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

• Published in: International journal of advanced manufacturing technology 2023-03, Vol.125 (5-6), p.2225-2242
• Main Authors: Zhu, Chenxi, Wan, Xiao-Jin, Zhou, Zhengjie
• Format: Article
• Language: English
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Computing costs; Deformation; Design optimization; Engineering; Grasping (robotics); Industrial and Production Engineering; Layouts; Mechanical Engineering; Media Management; Metal sheets; Multiple robots; Optimization; Original Article; Robots; Search algorithms
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-10835-1

Automobile flexible sheet metal parts are prone to produce non-negligible grasping deformation, 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. First, 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 point optimization of a certain car door sheet part is utilized as a case to validate the proposed method and we have designed an experimental system to test the proposed grasp method on a curved sheet part. 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.