水下机器人羽流追踪技术监测水环境应用综述

Review of Underwater Robot Plume Tracking Technology in Water Environment Monitoring

摘要:

水下机器人羽流追踪技术通过动态感知污染物扩散路径,为生态保护及应急响应提供关键支撑,逐渐成为水环境监测的重要手段。系统综述了该技术的发展脉络与技术体系:在羽流建模层面,对比分析了物理模型、数据驱动模型及混合模型的适用性与局限性;在追踪算法领域,重点剖析了单机策略与多机协同的优化机制,指出强化学习与物理模型的融合显著提升了动态环境下的鲁棒性;在系统应用方面,结合深海热液探测、河口水域污染定位等案例,评估了追踪效率、精度及能耗等指标。进一步指出当前水下机器人羽流追踪技术瓶颈,包括羽流突变适应性弱、强噪声信号分离难、小型平台负载–续航矛盾等。水下机器人羽流追踪技术可通过多模态感知架构的融合、智能算法集群的演化与能量优化机制的迭代,重塑海洋环境监测新范式。

Abstract:

Underwater robot plume tracking technology, by dynamically perceiving the path of pollution diffusion, provides crucial support for deep-sea resource exploration, ecological protection, and emergency response, and has gradually become an important means in the field of water environment monitoring. The development trajectory and technical system of this technology were systematically reviewed: at the plume modeling level, it compared and analyzed the applicability and limitations of physical models, data-driven models, and hybrid models; in the tracking algorithm domain, it focused on dissecting the optimization mechanisms of single-machine strategies and multi-machine collaboration, and pointed out that the integration of reinforcement learning and physical models significantly enhanced robustness in dynamic environments; in terms of system application, it quantitatively assessed indicators such as tracking efficiency, accuracy, and energy consumption by combining cases like deep-sea hydrothermal vent detection and river estuary pollution location. The research further revealed the current bottlenecks of underwater robot plume tracking technology, including weak adaptability to plume mutations, difficulty in separating strong noise signals, and the contradiction between load and endurance of small platforms. It looked forward to the future development trends of underwater robot plume tracking technology, focusing on the development of global water environment safety governance technologies.