Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot with Uncertainty Minimization
In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints.