Auto-Tuned Motion Scaling in Teleoperation Based on Human Reaction Model Identification

Motion scaling is an essential technique in robotic surgical systems adopting the leader-follower configuration. By properly reducing the scaling factor, the surgeon can magnify the motion resolution that a human cannot achieve. However, manually tuning the scaling factor distracts the surgeon during the operation. Hence, adaptive methods were introduced to adjust the scaling factor autonomously, despite increasing the system's complexity and leaving more parameters to be designed. We propose a novel framework enabling a systematic design of the motion scaling auto-tuner to address this problem. First, the leader-follower configurated teleoperation is modeled as a human-in-loop control system. Then, we attain the motion scaling auto-tuner by model-matching based filter design. The proposed method is also integrated with virtual fixture techniques, which improve the safety of surgical tasks via haptic feedback. Finally, experiments are conducted for performance evaluation and comparison. The task completion time and other evaluation metrics are effectively improved with the systematic design framework.