This research introduces a novel framework that leverages Large Language Models (LLMs) to enable robust mobile robot path planning through dynamic waypoint generation.

• Enhances robot navigation by generating adaptive waypoints that adjust to changing environments
• Outperforms traditional path planning techniques by offering greater flexibility across varied starting points and target positions
• Enables robots to navigate complex environments safely without extensive retraining
• Creates more robust, efficient paths that can adapt to obstacles in real-time

This engineering breakthrough matters because it reduces the need for scenario-specific training, making robotic systems more versatile and deployable in unpredictable real-world settings.

Robust Mobile Robot Path Planning via LLM-Based Dynamic Waypoint Generation