Surgeon Training in Telerobotic Surgery via a Hardware-in-the-Loop Simulator

Abstract

This work presents a software and hardware framework for a telerobotic surgery safety and motor skill training simulator. The aims are at providing trainees a comprehensive simulator for acquiring essential skills to perform telerobotic surgery. Existing commercial robotic surgery simulators lack features for safety training and optimal motion planning, which are critical factors in ensuring patient safety and efficiency in operation. In this work, we propose a hardware-in-the-loop simulator directly introducing these two features. The proposed simulator is built upon the Raven-II™ open source surgical robot, integrated with a physics engine and a safety hazard injection engine. Also, a Fast Marching Tree-based motion planning algorithm is used to help trainee learn the optimal instrument motion patterns. The main contributions of this work are (1) reproducing safety hazards events, related to da Vinci™ system, reported to the FDA MAUDE database, with a novel haptic feedback strategy to provide feedback to the operator when the underlying dynamics differ from the real robot's states so that the operator will be aware and can mitigate the negative impact of the safety-critical events, and (2) using motion planner to generate semioptimal path in an interactive robotic surgery training environment.

Figure 1

From left to right: LapSim haptic system, dV-trainer, and RoSS.

Figure 2

Hardware-in-the-loop surgical simulator architecture.

Figure 3

Simulated surgeon console and FRS training model—yellow sphere on each arm indicates the fixed remote motion center, and the cavity of the dome represents a human abdomen area; more details of the model can be found in Figure 7 and Figure 8.

Figure 4

Comparison of the model and robot running data (up to 5 joints) and end-effector position error of (2.43 ± 1.72).

Figure 5

Robot and model trajectories during fault injection are enabled (with teleoperation scaling factor of 0.1).

Figure 6

Haptic force feedback on the Omni device during fault injection.

Figure 7

Three end-effector trajectories representing user's movement and two different optimization criteria.

Figure 8

Three end-effector trajectories representing returned by FMT^∗ using different number of samples.

Algorithm 1

FMT^∗.