Service Discovery-Based Hybrid Network Middleware for Efficient Communication in Distributed Robotic Systems

TL;DR

This paper introduces RIMAOS2C, a hybrid network middleware for distributed robotic systems that enhances communication efficiency, reduces latency, and supports diverse protocols in large-scale autonomous vehicle deployments.

Contribution

It presents a novel service discovery-based middleware with a Message Bridge mechanism that optimizes data flow and reduces message redundancy in robotic communication networks.

Findings

Reduces large-data transmission delay by 36-40%.

Eliminates message redundancy in cross-chip communication.

Decreases callback latency variation by up to 906%.

Abstract

Robotic middleware is fundamental to ensuring reliable communication among system components and is crucial for intelligent robotics, autonomous vehicles, and smart manufacturing. However, existing robotic middleware often struggles to meet the diverse communication demands, optimize data transmission efficiency, and maintain scheduling determinism between Orin computing units in large-scale L4 autonomous vehicle deployments. This paper presents RIMAOS2C, a service discovery-based hybrid network communication middleware designed to tackle these challenges. By leveraging multi-level service discovery multicast, RIMAOS2C supports a wide variety of communication modes, including multiple cross-chip Ethernet protocols and PCIe communication capabilities. Its core mechanism, the Message Bridge, optimizes data flow forwarding and employs shared memory for centralized message distribution, reducing message redundancy and minimizing transmission delay uncertainty. Tested on L4 vehicles and Jetson Orin domain controllers, RIMAOS2C leverages TCP-based ZeroMQ to overcome the large-message transmission bottleneck in native CyberRT. In scenarios with two cross-chip subscribers, it eliminates message redundancy and improves large-data transmission efficiency by 36 to 40 percent while reducing callback latency variation by 42 to 906 percent. This research advances the communication capabilities of robotic operating systems and proposes a novel approach to optimizing communication in distributed computing architectures for autonomous driving.