面向数据流的机器人架构
DORA: Dataflow Oriented Robotic Architecture
AI总结：
DORA通过显式数据依赖规范和零拷贝传输，提升机器人系统在数据密集型任务中的通信效率和性能。
摘要：
机器人中间件作为基础架构，使复杂的机器人系统能够以协调和模块化的方式运行。在数据密集型的机器人应用中，尤其是在工业场景中，通信效率直接影响系统的响应速度、稳定性和整体生产率。然而，现有的机器人中间件存在几个限制：（1）它们严重依赖（反）序列化机制，为大尺寸数据引入了显著的开销；（2）它们缺乏对异构数据大小的高效和灵活支持，特别是在机器人内部通信和基于Python的执行环境中。为了解决这些挑战，我们提出了面向数据流的机器人架构（DORA），它能够实现显式数据依赖规范和高效的零拷贝数据传输。我们实现了所提出的框架作为开源系统，并通过在仿真和真实世界机器人环境中的大量实验来评估它。实验结果表明，与最先进的中间件相比，DORA在延迟和CPU开销方面有显著的减少。
英文摘要：
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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