In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5–74% of the results is better than the ROA, and the robustness results of 66.67–75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.

Owing to the assembly state changes during aircraft assembly processes, assembly force-deformation problem occurs. To obtain the structure shape in the product assembly process efficiently and accurately, a three-dimensional (3D) mapping technology for the structural deformation during the aircraft assembly process is proposed combined with a fiber Bragg grating (FBG) optical fiber sensor and binocular vision measurement system. First, this study established a curvature transformation model using optical fiber monitoring data, obtained the 3D spatial deformation of the product, and completed the unification of the optical fiber wavelength change and spatial 3D point coordinate heterogeneous data. Second, a mesh deformation optimization algorithm based on point-cloud optimization was established. Subsequently, the deformation effects of four mesh deformation models were compared to verify the feasibility and accuracy of HEC-Laplace, and the 3D mapping of the product structure shape in the assembly process was realized. Finally, a cantilever wing model was used to verify the deformation of different loading modes. The results show that the product structure changes can be accurately obtained through the proposed technology, thereby improving the accuracy control and overall assembly quality in the aircraft assembly process and providing a theoretical basis for intelligent aircraft assembly.

With the advent of the "Industry 4.0" era, the requirements for measurement efficiency, size and accuracy in the assembly process of large-scale equipment have been continuously improved. In the assembly process of large-scale equipment, due to the limitations and occlusions of components and assembly tooling, a single laser tracker cannot complete the measurement of all target points. Therefore, it is necessary to use multiple trackers to work together to build a measurement network covering the entire assembly space. In this paper, through the forensic-based investigation optimization algorithm, the three-dimensional coordinates of the stations of multiple laser trackers are used as the high-dimensional input parameters, the side of the bounding box of the large-scale measurement field is used as the feasible region of the high dimensional parameters, the ray accessibility of the laser tracker is used as the statistical result, the number of public transit points of adjacent stations of the laser tracker and the number of visible digital-analog key points are used as constraints, the ratio of the number of visible points of multiple laser trackers to the total number of points is used as the objective function, the optimization is carried out in the form of greedy iterative fitness function, and finally the maximum coverage rate under the measurement network is obtained in complex environments such as tooling occlusion. In a word, this method shortens the station layout time and the overall assembly period through autonomous calculation, and has certain practical significance for improving the assembly efficiency of large-scale equipment.