基于准最大最小模型预测控制的AUV视觉对接
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作者单位:

大连海事大学

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中图分类号:

TP29

基金项目:

辽宁省博士启动基金项目j基金号2021-BS-287,大连市科技创新基金基金号2019J12GX040,


Quasi-min-max MPC Algorithm for Visual Docking of an Autonomous Underwater Vehicle
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Affiliation:

Dalian Maritime University

Fund Project:

Doctoral Program of Liaoning Province[grant number 2021-BS-287]; Dalian Science and Technology Innovation Fund [grant number 2019J12GX040];

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    摘要:

    本文针对六自由度自主式水下机器人(Autonomous Underwater Vehicle,AUV)视觉对接这一重要课题,提出一种基于融合深度信息的改进准最大最小模型预测控制(Quasi-min-max model predictive control,QMM-MPC)方法,有效提高复杂水下视觉伺服对接系统性能.首先,针对水下AUV视觉能见度低,导致深度信息存在不确定性的影响,建立了新的六自由度AUV视觉伺服模型,该模型更加符合实际中AUV的水下弱光工况.然后,结合AUV运动和图像特征运动的测量数据,设计了在线深度估计器.同时,提出结合多李雅普诺夫函数的QMM-MPC算法,通过求取凸多面体中各顶点不同上界值,来降低传统QMM-MPC算法中单李雅普诺夫函数上界所带来的强保守性.最后通过仿真验证了方法的有效性和优越性.

    Abstract:

    In this paper, a quasi-max-min predictive control (QMM-MPC) algorithm based on depth information is proposed for visual docking of 6-DOF autonomous underwater vehicle (AUV), which effectively improves the performance of complex underwater visual servo docking system. Firstly, a new 6-DOF visual servo model for underwater AUV was established to solve the problem of the uncertainty of depth information caused by the low visual visibility of underwater AUV. The model was more consistent with the actual underwater low-light conditions of AUV. Then, an online depth estimator is designed by combining the measurement data of AUV motion and image feature motion. Meanwhile, a QMM-MPC algorithm combining multi-Lyapunov functions is proposed. This algorithm can reduce the strong conservatism caused by one upper bound of Lyapunov function in the traditional QMM-MPC algorithm by solving the different upper bound of each vertex in the convex polyhedron. Finally, the simulation results show the effectiveness of the proposed algorithm.

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  • 收稿日期:2021-11-26
  • 最后修改日期:2022-03-14
  • 录用日期:2022-03-15
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