Optimization of Surgical Robotic Configuration Considering Variations in Suturing Accuracy and Muscle Burden Through Virtual Simulation

Abstract

Surgical robots are used in minimally invasive surgery. The operator performs compensatory movements because the robot differs structurally from the human hand and arm. This paper investigates surgical robots’ optimal configuration by considering variations in the operator’s suturing accuracy and muscle burden across trials and between individual operators. The design factors were the motion scale, viewing angle, pivot point, tip length, and needle gripping angle. We developed a virtual surgical simulator implementing haptic feedback. As 20 participants manipulated the simulator in three trials for 27 surgical robot configurations, we evaluated each participant’s suturing error and joint burden. Considering the variation among trial and individual differences, we investigated the best-fitted probability distribution model, calculated the expectation and deviation in each index using the reliability design method, and constructed the response surface for the relationship between factors. Furthermore, we optimized the surgical robot using the satisficing trade-off method. Finally, when comparing theoretical and experimental values for the best solution, relative errors in suturing error and muscle burden were less than 7.14% and 15.1%, respectively. Moreover, the optimized surgical robot improved suturing error and joint energy by 18% relative to the average values across the 27 configurations.