<p>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&#xa0;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.</p>