Tao Shan, Yang Lei, Zhang Xiaobo, Zhao Shengya, Liu Kun, Tian Xinran, Xu Hengxin
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
National Deep Sea Center, Qingdao 266237, China.
Sensors (Basel). 2025 Aug 3;25(15):4785. doi: 10.3390/s25154785.
Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings.
鉴于水下云台系统恶劣的工作条件，其能源消耗管理尤为关键。本研究提出了一种具有自动唤醒机制的水下云台操作方法，该机制仅在检测到目标时才激活，取代了传统的基于定时器的触发方式。此外，与固定时长观测策略不同，我们引入了一种Q学习算法来优化操作模式。该算法根据周围生物活动频率动态调整工作模式：在生物出现稀疏的时期采用低功耗模式（以较低的监测强度降低能耗），而在生物活动频繁时切换到高性能模式（延长观测时长、增加能耗、提高监测强度）。仿真结果表明，与固定时长观测方案相比，所提出的优化策略在监测效果上提高了11.11%，同时实现了16.21%的节能。
