水下滑翔机自适应覆盖采样

Adaptive Coverage Sampling of Underwater Glider

针对水下滑翔机对动态、未知的海洋特征的采样问题,提出了自适应覆盖采样算法.首先,定义了基于质心Voronoi分割采样空间的最优覆盖采样准则;然后,设计了在线参数估计算法,利用带遗忘因子的递归最小二乘法估计海洋特征参数;最后,设计了分布式控制算法,能够保证各个水下滑翔机从任意的初始位置收敛于定义的最优的覆盖采样网络配置.利用仿真实验对上述方法进行了有效性验证,结果表明本文提出的算法能够更好地完成对动态海洋特征的覆盖采样.

An adaptive coverage sampling algorithm is proposed for solving the dynamic and unknown ocean phenomenon by underwater gliders. Firstly, the criterion for optimal coverage sampling is defined based on the centroidal Voronoi partition of the sampling space. Secondly, recursive least square with forgetting factor is used to estimate the parameters of ocean phenomenon online. Finally, the distributed control law is proposed, which can guarantee the sampling network composed of underwater gliders to converge to the optimal sampling network config defined from the random initial state. The simulation experiment is carded out to show the effectiveness of the proposed method. The results show that the proposed adaptive coverage sampling strategy has a better performance in the sampling of dynamic ocean phenomenon.