LLM增强的路径规划：利用指令输入实现安全高效的自主导航
LLM-Enhanced Path Planning: Safe and Efficient Autonomous Navigation with Instructional Inputs
AI总结：
本文提出融合LLM、二维占据栅格地图与自然语言指令的导航框架，通过结构化任务分解与动态重规划提升动态环境中拾取放置任务的安全性和效率。
摘要：
由自然语言指令引导的自主导航，对于改善人机交互并使机器人能够在动态环境中执行复杂任务至关重要。尽管大型语言模型（LLM）并非专为规划而设计，但它们可以通过提供指导并明确约束以确保安全性，显著提升规划效率。本文提出一种规划框架，将LLM与二维占据栅格地图和自然语言命令相集成，以在资源受限场景中增强空间推理和任务执行能力。通过分解高层命令与实时环境数据，该系统为拾取与放置任务生成结构化导航计划，涵盖避障、目标优先级排序和自适应行为。该框架动态重新计算路径以应对环境变化，并遵循隐性社会规范，从而实现顺畅的人机交互。结果表明，LLM有潜力设计情境感知系统，以提升工业及动态环境中的导航效率与安全性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
