DORA: Dataflow Oriented Robotic Architecture

TL;DR

DORA is a robotic middleware architecture that improves communication efficiency by enabling explicit data dependencies and zero-copy data transmission, significantly reducing latency and CPU overhead in robotic systems.

Contribution

The paper introduces DORA, a novel dataflow-oriented middleware that addresses serialization overhead and supports heterogeneous data sizes in robotic applications.

Findings

Significant latency reduction in robotic communication.

Lower CPU overhead compared to existing middleware.

Effective in both simulation and real-world environments.

Abstract

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.