  In recent years, Large Language Models (LLMs) have demonstrated remarkable
capabilities in understanding and solving mathematical problems, leading to
advancements in various fields. We propose an LLM-embodied path planning
framework for mobile agents, focusing on solving high-level coverage path
planning issues and low-level control. Our proposed multi-layer architecture
uses prompted LLMs in the path planning phase and integrates them with the
mobile agents' low-level actuators. To evaluate the performance of various
LLMs, we propose a coverage-weighted path planning metric to assess the
performance of the embodied models. Our experiments show that the proposed
framework improves LLMs' spatial inference abilities. We demonstrate that the
proposed multi-layer framework significantly enhances the efficiency and
accuracy of these tasks by leveraging the natural language understanding and
generative capabilities of LLMs. Our experiments show that this framework can
improve LLMs' 2D plane reasoning abilities and complete coverage path planning
tasks. We also tested three LLM kernels: gpt-4o, gemini-1.5-flash, and
claude-3.5-sonnet. The experimental results show that claude-3.5 can complete
the coverage planning task in different scenarios, and its indicators are
better than those of the other models.
