Research on autonomous dynamic real-time capture of robot based on hybrid prediction model

Abstract

The trajectory of the dynamic target is an important basis for the robot to capture the action, and its advanced prediction precision plays a decisive role in achieving efficient capture. In this paper, a dynamic adaptive weighting method is proposed to establish a hybrid prediction model of GM (1,1) and SESM. The weights are allocated in real time, according to the prediction accuracy to improve the prediction accuracy of a single model and enhance the applicability of spatial trajectory prediction. Then, based on the rolling prediction principle, a high-precision advanced prediction method of the hybrid model is created. Compared with the advanced prediction of the model itself, the average prediction errors of the spatial arc and straight line verification trajectories are reduced by 40.26% and 49.77% respectively. The simulation model of robot dynamic target capture is built in Matlab, and the measured trajectory is used as the target trajectory. The robot capture experiment platform is built, and the target motion trajectory is collected by the camera in real time to capture the experiment, which verifies the effectiveness of the proposed method in actual capture.