The learning method of robot teaching sewing motion

Abstract

In order to realize the robot′s learning of teaching sewing motion, a robot motion learning method based on Gaussian Mixture Model (GMM) -Gaussian Mixture Regression (GMR) was proposed. The improved OPENPOSE model was used to recognize the teaching sewing movements, and the label fusion method was used to correct the joint point labels, so as to solve the problem of joint positioning failure caused by cloth occlusion during the sewing process. Taking the coordinates of the human upper limb joints as training samples of sewing motion, the trajectory samples were divided into motion primitives by time interval, and each motion primitive and its corresponding time were mixed-encoded using GMM to obtain the regression function of the Gaussian component. Besides, GMR was used to predict the connection among motion primitives to generate the sewing motion trajectory, update the Gaussian parameters of regression function, and realize the learning of worker′ upper limbs sewing motion. Through the simulation experiment of trajectory tracking and experiment comparison with Kalman method, the stability and effectiveness of the sewing motion learning method were verified.

Keywords

• sewing robot
• openpose model
• teaching movement
• gaussian mixture model
• gaussian mixture regression