Predictive Communication Quality Control in Haptic Teleoperation With Time Delay and Packet Loss

Abstract

Teleoperation in extreme environments may suffer from communication delay and packet loss during the transmission of command signals and sensory feedback. The present study investigates whether online control of communication time delay by using Quality of Service (QoS) techniques can improve operator task performance in a virtual teleoperated collision avoidance task. We first introduce the framework of predictive communication quality control based on a dynamic performance model of a human handling the teleoperation system. We then apply the framework to a virtual collision avoidance scenario, and evaluate it with two behavioral studies. Study 1 identifies that prolonging time delay significantly increases the frequency of collisions and completion time. We develop a model for predicting the probability of the operator causing collisions with the wall and fit its parameters with the experimental data. In Study 2 we compare the completion time and the number of collisions with and without the predictive QoS control. We find the predictive QoS control to reduce the number of collisions, but not affect task completion time. The prediction model and empirical validation provide a successful proof-of-concept for a human-centered system design, in which the dynamic model of the operator is the center of the control architecture.

URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems

“…For individual visual perceptions, it has been reported by another experiment [4] that the PER is proportional to its sole JND γ 2 due to the resulting visual frame loss. Similarly, for individual haptic perceptions, it has been observed in [12] that the PER is proportional to the elapsed time to complete a given experimental task, which increases with the corresponding JND γ 1 due to the coarse perceptions. Based on such experimental evidence, we can write that γ i = (1−η i ) 2 , where (1 − η i ) represents the PER on Link i .…”

“…Following [24], our SIC do not allow to decode the Link 2 signal after the decoding failure of the Link 1 signal. With a different SIC architecture that allows such a decoding attempt, (12) is regarded as the lower bound, as done in [17]. For the given target decoding success probability η 1 of Link 1 , the average rate R 1,N (η 1 ) of Link 1 is given as R1,N(η1) = η1 • sup{log(1 + t1,N) : p1,N(t1,N) ≥ η1}…”

“…Furthermore, in order to render a smooth multimodal experience, the PERs associated with the visuo-haptic VR perceptions should guarantee a target perceptual resolution. To be precise, the perceptual resolution is commonly measured by using the just-noticeable difference (JND) in psychophysics, a field of study that focuses on the quantitative relation between physical stimulus and perception [4], [11], [12]. Following Weber's law, JND describes the minimum detectable change amount of perceptual inputs, e.g., 3 mm for the object size measurement using visuo-haptic perceptions [11].…”

Robotic remote control based on human motion via virtual collaboration system: A survey

“…Hua et al (2015) estimated the lost velocity command of robot links using a sliding-mode velocity observer, and designed the saturated proportion and saturated damping controller to compensate for the uncertainty of the model based on the estimated velocity input information. Rank et al (2016) proposed a way to regenerate position input data by using the probability prediction model data when data is lost or time delay occurs. In spite of the efforts stated above, in systems where the distance is very far or it is hard to ensure a stable network condition (e.g., where missing data can occur frequently), we need to figure out and apply a method to reduce the amount of data for the stable and sufficient network traffic condition.…”

“…Hua et al (2015) estimated the lost velocity command of robot links using a sliding-mode velocity observer, and designed the saturated proportion and saturated damping controller to compensate for the uncertainty of the model based on the estimated velocity input information. Rank et al (2016) proposed a way to regenerate position input data by using the probability prediction model data when data is lost or time delay occurs. To secure the stability, the Lyapunov-Krasovskii functional method is applied for the filtering of the error (e.g., Zhai et al, 2017;Hua et al, 2017).…”