Target reconstruction and process parameter decision-making for bolt intelligent assembly based on robot and multi-camera

Abstract

Bolt assembly is widely used in modern manufacturing. And with the development of industrial automation, there is a growing demand for automatic assembly. To form a fully automatic and highly intelligent system for bolt assembly, a novel strategy based robot and multi-camera is proposed. In this strategy, a high assurance components reconstruction scheme is designed, wherein the Mask R-CNN is used for the identification and segmentation of the workpiece, then the shape, size and pose of the workpiece are determined by the local point cloud data and its envelope using basic geometries. The results show that the accuracy of workpiece recognition exceeds 96 %, the maximum positioning accuracy of the hole is 0.334 mm based on multi-camera system. Based on the historical fastening process data, an improved artificial jellyfish search optimizer (AJSO) BP neural network is proposed to automatically determine the optimal values for the bolt fastening speed and torque. The BP neural network optimized by AJSO reduces the maximum prediction error of process parameters from 0.117 mm/s to 0.051 mm/s for speed and from 0.052 N·m to 0.027 N·m for torque. Finally, the feasibility of the proposed strategy is demonstrated by an experimental case, which used a dual robot unit for the bearing seat assembly.