Maritime Mission Planning for Unmanned Surface Vessel using Large Language Model
This addresses the challenge of suboptimal performance and high costs in USV operations for maritime monitoring and surveillance, though it appears incremental as it combines existing LLM capabilities with symbolic planning.
The paper tackled the problem of dynamic mission planning for Unmanned Surface Vessels (USVs) by introducing a framework using Large Language Models (LLMs) like GPT-4, resulting in optimized mission execution and real-time adaptation to changing maritime conditions as validated by simulation results.
Unmanned Surface Vessels (USVs) are essential for various maritime operations. USV mission planning approach offers autonomous solutions for monitoring, surveillance, and logistics. Existing approaches, which are based on static methods, struggle to adapt to dynamic environments, leading to suboptimal performance, higher costs, and increased risk of failure. This paper introduces a novel mission planning framework that uses Large Language Models (LLMs), such as GPT-4, to address these challenges. LLMs are proficient at understanding natural language commands, executing symbolic reasoning, and flexibly adjusting to changing situations. Our approach integrates LLMs into maritime mission planning to bridge the gap between high-level human instructions and executable plans, allowing real-time adaptation to environmental changes and unforeseen obstacles. In addition, feedback from low-level controllers is utilized to refine symbolic mission plans, ensuring robustness and adaptability. This framework improves the robustness and effectiveness of USV operations by integrating the power of symbolic planning with the reasoning abilities of LLMs. In addition, it simplifies the mission specification, allowing operators to focus on high-level objectives without requiring complex programming. The simulation results validate the proposed approach, demonstrating its ability to optimize mission execution while seamlessly adapting to dynamic maritime conditions.