Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery

Selvaggio, Mario and Ghalamzan Esfahani, Amir and Moccia, Rocco and Ficuciello, Fanny and Siciliano, Bruno (2019) Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery. IEEE/RSJ International Conference Intelligent Robotic System . ISSN UNSPECIFIED

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Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery
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Abstract

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re- grasp the needle using dual-arm movements. This yields an increased operator’s cognitive load, time-to-completion, fatigue and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability over the suturing trajectory. We evaluate the cost and its gradient on the needle grasping manifold that allows us to obtain the optimal grasping pose for joint-limit and singularity free movements of the needle during suturing. Then, we compute force cues that are applied to the Master Tool Manipulator (MTM) of the da Vinci to guide the operator towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration allowing the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of our proposed haptic- guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping.

Keywords:Haptic guided, shared control, Minimally invasive surgery
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:36571
Deposited On:02 Aug 2019 12:54

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