Grasping point optimization for sheet metal part based on GSA-Kriging model in a multi-robot assembly system

Abstract

Automobile flexible sheet metal parts are prone to produce non-negligible grasping deformation, which influences the location accuracy of the part during robot assembly operation, and choosing an appropriate layout of grasping points is an important means to reduce the grasping deformation for sheet metal parts. It is challenging to optimize the layout of grasping points efficiently and accurately because of the large number of robot fingers due to a large size sheet part. In order to improve the optimization efficiency and reduce the computational cost, this paper proposes a two-stage optimization design method for the layout of grasping points based on GSA-Kriging surrogate model. Firstly, the number of robot fingers and their feasible range are determined according to the stability of robotic grasping operation and the degree of deformation at different positions. Then according to the position distribution of grasping points, they are divided into two stages for optimization.Finally, GSA-Kriging surrogate model and gravitational search algorithm (GSA) are used to find the optimal layout of grasping points. In this paper, the layout of grasping points optimization of a certain car door sheet part is utilized as a case to validate the proposed method. The result shows that the GSA-Kriging surrogate model is more accurate and more stable than Kriging and other surrogate models. At the same time, the two-stage optimization method improves the optimization efficiency while reducing the calculation cost and burden.