LLM-Advisor introduces an innovative approach using large language models to help robots navigate efficiently across various terrains while minimizing energy costs.

• Tackles the critical challenge of finding optimal paths in diverse outdoor environments
• Leverages LLMs to plan routes that balance distance with terrain-specific energy constraints
• Enables robots to make smarter decisions when operating in areas where recharging is difficult
• Creates a new benchmark for evaluating cost-efficient path planning capabilities

This research bridges a significant gap in robotics engineering by addressing real-world navigation needs beyond simple obstacle avoidance, with potential applications in search and rescue, autonomous exploration, and field robotics.

LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains