Combining finite element analysis and reinforcement learning for optimal grip point planning of flexible components

Abstract

Handling large flexible components is still a challenge in many industries. Examples include the handling of fibre-reinforced plastics or the assembly of industrial-scale electrolytic cells. Difficulties often arise in the design of suitable endeffectors. Inefficient gripping point design, i.e. the total number and positioning of grippers, can lead to increased stress and deflection of the component being handled. To counteract this, endeffectors are often oversized resulting in the use of more grippers than needed. Correspondingly, heavier moving masses imply longer handling times as well as higher energy consumption. This paper presents a process for planning and optimising gripping points for large flexible components. In addition to the shape of the component, actual dynamic loads of the handling path are also taken into account. The key element to the process is an optimisation algorithm based on reinforcement learning and trained using an finite element method (FEM) simulation. After computing a desirable starting configuration, the algorithm optimises the placement of gripper positions while aiming for a reduced total number. In addition, the optimisation has prescribed handling limits, such as physical and geometric constraints, that must not be exceeded. It was shown that the algorithm satisfactorily optimises the gripping points for dynamic loads for different materials and shapes. Furthermore, it has been shown that the computation of an initial configuration yields preferable results for simple components, yet requiring optimisation in the case of more complex shapes.