LLM-Enhanced metaheuristics with single-shot and few-shot learning for multi-robot exploration tasks

Abstract Multi-robot exploration in unknown environments is a challenging optimization task. Although many optimization algorithms exist, there has been limited exploration of Large Language Models (LLMs) in the literature to enhance these methods. This study investigates the use of LLMs to improve optimization strategies for robotic exploration considering two main approaches: zero-shot and few-shot learnings. In the former case, the LLM generates solutions based on a problem description alone. In the latter, few-shot learning, the LLM refines solutions based on initial results as well as a problem description. Using models like GPT, Gemini, and Claude, we created enhanced variants of Particle Swarm Optimization (PSO) to improve exploration efficiency and help robots navigate complex environments to address PSO’s common issues such as local optima. Experimental results demonstrate the strong potential of LLMs in refining optimization strategies for multi-robot exploration.