自治水下机器人及其运动控制技术综述

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (11) : 321-338.

综述

自治水下机器人及其运动控制技术综述

孟繁贵1，刘爱民*1，胡岩1，王宇琛1，乔路宽1，张红奎1, 2

作者信息 +

Overview of autonomous underwater vehicles and their motion control technologies

MENG Fangui1, LIU Aimin*1, HU Yan1, WANG Yuchen1, QIAO Lukuan1, ZHANG Hongkui1,2

Author information +

文章历史 +

摘要

自治水下机器人（AUV）因其机动灵活、鲁棒性强、运动范围广等优势广泛应用于水文监测、海底科考、巡逻侦察等领域。为保安全高效地完成各种作业任务，AUV的运动控制技术研究显得尤为重要。本文按照AUV发展的时间进程，重点从运动控制相关角度对国内外AUV的典型产品进行介绍，并展示了团队最新研制的“盛鲸一号”AUV及其运动控制技术。根据AUV的运动控制研究现状，AUV的运动控制可分为路径跟踪、轨迹跟踪和镇定控制三种类型，其研究内容主要集中在制导方案的设计和控制器的优化上。AUV模型不确定、外界干扰和执行器饱和是影响运动控制系统的三大主要挑战。为应对这些难题，深度强化学习、滑模控制、自抗扰控制、神经网络、自适应控制、S面控制和模糊控制等智能控制技术已得到广泛应用，能够有效抑制AUV动力学模型变化以及海浪、洋流等环境干扰对跟踪精度的影响。针对执行器饱和问题，模型预测控制、深度强化学习和滑模控制技术表现尤为突出。现有研究显示，信息型AUV的数量较多，AUV的运动控制系统在精确性和鲁棒性方面具有显著优势。未来AUV的发展将聚焦于采用可再生能源驱动的长航程AUV和具备自主操纵机构的多模式AUV。同时，AUV的运动控制趋势将向低功耗运动控制和集群运动控制方向发展。

Abstract

Autonomous underwater vehicles (AUVs) are widely used in various fields such as hydrological monitoring, underwater exploration, and patrol reconnaissance, due to their mobility, robustness, and extensive operational range. To ensure the safe and efficient completion of various tasks, the research on motion control technology for AUVs is of paramount importance. This paper provides a chronological overview of the development of AUVs, focusing on typical products both domestically and internationally, with an emphasis on motion control technologies. Additionally, it presents the "Sheng Whale I" AUV developed by the team, along with its motion control technology. Based on the current state of AUV motion control research, strategies can be classified into path tracking, trajectory tracking, and stabilization control. Research in this field primarily focuses on the design of guidance schemes and the optimization of controllers. The main challenges affecting AUV motion control systems include model uncertainties, external disturbances, and actuator saturation. To address these challenges, intelligent control techniques such as deep reinforcement learning, sliding mode control, active disturbance rejection control, neural networks, adaptive control, S-plane control, and fuzzy control have been widely applied. These methods effectively mitigate the impact of changes in the AUV's dynamic model, as well as environmental disturbances like waves and ocean currents, on tracking accuracy. For actuator saturation issues, model predictive control, deep reinforcement learning, and sliding mode control have shown particularly promising results. Existing research indicates that information-based AUVs are numerous, and the motion control systems of AUVs demonstrate significant advantages in terms of accuracy and robustness. Future developments in AUVs will focus on long-range vehicles powered by renewable energy and multi-mode AUVs equipped with autonomous maneuvering capabilities. Additionally, the trend in AUV motion control will shift towards low-power motion control and swarm motion control.

导出引用

孟繁贵1, 刘爱民1, 胡岩1, 王宇琛1, 乔路宽1, 张红奎1, 2. 自治水下机器人及其运动控制技术综述[J]. 振动与冲击, 2025, 44(11): 321-338

MENG Fangui1, LIU Aimin1, HU Yan1, WANG Yuchen1, QIAO Lukuan1, ZHANG Hongkui1, 2. Overview of autonomous underwater vehicles and their motion control technologies[J]. Journal of Vibration and Shock, 2025, 44(11): 321-338

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