Data fusion of local and global sensors within a Global Reference System in aeroplane assembly

Currently, aeroplane assembly is carried out in rigid, aeroplane-specific assembly lines which causes high costs for reconfiguration. One approach to reduce the costs is to use automated lineless assembly systems. To enable automated reconfiguration of assembly processes, continuous availability of factory-wide position data is required. When providing these data, a challenge is the combination of large-volume, "global" measurement sensors and high-precision, "local" measurement sensors to a single easily interpretable indication. The aim of this research work is the fusion of local and global measurement data within a so-called Global Reference System (GRS). For data fusion, sensors are linked to a reference measurement system covering the entire assembly area. The relationships between the coordinate systems of the individual measurement systems are described using homogeneous transformations. Using Kalman filters, the transformed measurement data are fused into a vector containing information about the pose of aeroplane components. Furthermore, a GUM-compliant statement about the measurement uncertainty of the fused sensor data is made by specifying the covariance matrix. The method is validated using a demonstrator covering the essential aspects of the assembly process. The result is a validated procedure for data fusion and for the determination of the combined measurement uncertainty in a GRS.