A paced shared-control teleoperated architecture for supervised automation of multilateral surgical tasks

Abstract

Automation of repetitive tasks can improve laparoscopic surgical procedures by unloading surgeons and reducing duration, trauma, and expense. However, surgical procedures involve delicate manipulation of deformable tissues in a very dynamic environment, suggesting that automated execution of surgical tasks should be carried out under the supervision of the surgeon. We propose a teleoperated architecture that allows a surgeon to employ and supervise agents that can autonomously perform or assist with surgical tasks. The architecture is independent of the automation method. It includes a dominance factor that allows the surgeon to take control over the slave robot at any time, and an aggressiveness factor that sets the performance pace of the autonomous agent. We tested the architecture during execution of a multilateral tension-and-cut task, where a human operator and an autonomous agent are responsible for tensioning or cutting of a tissue. The architecture allowed for supervised and paced automation of the task. We found that collaboration of the human operator and autonomous agent can lead to shorter completion time compared to performance of only a human.

Review of Advanced Medical Telerobots

“…According to the topological structure of multilateral systems, they are categorized as Single-leader/Multi-follower (SL/MF), see for example [4,[225][226][227][228][229], Multi-leader/Singlefollower (ML/SF), see for example [230][231][232], and Multi-leader/Multi-follower (ML/MF), see for example [4,[233][234][235][236][237][238][239][240][241][242][243][244][245][246]. These systems allow for the realization of a variety of new tasks that require multiport communication between distributed modules such as collaboration and interactions between multiple network terminals, multiple robots, and multiple operators enhancing efficacy, precision/accuracy, dexterity/manipulability, loading capacity (through distributed power) and handling capability through joint task conduction and shared autonomy, see for example [218,227,230,[247][248][249][250][251][252].…”

“…This configuration consists of one operator/leader robot, one autonomous agent (please see Section 3 for more details), and one follower robot [206,216,251,[299][300][301][302][303]. The configuration has also been seen as a variation of SL/SF architecture, which is augmented with artificial intelligence.…”

Evaluating Human Behavior in Manual and Shared Control via Inverse Optimization

“…The cost function is given by (6) and is parameterized by a vector = [︀ 11 22 ]︀ ∈ R 3 . We chose to neglect the parameters 12 and 21 by setting them to zero since these represent angle-velocity mixed terms in the cost function which are neither meaningful nor interpretable.…”

“…The dynamic optimization problem is solved for a prediction horizon of the length = 50 with a sample rate of = 0.02 . Note that the cost function was given the same structure as the cost function of the human (6). We chose the parameter values = [︀ 5 0 1 ]︀ ⊤ which lead to a slight assistance level.…”

“…It has also been succesfully applied in several application fields. We mention here exemplarily robot-aided rehabilitation [5], surgical systems [6], advanced driver assistance systems [7] and teleoperation of unmanned aerial vehicles [8].…”