  Much worldly semantic knowledge can be encoded in large language models
(LLMs). Such information could be of great use to robots that want to carry out
high-level, temporally extended commands stated in natural language. However,
the lack of real-world experience that language models have is a key limitation
that makes it challenging to use them for decision-making inside a particular
embodiment. This research assesses the feasibility of using LLM (GPT-3.5-turbo
chatbot by OpenAI) for robotic path planning. The shortcomings of conventional
approaches to managing complex environments and developing trustworthy plans
for shifting environmental conditions serve as the driving force behind the
research. Due to the sophisticated natural language processing abilities of
LLM, the capacity to provide effective and adaptive path-planning algorithms in
real-time, great accuracy, and few-shot learning capabilities, GPT-3.5-turbo is
well suited for path planning in robotics. In numerous simulated scenarios, the
research compares the performance of GPT-3.5-turbo with that of
state-of-the-art path planners like Rapidly Exploring Random Tree (RRT) and A*.
We observed that GPT-3.5-turbo is able to provide real-time path planning
feedback to the robot and outperforms its counterparts. This paper establishes
the foundation for LLM-powered path planning for robotic systems.
