With the rapid development of logistics and manufacturing industries, traditional handling robots can no longer meet practical needs. In response to this, for the rapid handling of diversified products, research first combines deep learning technology to improve the Double Actors Regularized Critics (DARC) algorithm and design a robot path planning method; Then, a Reachability Analysis-based Time Optimal Trajectory Planning (RA-TOP) algorithm is designed to generate the time optimal trajectory from the interpolated robot path, thereby efficiently achieving the task of rapid handling of diversified products by robots. The findings demonstrate that the enhanced DARC algorithm offers notable benefits in terms of path planning, resulting in shorter paths, reduced curvature, enhanced smoothness, a minimum path length of less than 20 meters, and fewer convergence times, surpassing the performance of alternative algorithms. The time trajectory generation algorithm has a shorter motion time, taking about 1.75 seconds under the same displacement, which is better than the comparison algorithm and can effectively avoid robot motion shaking. Compared with the comparative method, the obstacle avoidance trajectory of the research method is closer to the expected value, with an average deviation of about 0.5 m from the expected trajectory. The application results of the example show that under the research method, the success rate of the handling robot task is 94% or above. The above results indicate that robots can stably and dynamically avoid obstacles, generate optimal trajectories, meet the real-time path planning and efficient handling needs of enterprises, and improve production efficiency under the research method.

The aircraft final assembly is a complex system, encompassing various aspects and multidimensional production factors. These numerous factors are interconnected, significantly impacting the efficiency of the final assembly process. To investigate the interrelationships among various production factors, this study introduces a specialized fine-tuning large language model for aircraft final assembly, termed Aircraft Final Assembly ChatGLM (AFA-ChatGLM). This model is designed to automatically extract essential information regarding key production factors from process documentation. Furthermore, the FP-Growth algorithm is employed to uncover association rules between these production factors and the various stages of the final assembly. Experimental results indicate that our method demonstrates outstanding performance in the aircraft final assembly domain. Specifically, for the assembly process documents of the C919 large passenger aircraft, our proposed model achieved a Precision of 82.7%, Recall of 89.1%, and F1 score of 85.4%, representing a substantial improvement over traditional word segmentation methods. leveraging the superior performance of the model, we utilized association rule mining techniques to construct 44,851 high-confidence association rules for the final assembly line of the C919, laying a foundation for subsequent optimization of the production line.