An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability.

For analyzing the influence of the system parameters on the density of the powder layers in laser powder bed fusion (LPBF) technology, an experimental method is proposed to improve the structure of the recoater in the powder laying system and optimize the parameters of the powder laying system. With this experimental method, the appropriate density of the powder layers can be attained. In the proposed experimental method, the recoater in the powder laying system was taken as the research object and the forces affecting the powder and recoater when the powder was in contact with the recoater were analyzed. The discrete element model of the powder laying system was established to simulate and analyze the influences of the recoater’s radius, translational velocity and angular velocity on the density of powder layers. In addition, orthogonal experiments were designed to discuss the magnitude of the influence of each of the powder laying system’s parameters on the density of powder layers. Finally, the optimized parameter combination plan was put forward. The results show that increasing the recoater’s radius can enhance the density of powder layers within a certain range; but, as the recoater’s radius is increased continuously, its impact on the recoater’s radius on f powder layers’ density decreases. When the translational velocity of the recoater rises, powder layers’ density increases first and then decreases. The coater’s angular velocity has little effect on powder layers’ density. Eventually, the optimized processing parameters were determined, which are 25 mm for the recoater’s radius, 30 mm/s for the recoater’s translational velocity, and 12 s⁻¹ for the recoater’s angular velocity. The results provide some significance and guidance in improving the recoater’s structure and optimizing the powder laying system’s parameters.

In industrial automation, an important task of robot system is to grasp moving objects. In order to grasp the moving target on the conveyor belt in the shortest time, this paper proposes a method of location prediction and interception grasping of the target object, and puts forward the corresponding time-optimal grasping point search algorithm. In order to reduce the motion impact of the manipulator and meet the kinematic constraints, the velocity planning curve of the normalized quintic polynomial was adopted to plan the motion of the manipulator in the joint space. In order to express the algorithm more clearly, the position-time function of the moving object and the position-time virtual function of the robot are introduced, and the time optimal grasping point appears at the intersection of the two functions. Finally, the method is verified by simulation analysis.