Dual large language model core-driven adaptive framework for ship navigation agents

Objective Current ship navigation decision-making systems struggle to demonstrate superior performance in undefined sailing scenarios. Given the broad applicability of large language models （LLMs） in unknown scenarios, this study proposes a dual-LLM-core-driven adaptive ship navigation agent architecture （Nav-DLLC） to address this issue.MethodNav-DLLC employs ReAct-based prompting to decompose complex navigation tasks into manageable subtasks and invoke external tools for information collection, reducing LLM errors. Subsequently, a small-parameter LLM fine-tuned with low-rank adaptation （LoRA） serves as the collision avoidance core, processing unstructured data to generate COLREG-compliant decisions. ResultsSimulation experiments show that Nav-DLLC achieves outstanding performance in both traditional ship collision avoidance tasks and unstructured dynamic scenarios. Its collision avoidance accuracy is 86%, and its behavior compliance rate is 90%, significantly outperforming LLM baselines and traditional methods such as the Dynamic Window Approach （DWA） and Artificial Potential Field （APF）. The decision core's single-decision latency is 11.13 seconds, higher than the 0.73 seconds of traditional methods, yet still within the safe time window for collision avoidance decision-making. ConclusionNav-DLLC bridges the gap between traditional navigation systems and LLM technology, providing a safe and efficient intelligent decision-making paradigm for complex navigation environments and promoting the intelligent development of ship navigation.