将ChatGPT接入ROS 2，降低机器人语音指令响应延迟7.01%
Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation
- 直接集成ChatGPT与ROS 2，免去中间件，简化通信架构
- 在Gazebo仿真中实现指令响应延迟平均降低7.01%
- 适合工业自动化中需实时人机交互的场景
大型语言模型（如GPT）在工业机器人中的应用提升了操作效率和人机协作能力。然而，这些模型的计算复杂性和体量常导致请求与响应时间延迟。本研究探索将ChatGPT自然语言模型与机器人操作系统2（ROS 2）集成，在模拟Gazebo环境中缓解交互延迟，提升机器人系统控制能力。我们提出一种无需中间件传输平台的架构，详细说明模拟移动机器人如何响应文本与语音指令。实验结果表明，该集成使执行速度、可用性与可访问性均得到改善，通信延迟平均降低7.01%。这一改进促进了更流畅、实时的机器人操作，对工业自动化与高精度任务至关重要。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs), such as GPT, in industrial robotics enhances operational efficiency and human-robot collaboration. However, the computational complexity and size of these models often provide latency problems in request and response times. This study explores the integration of the ChatGPT natural language model with the Robot Operating System 2 (ROS 2) to mitigate interaction latency and improve robotic system control within a simulated Gazebo environment. We present an architecture that integrates these technologies without requiring a middleware transport platform, detailing how a simulated mobile robot responds to text and voice commands. Experimental results demonstrate that this integration improves execution speed, usability, and accessibility of the human-robot interaction by decreasing the communication latency by 7.01\% on average. Such improvements facilitate smoother, real-time robot operations, which are crucial for industrial automation and precision tasks.
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