Abstract
In this paper, a novel relative position and orientation (R-P&O) measurement method for large-volume components is proposed. Based on the method, the parallel distances between the cooperative point pairs (CPPs) are collected by multiple pairs of wireless ranging sensors which are installed on respective components and finally turned into the R-P&O. Accordingly, a measurement model is built and an algorithm is designed to solve the model, in which the radial basis function neural network (RBFNN) produces a preliminary solution by offline training and the differential evolution (DE) strategy finds the accurate solution by online heuristic searching. Furthermore, the crucial parameters and the performance of the algorithm are analyzed through simulating a virtual alignment process which proves that the RBFNN-DE algorithm can quickly and accurately find the global optimal solution in the whole effective workspace. Besides the theory study, a ranging device based on ultrasound has been developed along with a calibration method. Depending on the device, an experiment of actual alignment is implemented to verify the algorithm. Experimental results indicate that the error of R-P&O is no more than 4.1 mm and 0.32° when the ranging error is 0.1 mm, compared with the measurement result of indoor GPS (iGPS).
Highlights
In aviation, aerospace, and ship manufacturing, the product assembly is characterized by large volume, high accuracy, and complex processes
The common digital measuring instrument includes a laser tracker [3, 4], theodolite [5], indoor GPS (iGPS) [6, 7], industrial camera [8, 9], coordinate measuring machine (CMM) [10, 11], and so on, which depends on transforming the relevant points into the reference coordinate system of a third party to fit the relative position and orientation (R-P&O) of components [12]
A number of wireless sensors are installed on cooperative point pairs (CPPs) of the components to measure the distances, which provide support for calculating the R-P&O
Summary
Aerospace, and ship manufacturing, the product assembly is characterized by large volume, high accuracy, and complex processes. The traditional method is generally to figure out the geometric size of the component by standard gauges with analog transfer, which has been unable to meet the increasing requirements of alignment It is of great theoretical and practical value to research for a high-accuracy digital measurement system for the alignment process of a large-volume component [1, 2]. RBFNN has the characteristics of approaching any nonlinear function with arbitrary-precision and having a good global approximation ability It is suitable for the solution of multidimensional parameters and can be adjusted by changing the number and width of network nodes.
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