用大模型让多水下机器人自动巡检渔网，还能抗故障省电。
AquaChat++: LLM-Assisted Multi-ROV Inspection for Aquaculture Net Pens with Integrated Battery Management and Thruster Fault Tolerance
- 大模型将自然语言指令转为多机器人的协同巡检计划。
- 实测覆盖更全、耗电更低，且能容忍推进器故障。
- 适合想搞智能养殖巡检的科研和工程人员看。
海洋养殖网箱的巡检对保障养殖系统结构完整与可持续运行至关重要。传统方法多依赖人工或单个水下机器人（ROV），难以适应能耗、硬件故障和动态水下环境等实时约束。本文提出AquaChat++，一种基于大语言模型（LLM）的多ROV巡检框架，实现自适应任务规划、协同执行与容错控制。系统采用两层架构：高层规划层利用如ChatGPT-4的LLM，将自然语言指令转化为符号化的多智能体巡检计划；任务管理器根据各ROV实时状态（包括电池电量与推进器故障）动态分配任务。底层控制层确保精准轨迹跟踪，并集成推进器故障检测与补偿机制。通过实时反馈与事件触发重规划，AquaChat++显著提升系统鲁棒性与效率。在基于物理引擎的模拟环境中验证，该系统实现了更高的巡检覆盖率、更优的能耗表现，并具备对执行器故障的韧性。结果表明，基于大模型的框架可推动养殖领域水下机器人向规模化、智能化与自主化发展。
原文摘要 · Abstract (English)
Inspection of aquaculture net pens is essential for ensuring the structural integrity and sustainable operation of offshore fish farming systems. Traditional methods, typically based on manually operated or single-ROV systems, offer limited adaptability to real-time constraints such as energy consumption, hardware faults, and dynamic underwater conditions. This paper introduces AquaChat++, a novel multi-ROV inspection framework that uses Large Language Models (LLMs) to enable adaptive mission planning, coordinated task execution, and fault-tolerant control in complex aquaculture environments. The proposed system consists of a two-layered architecture. The high-level plan generation layer employs an LLM, such as ChatGPT-4, to translate natural language user commands into symbolic, multi-agent inspection plans. A task manager dynamically allocates and schedules actions among ROVs based on their real-time status and operational constraints, including thruster faults and battery levels. The low-level control layer ensures accurate trajectory tracking and integrates thruster fault detection and compensation mechanisms. By incorporating real-time feedback and event-triggered replanning, AquaChat++ enhances system robustness and operational efficiency. Simulated experiments in a physics-based aquaculture environment demonstrate improved inspection coverage, energy-efficient behavior, and resilience to actuator failures. These findings highlight the potential of LLM-driven frameworks to support scalable, intelligent, and autonomous underwater robotic operations within the aquaculture sector.
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