LLMを活用した経路計画：指示入力による安全で効率的な自律ナビゲーション
LLM-Enhanced Path Planning: Safe and Efficient Autonomous Navigation with Instructional Inputs
大規模言語モデルと2D占有グリッドマップを組み合わせ、自然言語指示から障害物回避や目標優先順位を含むナビゲーション計画を生成するフレームワークを提案した。
著者: Pranav Doma, Aliasghar Arab, Xuesu Xiao
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
