Cursi, Francesco;
Modugno, Valerio;
Lanari, Leonardo;
Oriolo, Giuseppe;
Kormushev, Petar;
(2021)
Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization.
IEEE Robotics and Automation Letters
, 6
(2)
pp. 2642-2649.
10.1109/LRA.2021.3062339.
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Abstract
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. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.
| Type: | Article |
|---|---|
| Title: | Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LRA.2021.3062339 |
| Publisher version: | https://doi.org/10.1109/lra.2021.3062339 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | INVERSE KINEMATICS, MANIPULATORS, Medical robots and systems, model learning for control, robot safety, Robotics, Science & Technology, Technology, tendon/wire mechanism |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215883 |
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