面向船舶船体检查应用的基于大语言模型与强化学习的共享自主系统
Shared Autonomy through LLMs and Reinforcement Learning for Applications to Ship Hull Inspections
AI总结：
本文针对海事领域复杂高风险环境下的人机协作需求，提出结合LLMs、人在回路多智能体框架和基于行为树的模块化任务管理器的共享自主方案，初步验证了其降低认知负荷等效果，为海事人机协作自主系统奠基。
摘要：
共享自主是机器人系统中极具前景的范式，尤其在海事领域，复杂、高风险且不确定的环境对高效的人机协作提出了要求。本文研究了三种互补方法的协同作用，以推动异构海事机器人群体中的共享自主发展：（i）集成大语言模型（LLMs）以实现直观的高层任务指定并为船体检查任务提供支持；（ii）在多智能体场景中部署人在回路交互框架，以实现具备适应性和意图感知能力的协调；（iii）开发基于行为树（Behavior Trees）的模块化任务管理器，提供可解释且灵活的任务控制。仿真和真实湖泊类环境中的初步结果表明，这种多层架构有望降低操作员的认知负荷、提升透明度，并增强自适应行为与人类意图的一致性。正在进行的工作聚焦于实现这些组件的完全集成、优化协调机制，以及在实际港口场景中验证系统。本研究为安全关键型海事机器人应用中可信赖的人机协作自主系统建立了模块化、可扩展的基础。
英文摘要：
Shared autonomy is a promising paradigm in robotic systems, particularly within the maritime domain, where complex, high-risk, and uncertain environments necessitate effective human-robot collaboration. This paper investigates the interaction of three complementary approaches to advance shared autonomy in heterogeneous marine robotic fleets: (i) the integration of Large Language Models (LLMs) to facilitate intuitive high-level task specification and support hull inspection missions, (ii) the implementation of human-in-the-loop interaction frameworks in multi-agent settings to enable adaptive and intent-aware coordination, and (iii) the development of a modular Mission Manager based on Behavior Trees to provide interpretable and flexible mission control. Preliminary results from simulation and real-world lake-like environments demonstrate the potential of this multi-layered architecture to reduce operator cognitive load, enhance transparency, and improve adaptive behaviour alignment with human intent. Ongoing work focuses on fully integrating these components, refining coordination mechanisms, and validating the system in operational port scenarios. This study contributes to establishing a modular and scalable foundation for trustworthy, human-collaborative autonomy in safety-critical maritime robotics applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
