Forward Kinematics for Parallel Platforms Based on the Integration of Residual Networks With Self-Attention and Error Compensation

Parallel platforms are widely used in modern industry due to their superior dynamic and static properties. However, different parallel platforms have different closure structures, and their forward kinematics equations have high nonlinearity, leading to poor generalization, low efficiency, and precision in solving these equations. This study presents a novel RNSA-EC algorithm designed to enhance the computational precision and efficiency of forward kinematics for parallel platforms. The algorithm integrates a Self-Attention optimized Residual Network (RNSA) with an Error Compensation (EC) method. The RNSA model initially achieves high-precision nonlinear mapping from joint coordinates to pose coordinates of parallel platforms. Subsequently, the inverse kinematic model is employed to develop an EC method, which further reduces computational errors in the forward kinematics of the parallel platform by iteratively updating the inputs to the RNSA model. We applied the RNSA-EC algorithm to solve the forward kinematics of two 6-degree-of-freedom (6-DOF) parallel platforms with distinct structures. The results demonstrate that the algorithm is approximately 60%, 50%, and 45% more efficient than the Newton-Raphson (NR), the hybrid Backpropagation Neural Network (BPNN)-NR, and the Iterative Artificial Neural Network (IANN) methods, respectively, on the two parallel platforms. Furthermore, the algorithm can be extended to solve forward kinematics for parallel platforms with different structures and DOFs.