# 3P-LLM: Probabilistic Path Planning using Large Language Model for Autonomous Robot Navigation

arXiv: https://arxiv.org/abs/2403.18778 · HTML: https://mlsanity.com/paper/2403.18778/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2024.

## Built on by (most cited first, 8 of 8)

- [A Study on Prompt Injection Attack Against LLM-Integrated Mobile Robotic Systems](https://mlsanity.com/paper/2408.03515/) (2024)
- [ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees](https://mlsanity.com/paper/2504.21022/) (2025)
- [Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach](https://mlsanity.com/paper/2410.17602/) (2024)
- [Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models](https://mlsanity.com/paper/2407.02220/) (2024)
- [Efficient Navigation in Unknown Indoor Environments with Vision-Language Models](https://mlsanity.com/paper/2510.04991/) (2025)
- [Think Small, Plan Smart: Minimalist Symbolic Abstraction and Heuristic Subspace Search for LLM-Guided Task Planning](https://mlsanity.com/paper/2501.15214/) (2025)
- [RoboAtlas: Contextual Active SLAM](https://mlsanity.com/paper/2606.26046/) (2026)
- [CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query](https://mlsanity.com/paper/2510.07516/) (2025)

## Abstract (arXiv)

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.