Purpose Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7‐degree of freedom (DoF) robot in a surgical operation. Methods Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network‐based manipulability optimization control was implemented on a 7‐DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation. Results By comparison, the dynamic neural network‐based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non‐optimized method). Conclusions The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obot-assisted minimally invasive surgery (RAMIS) has produced noticeable benefits for patients in the recent years [1], making it a favorable approach for a wide range of surgeries. The benefits of improving the dexterity of patient side manipulators to enable surgeons to perform more complex tasks are offset by the increased complexity of teleoperation and cognitive and physical effort on the operator side typically. A right balance between higher dexterity and intuitive control in teleoperation is yet to be defined. In this study, a dexterous, anthropomorphic primary master controller was deployed to assess and compare the efficiency of simulated anthropomorphic surgical instruments in an immersive surgical concept. Virtual surgical training tasks were built using a gaming software engine (Unity) and performed using simulated surgical tools with extended degrees of freedom (DoF) in the surgical shaft and gripper and compared with the standard da Vinci (DV) grasper. The motion of the tools were controlled using commercial inertial measurement unit (IMU) sensor-based devices attached to the user's arms and hands. This article summarizes results obtained from three studies with similar features but different levels of complexity, taken with both lay users with no experience in surgery or teleoperation and surgeons experienced in RAMIS. The results showed that more than 70% of users achieved better results using articulated tools but required more physical and mental effort for teleoperation.