模态切换水下机器人微惯性导航分析

针对某模态切换远程遥控水下机器人(Model-Converted Remotely Operated Vehicle,MC-ROV),基于微机电系统(Micro-Electro-Mechanical Systems,MEMS)器件设计微惯性组合导航系统.该系统包括陀螺仪、加速度计、磁罗盘、深度传感器和微处理器等.采用互补滤波方法抑制陀螺漂移,基于四元数算法对陀螺仪积分,并以四元数为估计对象设计无损卡尔曼滤波算法,从而提高导航精度.分析梯度下降法原理,并研究其在四元数更新中的补偿作用.水池试验表明:互补滤波与无损卡尔曼滤波相结合的方法能够获得比较精确、稳定的MC-ROV导航信息.基于实测数据的算法仿真表明梯度下降法可以在一定程度上改善导航效果.

Analysis on micro-inertial navigation of model-converted remotely operated vehicle

In terms of the MC-ROV underwater vehicle, a set of MEMS-inertial integrated navigation system has been developed, including gyroscope, accelerometer, magnetic compass, depth sensor and a micro-controller. Utilizing complementary filter method to restrain the drifting of gyroscope, while the quaternions were introduced into integration algorithm. And selecting the quaternions as estimating objects, an Unscented Kalman Filter (UKF) algorithm was competed, resulting in an enhancement of navigation accuracy. Moreover, a detail analysis was done for the gradient descent method in this paper, researching its function in the updating of quaternion. The pool experimental results present the method that combines complementary filter with Kalman filter gains stable and high-precision effects. Meanwhile, algorithm simulations based on measured data demonstrate that the gradient descent method can improve the navigation effects in a certain degree.