A Manipulator Grasping Method Based on Mixture of Gaussian Processes Model

A manipulator grasping method based on the mixture of Gaussian process (MGP) model is proposed, in order to avoid the shortcomings of the commonly used vision-based method, such as the cumbersome visual calibration and the difficulties in the inverse kinematics solution. In the learning phase, the MGP model is used to directly construct the mapping from the pose of the target object to the joint angles of the manipulator. In the grasping phase, the pose of the target object is firstly captured by the camera. Secondly, the generating probability of the pose under each Gaussian component is calculated respectively. Finally, the Gaussian process regression is selected to calculate the corresponding joint angles, which is corresponding to the Gaussian component with the maximum posterior probability. When the positioning tolerance is 20 mm, the success rate of grasping simulation reaches 93.3%, and the success rate of actual grasping reaches 88.3%. For the grasping with low precision, this method can realize the rapid deployment and the use of the robot.