用大模型理解指令，让机器人在动态环境中安全高效导航。
LLM-Enhanced Path Planning: Safe and Efficient Autonomous Navigation with Instructional Inputs
- 结合大模型与栅格地图，解析自然语言指令生成路径
- 支持实时避障、目标优先级排序和动态路径重规划
- 适合工业场景中需人机协作的智能导航系统
以自然语言指令引导的自主导航对提升人机交互能力、实现动态环境中的复杂操作至关重要。尽管大语言模型（LLMs）并非专为规划设计，但可通过提供指导和约束信息显著提升规划效率并保障安全。本文提出一种将LLMs与二维占用栅格图及自然语言命令融合的规划框架，用于在资源受限环境下增强空间推理与任务执行能力。通过分解高层指令与实时环境数据，系统可生成包含障碍物规避、目标优先级排序和自适应行为的结构化导航计划，并能动态重计算路径以应对环境变化，同时符合隐含社交规范，实现流畅的人机协同。实验表明，该框架展现了利用LLMs构建上下文感知系统以提升工业及动态环境中导航效率与安全性的潜力。
原文摘要 · Abstract (English)
Autonomous navigation guided by natural language instructions is essential for improving human-robot interaction and enabling complex operations in dynamic environments. While large language models (LLMs) are not inherently designed for planning, they can significantly enhance planning efficiency by providing guidance and informing constraints to ensure safety. This paper introduces a planning framework that integrates LLMs with 2D occupancy grid maps and natural language commands to improve spatial reasoning and task execution in resource-limited settings. By decomposing high-level commands and real-time environmental data, the system generates structured navigation plans for pick-and-place tasks, including obstacle avoidance, goal prioritization, and adaptive behaviors. The framework dynamically recalculates paths to address environmental changes and aligns with implicit social norms for seamless human-robot interaction. Our results demonstrates the potential of LLMs to design context-aware system to enhance navigation efficiency and safety in industrial and dynamic environments.
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