基于准最大最小模型预测控制的AUV视觉对接

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视觉由于能见度低导致深度信息存在不确定性的影响,建立新的六自由度AUV视觉伺服模型;然后,结合AUV运动和图像特征运动的测量数据,设计在线深度估计器,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,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,通过仿真验证所提出方法的有效性和优越性.

Quasi-min-max MPC algorithm for visual docking of an autonomous underwater vehicle

In this paper, a quasi-max-min predictive control(QMM-MPC) algorithm based on depth information is proposed for visual docking of the 6-DOF autonomous underwater vehicle(AUV), which can effectively improve the performance of the complex underwater visual servo docking system. Firstly, a new 6-DOF visual servo model for the underwater AUV is established to solve the problem of the uncertainty of depth information caused by the low visual visibility of the underwater 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, which can reduce the strong conservatism caused by one upper bound of the 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.