A convolutional neural network based method for fork type wing fuselage docking

The fork-ear connection attachment method is a widely used technique for attaching the fuselage to the wing. During the fork-ear wing-fuselage docking process, it is essential to ensure that the fork-ear holes in the fuselage and wing are precisely aligned before inserting the mounting pins to complete the final connection. Thus, the alignment accuracy of the fork-ear holes is crucial during the assembly process. When employing conventional visual servo methods to determine the pose adjustment for fork-type wing-fuselage docking, it is necessary to establish a conversion relationship between the image, camera, and posture adjustment mechanism. However, this process can introduce various nonlinear errors that are challenging to compensate for through mathematical modeling. To address this issue, this paper proposes a method for nonlinear mapping estimation of the pose adjustment amounts and develops a pose adjustment estimation convolutional neural network (PAAECNN) with superior performance validated through experiments and discussions. PAAECNN achieves end-to-end mapping from the fork-ear intersection hole images to the pose adjustment amounts for fork-ear wing-fuselage docking. The experimental results demonstrate that the proposed method for estimating the pose adjustment amounts in fork-ear wing-fuselage docking is highly accurate and suitable for visually guided docking tasks.