Two algorithms that are distinct from the closed algorithm are proposed to create the inverse kinematics model of the UR10 robot: the Sequential Quadratic Programming (SQP) algorithm and the Back Propagation-Sequential Quadratic Programming (BP-SQP) algorithm. The SQP algorithm is an iterative algorithm in which the fundamental tenet is that the joint’s total rotation radian should be at a minimum when the industrial robot reaches the target attitude. With this tenet, the SQP algorithm establishes the inverse kinematics model of the robot. Since the SQP algorithm is overly reliant on the initial values, deviations occur easily and the solution speed, and the accuracy of the algorithm is undermined. To assuage this disadvantage of the SQP algorithm, a BP-SQP algorithm incorporating a neural network is introduced to optimize the initial values. The results show that the SQP algorithm is an iterative algorithm that relies excessively on the initial values and has a narrow range of applications. The BP-SQP algorithm eliminates the limitations of the SQP algorithm, and the time complexity of the BP-SQP algorithm is greatly reduced. Subsequently, the effectiveness of the SQP algorithm and the BP-SQP algorithm is verified. The results show that the SQP and BP-SQP algorithms can significantly reduce the operation time compared with the closed algorithm, and the BP-SQP algorithm is faster but requires a certain number of samples as a prerequisite.

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.