Navigation-GPT: A Robust and Adaptive Framework Utilizing Large Language Models for Navigation Applications

TL;DR

This paper documents elsarticle.cls, a robust LaTeX class designed for Elsevier submissions. Built on article.cls, it minimizes package conflicts and integrates essential tools (e.g., natbib, geometry, hyperref) to ensure reliable formatting and compatibility with standard LaTeX distributions. It distinguishes itself from the older elsart.cls by offering versatile preprint and final-format models, improved frontmatter handling, and streamlined theorem and list formatting. The class provides practical installation guidance and usage options, simplifying manuscript preparation and adherence to Elsevier’s formatting guidelines.

Abstract

Existing navigation decision support systems often perform poorly when handling non-predefined navigation scenarios. Leveraging the generalization capabilities of large language model (LLM) in handling unknown scenarios, this research proposes a dual-core framework for LLM applications to address this issue. Firstly, through ReAct-based prompt engineering, a larger LLM core decomposes intricate navigation tasks into manageable sub-tasks, which autonomously invoke corresponding external tools to gather relevant information, using this feedback to mitigate the risk of LLM hallucinations. Subsequently, a fine-tuned and compact LLM core, acting like a first-mate is designed to process such information and unstructured external data, then to generates context-aware recommendations, ultimately delivering lookout insights and navigation hints that adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) and other rules. Extensive experiments demonstrate the proposed framework not only excels in traditional ship collision avoidance tasks but also adapts effectively to unstructured, non-predefined, and unpredictable scenarios. A comparative analysis with DeepSeek-R1, GPT-4o and other SOTA models highlights the efficacy and rationality of the proposed framework. This research bridges the gap between conventional navigation systems and LLMs, offering a framework to enhance safety and operational efficiency across diverse navigation applications.