Visual–haptic attention fusion based flexible printed circuit position identification in industrial robotic mobile phone assembly

Despite various methods in robotic operation, it remains a great challenge for industrial mobile phone flexible printed circuit (FPC) assembly due to the extremely stringent demand in position identification for the very tiny FPC assembly targets under strict assembly tolerance. To this end, this work proposes an accurate FPC position identification strategy in industrial robotic mobile phone assembly. The contributions of the work are concluded as follows: (1) we construct a Multi-Head Attention (MHA) architecture with the objective of encoding the visual–haptic information of the position into compact fusion presentations; (2) the distance and rotation errors between the ideal FPC position and the varying mismatched FPC positions around the correct one are connected to the Multi-Head Attention decoder information in a regression manner; (3) a dynamic weight averaging (DWA) strategy is adopted to adjust the weights in the loss calculations, which is able to achieve a better balance between the position and rotation errors in loss regression. Experiments were conducted on a practical FPC assembly platform. The results show that the proposed method can significantly improve the FPC location accuracy under the limited requirements of assembly attempts. The possibility exists that the proposed method applies to the real mobile phone assembly lines to reduce the labor burden in the near future.