Implicit Active Constraints for robot-assisted arthroscopy

Abstract

This paper presents an Implicit Active Constraints control framework for robot-assisted minimally invasive surgery. It extends on current frameworks by prescribing the external constraints implicitly from the operator motion, forgoing the need for pre-operative imaging; the constraints are defined in situ so as to avoid the use of invasive fiducial markers.A hands-on cooperatively-controlled robotic platform, comprising of a surgical instrument and a compliant manipulator, has been designed for an arthroscopic procedure. The surgical platform is capable of constraining the pose of the instrument so as to ensure it passes through the incision point and does not cause trauma to the surrounding tissue.A flexible arthroscopic instrument is designed and its use is investigated to enlarge reachable and dexterous workspace, increasing the accessibility to the target anatomy.The behaviour of the flexible instrument is analysed. A detailed performance analysis is conducted on a group of subjects for validating the control framework, simulating a minimally invasive arthroscopic procedure. Results demonstrate a statistically significant enhancement in the control ergonomics as well as the accuracy and safety of the procedure.

Selective Effects of Manual Massage and Foam Rolling on Perceived Recovery and Performance: Current Knowledge and Future Directions Toward Robotic Massages

“…Advances in robotics gradually spread within the field of medicine ( Petrescu et al, 2016 ). Like the da Vinci surgical robot and the ROSA TM spine robot, it is clearly established that robots can assist surgeons with precision during minimally invasive procedures such as arthroscopy or laparoscopic splenectomy ( Chapman et al, 2002 ; Lopez et al, 2013 ; Lefranc and Peltier, 2016 ). The increasing amount of robotic intervention should be considered within a broader framework.…”

Implicit active constraints for safe and effective guidance of unstable concentric tube robots

“…While in conventional AC a human explicitely defines the constraint, e.g. by segmenting pre-operative images and considering the resulting polygon meshes as obstacles, or by learning from demonstrations of common trajectories, the benefit of IACs is that they algorithmically create task-relevant constraints based on limited user input by leveraging information about the defined surgical tasks [21].…”