摘要
考虑到深海底的复杂、未知工作环境,在深海集矿机的海底导航与定位研究中提出了一种基于多传感器数据融合思想的深海集矿机导航与定位方法,该方法使用一种修正的长基线水声定位系统,采用自适应卡尔曼滤波算法,融合由长基线水声定位系统对集矿机位置的测量值和通过多普勒测速仪等其他传感器的测量值间接得到的关于集矿机位置的推测值,得出某时刻集矿机位置的最优估计;此方法适合集矿机在复杂未知的深海底环境中实现自主导航与定位任务;仿真和试验结果印证了此方法的有效性。
In the research of navigation and location about deep-seabed mining robot vehicle in unknown and complex environment, a method based on multi-sensor data fusion is presented. This method is based on a kind of correctional long baseline positioning system and introduces adaptive Kalman filter algorithm in order to fuse the conjectural value of positions by the long baseline positioning system and the conjectural value of positions conjectured by Doppler sensor and other sensors, The optimization estimate about the position of the operating robot is obtained. This method is valid in unknown and complex of deep-seabed environment for the job of navigation and location, The effectiveness of this method is verified by simulation and experiment results.
出处
《计算机测量与控制》
CSCD
2007年第8期1003-1005,共3页
Computer Measurement &Control
基金
国际海底区域研究开发"十五"项目(DY105-03-02-06)
国家重点基础研究发展规划项目(2002CB312203)
国家自然科学基金项目(60505018)
关键词
深海集矿机
多传感器数据融合
卡尔曼滤波器
导航
定位
deep-seabed mining robot vehicle
multi-sensor data fusion
Kalman filter
navigation
location
作者简介
王随平（1956-），男，教授，主要从事人工智能，现场总线，计算机控制系统方向的研究。
