Underwater robot multi-sensor fusion data processing method

Abstract

The invention discloses an underwater robot multi-sensor fusion data processing method. Attitude, position, and velocity information of an underwater robot is estimated based on a plurality of sensors; sensor measurement data with significant errors can be better eliminated by fusing all sensor measurement data, so that the underwater robot is not easily affected by single sensor failures, and theunderwater robot multi-sensor fusion data processing method is more suitable for nonlinear systems such as the underwater robot; and the angular deviation and velocity deviation are added to a statevector when constructing the state vector, and the influence of the angular deviation and the velocity deviation on state update is considered to accurately estimate the attitude, position and speed information of the underwater robot. The condition that the process noise is obtained from filtering results and observation results is assumed, and the differential evolution algorithm is used for performing optimization selection on the obtained process noise variance to improve the filtering accuracy. The underwater robot multi-sensor fusion data processing method is applicable to underwater robot type nonlinear systems to accurately estimate the attitude, position and speed information of the underwater robot.

Description

technical field [0001] The invention belongs to the technical field of underwater robot data processing, and in particular relates to an underwater robot multi-sensor fusion data processing method based on an extended Kalman filter algorithm of a differential evolution algorithm. Background technique [0002] There is huge economic potential in the ocean, which has attracted extensive attention from all over the world. In order to better detect and develop deep-sea resources, scholars from various countries have stepped up research and development of marine engineering detection devices and mining equipment. Among them, underwater robots have been widely used in military and civilian applications such as underwater target search, marine resource detection, and marine military missions. In practical applications, how to improve the maneuverability and maneuverability of underwater robots in complex underwater environments is an urgent problem to be solved. Research on multi-...

AI Technical Summary

Problems solved by technology

[0003] At present, the extended Kalman filter has been widely used in practical fields such as unmanned aerial vehicles, but due to the complexity and diversity of the underwater environment, the application of multi-sensor fusion data processing methods in underwater robots is still in a blank stage.

The extended Kalman filter assumes that the process noise and the observation noise are a constant matrix. However, in actual engineering, due to external force or magnetic interference, the working environment of the filter is not constant, and different environments correspond to the best noise. The matrix is ​​not the same, and the filter cannot correct the noise matrix in real time for these transformed environments, which brings errors to the estimation of attitude, position and speed, and the accuracy is not high.

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