水下滑翔机自适应覆盖采样  

Adaptive Coverage Sampling of Underwater Glider

作　　者：朱心科[1,2] 俞建成[1] 王晓辉[1] 

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [2]国家海洋局第二海洋研究所国家海洋局海底科学重点实验室,浙江杭州310012

出　　处：《机器人》2012年第5期566-573,580,共9页Robot

基　　金：中科院知识创新工程资助项目(KZCX2-YW-JS205);机器人学国家重点实验室课题(2009-Z05);国家海洋局青年海洋科学基金资助项目(2011333);国家海洋局第二海洋研究所基本科研业务费专项资助项目(JT1001);浙江省自然科学基金资助项目(LQ12D06002)

摘　　要：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.

关 键 词：水下滑翔机 覆盖采样 质心Voronoi图 加权最小二乘估计 自适应遗忘因子 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]