Experimental evaluation of accuracy and efficiency of two control strategies for a novel foot commanded robotic laparoscope holders with surgeons

The implementation of a laparoscope-holding robot in minimally invasive surgery enhances the efficiency and safety of the operation. However, the extra robot control task can increase the cognitive load on surgeons. A suitable interface may simplify the control task and reduce the surgeon load. Foot interfaces are commonly used for commanding laparoscope-holding robots, with two control strategies available: decoupled control permits only one Cartesian axis actuation, known as decoupled commands; hybrid control allows for both decoupled commands and multiple axes actuation, known as coupled commands. This paper aims to determine the optimal control strategy for foot interfaces by investigating two common assumptions in the literature: (1) Decoupled control is believed to result in better predictability of the final laparoscopic view orientation, and (2) Hybrid control has the efficiency advantage in laparoscope control. Our user study with 11 experienced and trainee surgeons shows that decoupled control has better predictability than hybrid control, while both approaches are equally efficient. In addition, using two surgery-like tasks in a simulator, users' choice of decoupled and coupled commands is analysed based on their level of surgical experience and the nature of the movement. Results show that trainee surgeons tend to issue more commands than the more experienced participants. Single decoupled commands were frequently used in small view adjustments, while a mixture of coupled and decoupled commands was preferred in larger view adjustments. A guideline for foot interface control strategy selection is provided.

Enabling personalization of a robotic surgery procedure via a surgery training simulator

Although robotic or robot-assisted surgery has been increasingly used by many surgical disciplines, its application in cranial or skull base surgery is still in its infancy. Master-slave teleoperation setting of these robotic systems enables these surgical procedures to be replicated in a virtual reality environment for surgeon training purposes. A variety of teleoperation modes were previously determined with respect to the motion capability of the surgeon’s hand that wears the ring as the surgeon handles a surgical tool inside the surgical workspace. In this surgery training simulator developed for a robot-assisted endoscopic skull base surgery, a new strategy is developed to identify the preferred motion axes of the surgeon. This simulator is designed specifically for tuning the teleoperation system for each surgeon via the identification. This tuning capability brings flexibility to adjust the system operation with respect to the motion characteristics of the surgeon.

A Novel Training and Collaboration Integrated Framework for Human-Agent Teleoperation

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.