Robotic Layup of Woven Composite on Double Curvature Surface Parts Based on Imitation Learning

In layup of woven composite on double curvature surface, the manual layup is of low efficiency and poor quality uniformity, while research findings on robotic layup are not accurate enough to describe manual layup skills. Therefore, a method of acquisition, representation and recurrence of woven composite layup skills is proposed based on imitation learning. Firstly, the dragging demonstration method is used to obtain trajectory information of woven layup, and supervised trajectory segmentation is carried out based on pressure threshold. After that, probabilistic models of layup trajectory are learned by Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). On this basis, the normal orientation of the end-effector is calculated taking demonstration orientation and surface norm as reference. Then, another demonstration is carried out to obtain normal pressure information, and probabilistic models of layup pressure are learned though GMM/GMR. For the establishment process of contact status, a pre-control quantity exit mechanism is proposed within the framework of hybrid force/motion control to accomplish non-impact switching of the controllers during the establishment of contact status. Finally, an experiment platform is built based on UR5e robot, ATI six-dimensional force/torque sensor and ROS (robot operating system). The result shows that the proposed skills acquisition and description method can realize accurate extraction and expression of manual layup experience, and the maximum pressure error during contact status establishment is 1.8 N when using the proposed pre-control quantity exit mechanism, which is less than 2.0 N, the root mean square error of layup pressure of all 16 trajectories on the curvature surface mould. The finished layer has no visible defects such as bulge and folding. At about 97.3% of points in the woven point cloud measured by depth camera, their distances from the mould point cloud are less than 1.0 mm. © 2022 Chinese Academy of Sciences. All rights reserved.