DORA通过零拷贝数据传输提升机器人系统通信效率
DORA: Dataflow Oriented Robotic Architecture
- 基于数据流设计，显式声明数据依赖关系
- 相比现有中间件，延迟和CPU开销显著降低
- 适合工业场景下高负载、异构数据的机器人系统
机器人中间件作为基础架构，支持复杂机器人系统以协同、模块化方式运行。在数据密集型机器人应用中，尤其在工业场景下，通信效率直接影响系统响应性、稳定性与整体生产力。然而，现有机器人中间件存在诸多局限：(1) 严重依赖序列化/反序列化机制，对大数据量引入显著开销；(2) 缺乏对异构数据大小的高效灵活支持，尤其在机器人内部通信及基于Python的执行环境中。为此，我们提出数据流导向的机器人架构（DORA），支持显式的数据依赖定义与高效的零拷贝数据传输。我们实现了该框架的开源系统，并在仿真与真实机器人环境中进行了广泛实验。结果表明，相较于当前最先进中间件，DORA在延迟和CPU开销方面均有显著降低。
原文摘要 · Abstract (English)
Robotic middleware serves as the foundational infrastructure, enabling complex robotic systems to operate in a coordinated and modular manner. In data-intensive robotic applications, especially in industrial scenarios, communication efficiency directly impact system responsiveness, stability, and overall productivity. However, existing robotic middleware exhibit several limitations: (1) they rely heavily on (de)serialization mechanisms, introducing significant overhead for large-sized data; (2) they lack efficient and flexible support for heterogeneous data sizes, particularly in intra-robot communication and Python-based execution environments. To address these challenges, we propose Dataflow-Oriented Robotic Architecture (DORA) that enables explicit data dependency specification and efficient zero-copy data transmission. We implement the proposed framework as an open-source system and evaluate it through extensive experiments in both simulation and real-world robotic environments. Experimental results demonstrate substantial reductions in latency and CPU overhead compared to state-of-the-art middleware.
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