A Hybrid Method Using FABRIK and Custom ANN in Solving Inverse Kinematic for Generic Serial Robot Manipulator

Abstract

Abstract Solving Inverse kinematic(IK) of general robot manipulators remains significant challenge in current industrial manufacturing, particularly in human-robot collaborative scenarios. Most current approaches employ numerical, analytical or machine learning methods to solve IK. However, accurately determining the end-effector(EE) position and capable of handling multiple solutions are unresolved challenges. In this paper, we propose a hybrid method that combines Forward and Backward Reaching Inverse Kinematics(FABRIK) with a custom Artificial Neural Network(ANN) to solve IK for a broad range of serial robot manipulators. The results demonstrate that the hybrid method yields a unique solution and achieves a lower position error(up to 0.002 inches) compared to a standard ANN implementation. Furthermore, compared to the numerical method(FABRIK), the hybrid approach offers a more versatile framework for solving IK, resulting in superior overall performance in terms of solving complexity and accuracy among the three methods.