Multistation Layout Optimization Method Based on Genetic Algorithm-Sequence Quadratic Programming.

In the domain of large-scale precision measurement, laser tracer multistation measurement systems are extensively utilized due to their superior accuracy and efficiency. Within multistation measurement, system layout is a pivotal factor influencing measurement accuracy, making its optimal design essential for enhancing the performance of multistation technologies. This research introduces a multistation layout optimization method based on a genetic algorithm combined with sequence quadratic programming. This approach formulates an objective function centered on the positional accuracy attenuation factor and develops a hybrid optimization model by integrating genetic algorithms with sequence quadratic programming. The model incorporates constraints such as the maximum measurement length and angle of the tracer as well as the measurement space range. Initially, it identifies the potential area through a global scope analysis, followed by utilizing the sequence quadratic programming method to ascertain the precise optimal station configuration within the designated area. Multistation measurement and comparative validation experiments were conducted by designing measurement points within a (200 × 200 × 150 mm3) workspace, employing a coordinate measuring machine as the experimental subject. The results indicate that the optimization reduced three-axis positioning errors by 60.8%, 38.1%, and 25.7%, respectively. The present findings confirm the effectiveness of the proposed method and highlight its extensive engineering application potential.