Shared Control for Bimanual Telesurgery with Optimized Robotic Partner

TL;DR

This paper presents a shared control system with a robotic partner for bimanual telesurgery, reducing surgeon workload and improving efficiency by using fuzzy-model-based learning without relying on force sensors.

Contribution

Introduces a novel shared control framework with a robotic partner using fuzzy-model-based learning, enhancing bimanual telesurgery without needing environmental force sensors.

Findings

Reduced surgeon workload by 51% in experiments.

Effective assistance in peg-transfer tasks.

Operates without reliance on force sensors.

Abstract

Traditional telesurgery relies on the surgeon's full control of the robot on the patient's side, which tends to increase surgeon fatigue and may reduce the efficiency of the operation. This paper introduces a Robotic Partner (RP) to facilitate intuitive bimanual telesurgery, aiming at reducing the surgeon workload and enhancing surgeon-assisted capability. An interval type-2 polynomial fuzzy-model-based learning algorithm is employed to extract expert domain knowledge from surgeons and reflect environmental interaction information. Based on this, a bimanual shared control is developed to interact with the other robot teleoperated by the surgeon, understanding their control and providing assistance. As prior information of the environment model is not required, it reduces reliance on force sensors in control design. Experimental results on the DaVinci Surgical System show that the RP could assist peg-transfer tasks and reduce the surgeon's workload by 51\% in force-sensor-free scenarios.