Imagine a robot navigating a bustling warehouse, effortlessly weaving through obstacles and responding to complex instructions like "Pick up the blue box from Shelf 3, avoiding the spill in aisle 5." This isn't science fiction, but the near future promised by Large Language Models (LLMs). Researchers are now using LLMs to revolutionize robot path planning, moving beyond rigid algorithms to a more flexible, human-like approach. Traditional methods like A* struggle in dynamic environments, lacking the real-time adaptability needed to handle unexpected obstacles or changing instructions. This is where LLMs come in. The groundbreaking 'Dynamic Chain of Instruction-based Planning' (DCIP) framework empowers robots to understand and execute nuanced natural language commands. DCIP breaks down high-level instructions (e.g., "navigate to the charging station, prioritizing safety") into actionable steps, dynamically updating its plan as the robot encounters real-world changes. Think of it as giving the robot a common-sense understanding of its environment. By combining LLMs with traditional occupancy grid maps, DCIP allows robots to not only avoid obstacles but also prioritize actions based on the given instructions. For example, a robot could be instructed to 'navigate quickly' or 'maximize safety,' and DCIP would adjust its path accordingly, even in the presence of unforeseen obstacles like a pothole or a sudden spill. Experiments with various LLMs, including Mistral, Llama3.1, and Llama3, show that this approach significantly enhances robot navigation, outperforming traditional methods in both efficiency and safety. Llama3, in particular, excelled in following complex instructions, exhibiting a deep understanding of navigation strategies and safety constraints. While exciting, this technology faces challenges, particularly in handling ambiguous instructions. Future research aims to refine the interpretation of complex commands and enhance real-time adaptability in increasingly complex environments. The integration of LLMs into robotics promises a future where robots can seamlessly collaborate with humans, performing complex tasks with greater efficiency and safety, ultimately transforming how we work and live.