Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review

Abstract

The degree of successful human-robot collaboration is dependent on the joint consideration of robot factors (RF) and human factors (HF). Depending on the state of the operator, a change in a robot factor, such as the behavior or level of autonomy, can be perceived differently and affect how the operator chooses to interact with and utilize the robot. This interaction can affect system performance and safety in dynamic ways. The theory of human factors in human-automation interaction has long been studied; however, the formal investigation of these HFs in shared space human-robot collaboration (HRC) and the potential interactive effects between covariate HFs (HF-HF) and HF-RF in shared space collaborative robotics requires additional investigation. Furthermore, methodological applications to measure or manipulate these factors can provide insights into contextual effects and potential for improved measurement techniques. As such, a systematic literature review was performed to evaluate the most frequently addressed operator HF states in shared space HRC, the methods used to quantify these states, and the implications of the states on HRC. The three most frequently measured states are: trust, cognitive workload, and anxiety, with subjective questionnaires universally the most common method to quantify operator states, excluding fatigue where electromyography is more common. Furthermore, the majority of included studies evaluate the effect of manipulating RFs on HFs, but few explain the effect of the HFs on system attributes or performance. For those that provided this information, HFs have been shown to impact system efficiency and response time, collaborative performance and quality of work, and operator utilization strategy.

A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning

“…The degree of success of human-robot collaboration depends on the joint consideration of the robot factor (RF) and the human factor (HF) [ 34 ]. The experiment mentioned above is an evaluation of the robot factor.…”

“…erefore, this section uses four human factors indicators to evaluate the MRLC qualitatively. e four indicators are trust, anxiety, safety perception, and fatigue [34]. e score interval of each indicator is [1][2][3][4][5][6][7][8][9][10], and the lower score means the worse performance under the indicator, 0 means very poor, and 10 means very good.…”

Capturing Dynamic Trust Metrics during Shared Space Human Robot Collaboration: An eye-tracking approach

“…Gender differences has also been found in a recent humanrobot trust study, which showed females tend to trust the robot more after observing human-robot interaction scenarios (Gallimore et al, 2019). However, a systematic review on existing human-robot studies showed there has been a common lack of gender considerations in general (Hopko et al, 2022).…”

An Open Source Multi-Modal Data-Acquisition Platform for Experimental Investigation of Blended Control of Scale Vehicles

“…Autonomous vehicles are becoming more and more ubiquitous [1]. Whilst these vehicles should do as much of the journey in autonomous mode as possible, the current state of the art depends heavily on a more blended control system, often requiring an operator to take over control [2] to maneuver the vehicle around unexpected obstacles or events, etc.…”