In aircraft assembly, multiple laser trackers are used simultaneously to measure large-scale aircraft components. To combine the independent measurements, the transformation matrices between the laser trackers’ coordinate systems and the assembly coordinate system are calculated, by measuring the enhanced referring system (ERS) points. This article aims to understand the influence of the configuration of the ERS points that affect the transformation matrix errors, and then optimize the deployment of the ERS points to reduce the transformation matrix errors. To optimize the deployment of the ERS points, an explicit model is derived to estimate the transformation matrix errors. The estimation model is verified by the experiment implemented in the factory floor. Based on the proposed model, a group of sensitivity coefficients are derived to evaluate the quality of the configuration of the ERS points, and then several typical configurations of the ERS points are analyzed in detail with the sensitivity coefficients. Finally general guidance is established to instruct the deployment of the ERS points in the aspects of the layout, the volume size and the number of the ERS points, as well as the position and orientation of the assembly coordinate system.

Purpose – The purpose of this paper is to propose a robot-assisted assembly system (RAAS) for the installation of a variety of small components in the aircraft assembly system. The RAAS is designed to improve the assembly accuracy and increase the productive efficiency. Design/methodology/approach – The RAAS is a closed-loop feedback system, which is integrated with a laser tracking system and an industrial robot system. The laser tracking system is used to evaluate the deviations of the position and orientation of the small component and the industrial robot system is used to locate and re-align the small component according to the deviations. Findings – The RAAS has exhibited considerable accuracy improvement and acceptable assembly efficiency in aircraft assembly project. With the RAAS, the maximum position deviation of the component is reduced to 0.069 mm and the maximum orientation deviation is reduced to 0.013°. Social implications – The RAAS is applied successfully in one of the aircraft final assembly projects in southwest China. Originality/value – By integrating the laser tracking system, the RAAS is constructed as a closed-loop feedback system of both the position and orientation of the component. With the RAAS, the installation a variety of small components can be dealt with by a single industrial robot.

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In the digital drilling and riveting process of complex surfaces such as aircraft panels, the reference hole is pre-drilled on the skin surface. Generally, four laser displacement sensors (LDSs) are used as a group for normal adjustment. The camera vision is used to determine the position of the reference hole to obtain the accurate positioning of the drilling position. However, while dealing with large curvature complex panel surface and panel edge, four LDSs have the problem of reflection laser disappearance or matching failure. Applying two LDSs for normal adjustment on one side, the projection of the reference hole on the camera focal plane is an ellipse which means a further normal adjustment is desired in the direction of the ellipse's minor axis. Therefore, this paper proposes a 3-dimensional pose estimation method (TDPEM) combining multi-sensor fusion and space geometry to realize the normal adjustment and position measurement of reference holes with a monocular camera and two LDSs. Firstly, two LDSs are used to adjust the reference hole's horizontal (or vertical) direction. And then, an ellipse contour extraction algorithm is proposed to determine the ellipse parameters. Finally, the pose of the reference hole on the panel is determined by a spatial circle reverse algorithm. The experiment proves that the position error and angle error between this algorithm and the traditional four-LDS-based measurement method are within 0.03 mm and 0.2°, respectively, which verifies the feasibility and reliability of this algorithm.