Dr Ren Invited to Talk at SUSTC (South University of Science and Technology of China)ย about recent developments in flexible surgical robotics.
Month: October 2016
Towards hybrid control of a flexible curvilinear surgical robot with visual/haptic guidance
Abstract
Comprised of multiple telescoptic precurved tubesthat can independently rotate and translate, concentric tuberobots (CTRs) are favorable in minimally invasive surgeriesthanks to their small size and considerable dexterity along withcurvilinear accessibility. However, there is a lack of investigationon improvement of the surgeonsโ perception which in turn canbe used to guide the telemanipulation. In this work, we proposedan eye-in-hand con๏ฌguration for the concentric tube robot byadding an endoscope to the tip of the inner tube, which providesdirect and intuitive visual sensing ability for the operator. Basedon this visual feedback, we further developed two frameworksfor the hybrid control of CTR, namely Teleoperation BeforeVisual Servoing (TBVS) and Teleoperation During Visual Ser-voing (TDVS). The structures of these two frameworks wereelaborated with key algorithms derived. The effectiveness ofthe proposed methods were demonstrated through a series ofexperiments both in free space and in a con๏ฌned environment(inside a skull model). The results manifested that the visualguidance had the potential of assisting the operator to controlthe CTR more ef๏ฌciently.
More information
Data-driven Learning Intelligent Control for Flexible Surgical Manipulators
Abstract
Automate Surgical Tasks for A Flexible Serpentine Manipulator via Learning Actuation Space Trajectory from Demonstration
Background: Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. Tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in MIS for its enhanced maneuverability in torturous environment. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy.
Methods: To account for the system nonlinearities, we applied data driven approach to encode the system inverse kinematics. Three regression methods: Extreme Learning Machine (ELM), Gaussian Mixture Regression (GMR) and K-Nearest Neighbors Regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position state to the control inputs.
Results: The performance of the three algorithms were evaluated both in simulation and physical trajectory tracking experiments. KNNR performs the best in the tracking experiments with the lowest RMSE of 2.1275mm.
Conclusions: The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the challenging tendon driven flexible manipulator.
Keywords: Tendon-driven serpentine manipulator; surgical robotics; Inverse kinematics; Heuristic Methods
Demo video at:
Publications
- W. Xu; J. Chen; H. Y. Lau & H. Ren Data-driven Methods towards Learning the Highly Nonlinear Inverse Kinematics of Tendon-driven Surgical Manipulators International Journal of Medical Robotics and Computer Assisted Surgery , 2016, 1-13
- W. Xu; J. Chen; H. Y. Lau & H. Ren Automate Surgical Tasks for A Flexible Serpentine Manipulator via Learning Actuation Space Trajectory from Demonstration ICRA2016, IEEE International Conference on Robotics and Automation, 2016, –
Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey
Abstract
Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments, and enable to perform complex and delicate operations through small incisions in minimally invasive surgery. These advantages enable their broad applications with minimal trauma, and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities. However, their inherent deformable designs make it difficult to realize three-dimensional (3D) intraoperative real-time shape sensing to accurately model their shape. Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, humanโrobot interaction and surgical manipulation safety concerns in minimally invasive surgery. Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads. This survey investigates the recent advances in alternative emerging techniques for 3D shape sensing in this field, and focuses on the following categories: fiber optic sensors based, electromagnetic tracking based and intraoperative imaging modalities based shape reconstruction methods. The limitations of existing technologies and prospects of new technologies are also discussed.
