Investigating Quantum Artificial Neural Networks for Singularity Avoidance in Robotic Manipulators

Abstract

This research explores the application of quantum-inspired neural networks (QNNs) to address the inverse kinematics problem in robotic arms, explicitly focusing on the ABB IRB140, an articulated robot arm with six degrees of freedom. The primary objective is to develop a quantum-inspired activation function for multilayer perceptron (MLP) neural networks. The study evaluates their performance in avoiding singularities by comparing artificial neural networks (ANNs) with quantum neural networks (QNNs). The findings demonstrate that QNNs outperform ANNs in terms of mean absolute error (MAE), achieving a 15.60% lower MAE in the model without singularities and a 16.67% reduction in the Jacobian-based MAE in the model designed to avoid singularities. The study demonstrates that QNNs offer superior accuracy in predicting the robot arm's inverse kinematics, achieving a position error of 1.64 mm and an orientation error of 0.00179 radians while effectively avoiding singularities. These outcomes underscore the potential of quantum-inspired neural networks to enhance robotic arm manipulations' precision, efficiency, and performance.