A Novel Graph Neural Network Approach for Inverse Kinematics in Robotic Arms

Abstract

The accurate prediction of kinematic configurations, including joint limit violations, collisions, and trajectory anomalies, is essential for ensuring the safety and efficiency of 6-DOF robotic arms in industrial applications. In this study, we develop a novel inverse kinematics (IK) solver based on a data-driven approach utilizing Graph Neural Networks (GNNs). Our model effectively captures the complex spatial relationships governing kinematic behavior by representing the robotic system as a graph—where nodes correspond to joints and edges represent physical linkages. Trained on a dataset of direct kinematics, our GNN-based model infers joint angles given an end-effector position with high accuracy and efficiency. The proposed approach achieves an accuracy of 92% with a 100.0% success rate and a computational runtime of 2.4 ms, outperforming conventional numerical and optimization-based IK solvers. These results highlight the potential of GNNs in real-time inverse kinematics prediction, enabling improved motion planning, reduced computational costs, and enhanced adaptability in dynamic environments. This research paves the way for more efficient and scalable solutions in industrial automation, human-robot collaboration, and autonomous robotic systems.