Semi-Autonomous Robotic Manipulator for Minimally Invasive Aortic Valve Replacement

Aortic valve surgery is the preferred procedure for replacing a damaged valve with an artificial one. The ValveTech robotic platform comprises a flexible articulated manipulator and surgical interface supporting the effective delivery of an artificial valve by teleoperation and endoscopic vision. This article presents our recent work on force-perceptive, safe, semiautonomous navi- gation of the ValveTech platform prior to valve implantation. First, we present a force observer that transfers forces from the manip- ulator body and tip to a haptic interface. Second, we demonstrate how hybrid forward/inverse mechanics, together with endoscopic visual servoing, lead to autonomous valve positioning. Benchtop experiments and an artificial phantom quantify the performance of the developed robot controller and navigator. Valves can be au- tonomously delivered with a 2.0±0.5 mm position error and a min- imal misalignment of 3.4±0.9°. The hybrid force/shape observer (FSO) algorithm was able to predict distributed external forces on the articulated manipulator body with an average error of 0.09 N. FSO can also estimate loads on the tip with an average accuracy of 3.3%. The presented system can lead to better patient care, delivery outcome, and surgeon comfort during aortic valve surgery, without requiring sensorization of the robot tip, and therefore obviating miniaturization constraints. Manuscript received 26 May 2023; accepted 28 August 2023. Date of pub- lication 9 October 2023; date of current version 6 December 2023. This paper was recommended for publication by Associate Editor L. Zhao and Editor S. Behnke upon evaluation of the reviewers’ comments. This work was supported in part by Regione Toscana, Bando Fas Salute 2014, through the ValveTech project, in part by an ERC Starting Grant (714562), in part by core funding from the Wellcome/EPSRC Centre for Medical Engineering, Welcome Trust [WT203148/Z/16/Z] in part by an NIHR Cardiovascular MIC Grant in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 101017140, the ARTERY project in part by the European Union by the Next Generation EU Project ECS00000017 ‘Ecosistema dell’Innovazione’ Tuscany Health Ecosystem (THE, PNRR, Spoke 9: Robotics and Automation for Health) and in part by the Italian Ministry of Education and Research (MUR) in the framework of the FoReLab and CrossLab projects (Departments of Excellence). (Corresponding author: Izadyar Tamadon.) Izadyar Tamadon is with the Faculty of Engineering Technology, Uni- versity of Twente, 7522 NB Enschede, The Netherlands, and also with the BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy (e-mail: i.tamadon@utwente.nl). S. M. Hadi Sadati and Christos Bergeles are with the Robotics and Vi- sion Department in Medicine Lab, School of Biomedical Engineering & Imaging Sciences, King’s College London, SE17EU London, U.K. (e-mail: m.hadi.sadati@gmail.com; christos.bergeles@kcl.ac.uk). Virginia Mamone and Vincenzo Ferrari are with the Department of Computer Science and the EndoCAS Center for Computer-Assisted Surgery, University of Pisa, 56124 Pisa, Italy (e-mail: virginia.mamone@endocas.unipi.it; vin- cenzo.ferrari@endocas.org). Arianna Menciassi is with the BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy (e-mail: arianna@sssup.it). This article has supplementary material provided by the au- thors and color versions of one or more figures available at https://doi.org/10.1109/TRO.2023.3315966. Digital Object Identifier 10.1109/TRO.2023.3315966