为自动驾驶系统设计的新型通信中间件，显著提升多芯片环境下的数据传输效率。
Service Discovery-Based Hybrid Network Middleware for Efficient Communication in Distributed Robotic Systems
- 基于多级服务发现与消息桥机制，支持跨芯片多种通信协议。
- 大消息传输效率提升36%至40%，回调延迟波动降低42%至906%。
- 适合大规模自动驾驶车辆中分布式计算系统的通信优化场景。
机器人中间件对保障系统组件间可靠通信至关重要，广泛应用于智能机器人、自动驾驶和智能制造。然而，现有中间件在满足多样化通信需求、优化数据传输效率以及维持大规模L4自动驾驶部署中Orin计算单元间的调度确定性方面存在不足。本文提出RIMAOS2C，一种基于服务发现的混合网络通信中间件，通过多级服务发现组播技术，支持多种跨芯片以太网协议及PCIe通信能力。其核心机制“消息桥”优化数据流转发，采用共享内存实现集中式消息分发，减少冗余并降低传输延迟不确定性。在L4车辆和Jetson Orin域控制器上测试表明，RIMAOS2C利用基于TCP的ZeroMQ克服了原CyberRT在大消息传输中的瓶颈。在双跨芯片订阅场景下，消除消息冗余，使大数据传输效率提升36%至40%，回调延迟波动降低42%至906%。该研究提升了机器人操作系统的通信能力，为自动驾驶分布式计算架构中的通信优化提供了新方案。
原文摘要 · Abstract (English)
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.
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