The automatic docking process of connector and rocket valve is studied, and a bias measurement system based on dynamic azimuth detection alignment technique is proposed. The deviation value of the connector with the fill valve is obtained and defined as the initial value. The image of the cooperative target is obtained by the optical camera. The camera pose in the two-dimensional plane is quickly extracted by identifying the direction of the target image and the marker size; and the laser sensors are used to accurately obtain the distance information between the target and the camera. The deviation value between the connector and the fill valve can be obtained in real time through this measurement system. It is applied to the automatic docking system, and the results show that the method meets the technical requirements of the automatic docking displacement accuracy 1 mm of the rocket.

Aiming at the problem that the vehicle odometer scale factor changes in real time due to the influence of tire pressure and temperature, a method of SINS (strap down inertial navigation system)/odometer integrated navigation based on strong tracking filter is presented. The property that the residual sequences of Kalman filter are mutually orthogonal is used to adjust fading factor adaptively. The strong tracking filter has the ability to track the sudden change state. The average velocity matching method is proposed to eliminate the influence of vehicle vibration and odometer quantization error on integrated navigation. The lever arm error is adopted into the state variables to eliminate the influence of inaccurate lever arm on integrated navigation when the vehicle is turning. The SINS/odometer integrated navigation model is built. The actual vehicle integrated navigation experiment is carried out. The results show that the positioning accuracy of integrated navigation is better than 1‰D.