MC-ROV导航系统研究

针对研制的新型模态切换水下机器人(Mode-Converted ROV, MC-ROV),设计了一套以MEMS器件为主的微惯性组合导航系统,包括陀螺仪、加速度计、磁力计、深度传感器及微处理器等。系统采用互补滤波方法抑制陀螺漂移,设计卡尔曼滤波器计算姿态角。本文采用了改进的自适用卡尔曼滤波器,增大新近数据的作用,减小陈旧数据的作用,避免滤波发散,提高导航精度。水池实验表明结合互补滤波、自适应卡尔曼滤波能够获得比较精确、稳定的水下机器人导航信息。同时,基于实测数据进行的算法仿真表明改进后的渐消记忆指数加权自适应卡尔曼滤波可以在一定程度上改善导航效果。

Navigation System Research of MC-ROV

For the novelty Mode-Converted ROV(MC-ROV), a set of MEMS-based integrated inertial navigation system has been developed, including gyroscope, accelerometer, magnetic compass, depth sensor and a micro-controller. Utilizing complementary filter to restrain the drifts of gyroscope while Kalman filter is designed to estimate attitude angles. This paper applies an improved adaptive Kalman filter (AKF), stressing the effects of new data with adding weights to gradually leave old data behind to avoid divergence, to further improve the navigation effects. The pool experimental results present that complementary filter and Kalman filter can get stable and high-precision effects. Meanwhile, algorithm simulations based on the real measured data demonstrate that the improvement of fading memory AKF based on exponential weighting can better the navigation results to some degree.