  Path planning is a fundamental scientific problem in robotics and autonomous
navigation, requiring the derivation of efficient routes from starting to
destination points while avoiding obstacles. Traditional algorithms like A* and
its variants are capable of ensuring path validity but suffer from significant
computational and memory inefficiencies as the state space grows. Conversely,
large language models (LLMs) excel in broader environmental analysis through
contextual understanding, providing global insights into environments. However,
they fall short in detailed spatial and temporal reasoning, often leading to
invalid or inefficient routes. In this work, we propose LLM-A*, an new LLM
based route planning method that synergistically combines the precise
pathfinding capabilities of A* with the global reasoning capability of LLMs.
This hybrid approach aims to enhance pathfinding efficiency in terms of time
and space complexity while maintaining the integrity of path validity,
especially in large-scale scenarios. By integrating the strengths of both
methodologies, LLM-A* addresses the computational and memory limitations of
conventional algorithms without compromising on the validity required for
effective pathfinding.
