An Efficient Computational Approach for Inverse Kinematics Analysis of the UR10 Robot with SQP and BP-SQP Algorithms

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.

Automated summary

Two algorithms, SQP and BP-SQP, are proposed for creating the inverse kinematics model of the UR10 robot. The SQP algorithm relies heavily on initial values, leading to deviations and reduced solution speed. To mitigate this, the BP-SQP algorithm incorporating a neural network optimizes the initial values. Results show that SQP has a narrow range of applications, while BP-SQP eliminates limitations and reduces time complexity. Both algorithms significantly reduce operation time compared to the closed algorithm, with BP-SQP being faster but requiring a certain number of samples as a prerequisite.