Forward geometric model prediction of a 6-RSU parallel manipulator using a modified NARX Bayesian neural network.

The precision and safety of robotic applications rely on accurate robot models. Bayesian Neural Networks (BNNs) offer the capability to acquire intricate models and provide insights into inherent uncertainties. While recent studies have successfully employed machine learning to predict the Forward Geometric Model (FGM) of a 6-DOF (degrees of freedom) parallel manipulator, traditional methods lack predictive uncertainty estimation.