AquaChat++: LLM-Assisted Multi-ROV Inspection for Aquaculture Net Pens with Integrated Battery Management and Thruster Fault Tolerance

The global demand for aquaculture has surged over the past decade, driving the expansion of offshore fish farming systems such as net pens [1, 2]. These structures, while effective for large-scale fish production, are continuously exposed to harsh marine environments that can degrade structural integrity, compromise biosecurity, and increase the risk of fish escape or environmental contamination [3]. As a result, regular and reliable inspection of aquaculture net pens is critical to ensuring operational safety, productivity, and regulatory compliance [4]. Recent advances in underwater robotics, control systems, and computer vision have enabled significant progress in autonomous inspection [5, 6]. Remotely Operated Vehicles (ROVs), in particular, offer a practical platform for deploying sensing payloads such as cameras, sonars and performing close-range inspection in confined underwater environments [7]. However, most existing ROV-based systems operate in isolation, with limited autonomy and minimal adaptability to dynamic conditions such as power constraints, actuator degradation, and evolving mission demands [8, 9]. Moreover, mission planning and coordination typically require expert operators, limiting the scalability and responsiveness of these systems in real-world aquaculture operations [10, 11, 12]. To address these challenges, we propose AquaChat++, a novel framework that combines the reasoning capabilities of Large Language Models (LLMs) with multi-ROV coordination, battery-aware mission planning, and fault-tolerant control [13, 14]. Unlike traditional inspection pipelines that rely on fixed scripts or manual supervision, AquaChat++ enables natural language-driven task planning and dynamic allocation across multiple ROVs.