Title: Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation Show Chinese title

Title: 基于LLM的机器人导航自然语言命令处理中的低延迟方法

Abstract: 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. Show Chinese abstract

Abstract: 大型语言模型（LLMs）如GPT在工业机器人中的集成提高了操作效率和人机协作能力。然而，这些模型的计算复杂度和规模常常会导致请求和响应时间的延迟问题。 本研究探索了将ChatGPT自然语言模型与机器人操作系统2（ROS 2）集成，以减轻交互延迟并改善模拟Gazebo环境中的机器人系统控制。我们提出了一种无需中间件传输平台即可集成这些技术的架构，并详细描述了模拟移动机器人如何响应文本和语音命令。 实验结果显示，这种集成平均减少了7.01%的通信延迟，从而提升了人机交互的执行速度、可用性和可访问性。这些改进有助于实现更流畅、实时的机器人操作，这对工业自动化和精密任务至关重要。