Optimal Motion Scaling for Delayed Telesurgery

TL;DR

This paper investigates how to optimize motion scaling in delayed telesurgery to improve performance, showing that personalized scaling models outperform fixed strategies under various latency conditions.

Contribution

It introduces user-specific models for optimal motion scaling in delayed telesurgery, addressing the variability in operator response to communication delays.

Findings

Performance varies significantly with scaling factors at different delays.

Optimal scaling factors are user-specific and depend on latency levels.

Personalized models improve teleoperation performance under delay.

Abstract

Robotic teleoperation over long communication distances poses challenges due to delays in commands and feedback from network latency. One simple yet effective strategy to reduce errors and increase performance under delay is to downscale the relative motion between the operating surgeon and the robot. The question remains as to what is the optimal scaling factor, and how this value changes depending on the level of latency as well as operator tendencies. We present user studies investigating the relationship between latency, scaling factor, and performance. The results of our studies demonstrate a statistically significant difference in performance between users and across scaling factors for certain levels of delay. These findings indicate that the optimal scaling factor for a given level of delay is specific to each user, motivating the need for personalized models for optimal performance. We present techniques to model the user-specific mapping of latency level to scaling factor for optimal performance, leading to an efficient and effective solution to optimizing performance of robotic teleoperation and specifically telesurgery under large communication delay.