi-Agent Perception

2026-05-22

AI Summary

Standard ROS 2 faces challenges such as network congestion, naming conflicts, and high computational overhead in dense multi-agent perception scenarios. This work proposes a resource-aware collaborative perception framework that employs structured fully qualified names to achieve traffic isolation, integrates Fast DDS directional routing with lightweight inter-process communication (IPC) optimizations, and introduces on-demand centralized decoding alongside hardware abstraction containers to enable zero-configuration deployment across heterogeneous accelerators. The proposed approach reduces network traffic control complexity to O(1), decreases per-subscriber CPU overhead by 72.3% compared to standard ROS 2, and maintains low latency, thereby significantly enhancing system scalability and efficiency.

Abstract

Deploying heterogeneous multi-agent robot fleets for collaborative perception requires robust data exchange and scalable software architectures. However, standard ROS 2 implementations often suffer from network saturation, namespace collisions, and severe computational overhead when distributing dense sensor streams across devices. To address these bottlenecks, we present SFG-ROS, a resource-aware multi-agent software framework designed for dynamic fleet deployments. SFG-ROS addresses these challenges through three primary contributions. First, schema-driven traffic routing isolates high-frequency intra-agent traffic from the global network using a programmatic fully qualified name schema and targeted Fast DDS routing. Second, an on-demand centralized decoding pipeline automatically offloads high-bandwidth sensor data decompression, eliminating redundant processing across local consumer nodes. Finally, a hardware-agnostic container pipeline dynamically adapts to heterogeneous accelerators, seamlessly bridging development environments with zero-touch, field-ready execution. We evaluate the framework using a fleet of wheeled and legged robots equipped with LiDAR and stereo depth cameras. Experimental results show SFG-ROS bounds network traffic to $\mathcal{O}(1)$ and, by replacing redundant decompression with lightweight IPC, reduces the per-subscriber CPU scaling penalty by 72.3\% versus standard ROS 2, all while maintaining low latency. Finally, we publish SFG-ROS under a permissive license, available via \href{https://iis-esslingen.github.io/sfg-ros}{iis-esslingen.github.io/sfg-ros}.

Problem

• multi-agent perception
• ROS 2
• network saturation
• computational overhead
• heterogeneous robot fleets

Innovation

• resource-aware
• multi-agent perception
• schema-driven routing
• centralized decoding
• hardware-agnostic container