This study investigated the trajectory-planning problem of a six-axis robotic arm based on deep reinforcement learning. Taking into account several characteristics of robot motion, a multi-objective optimization approach is proposed, which was based on the motivations of deep reinforcement learning and optimal planning. The optimal trajectory was considered with respect to multiple objectives, aiming to minimize factors such as accuracy, energy consumption, and smoothness. The multiple objectives were integrated into the reinforcement learning environment to achieve the desired trajectory. Based on forward and inverse kinematics, the joint angles and Cartesian coordinates were used as the input parameters, while the joint angle estimation served as the output. To enable the environment to rapidly find more-efficient solutions, the decaying episode mechanism was employed throughout the training process. The distribution of the trajectory points was improved in terms of uniformity and smoothness, which greatly contributed to the optimization of the robotic arm’s trajectory. The proposed method demonstrated its effectiveness in comparison with the RRT algorithm, as evidenced by the simulations and physical experiments.

The strengthening effect and uniformity of residual stress and strain of 7075-T6 aviation aluminum after laser shock peening were investigated. The finite element simulation model of laser shot peening on 7075-T6 aviation aluminum alloy was established and verified. On this basis and combined with experiment, the microstructure, residual stress distribution and equivalent strain distribution of 7075-T6 aviation aluminum alloy after laser shock peening were studied. An index U for evaluating the strain uniformity in the peened area was proposed based on the statistical processing of the equivalent strain distribution in the peened area. The results show that the average grain size on the peened surface of the specimen was refined to 20–30 μm after LSP, and a residual compressive stress layer with thickness of about 0.5 mm was induced on the surface. Grain refinement and residual compressive stress can promote the strength and fatigue properties of 7075-T6 aviation aluminum alloy. The minimum value of U is obtained in the 6 × 6 spot array (the corresponding overlap ratio is 58%), which suggest that the most uniform deformation peened area can be obtained by using 58% overlap ratio in the laser shot peening process, which can avoid stress concentration and improve fatigue life.