用大模型让四足机器人听懂人话导航，成功率超90%
Quadrupped-Legged Robot Movement Plan Generation using Large Language Model
- 把大模型放远程服务器处理指令，减轻机器人本地算力压力
- 融合激光雷达等传感器，将语言指令转为可执行的导航动作
- 在4种真实场景中测试，整体成功率达90%以上，适合非专业用户
传统四足机器人控制界面门槛高，需专业知识才能操作。本文提出一种新控制框架，利用大语言模型（LLM）实现自然语言驱动的导航。采用分布式架构，将高层指令处理任务移至外部服务器，以克服DeepRobotics Jueying Lite 3平台的机载计算限制。系统通过实时传感器融合（激光雷达、惯性测量单元与里程计），将LLM生成的规划转化为可执行的ROS导航命令。在结构化室内环境中，针对从单房间任务到跨区域复杂导航的四种不同场景进行了实验验证。结果表明该系统具备强鲁棒性，在所有场景下综合成功率达到90%以上，证实了外接大模型规划在真实环境部署中的可行性。
原文摘要 · Abstract (English)
Traditional control interfaces for quadruped robots often impose a high barrier to entry, requiring specialized technical knowledge for effective operation. To address this, this paper presents a novel control framework that integrates Large Language Models (LLMs) to enable intuitive, natural language-based navigation. We propose a distributed architecture where high-level instruction processing is offloaded to an external server to overcome the onboard computational constraints of the DeepRobotics Jueying Lite 3 platform. The system grounds LLM-generated plans into executable ROS navigation commands using real-time sensor fusion (LiDAR, IMU, and Odometry). Experimental validation was conducted in a structured indoor environment across four distinct scenarios, ranging from single-room tasks to complex cross-zone navigation. The results demonstrate the system's robustness, achieving an aggregate success rate of over 90\% across all scenarios, validating the feasibility of offloaded LLM-based planning for autonomous quadruped deployment in real-world settings.
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