1.1 Workload
As HRC will predominantly take place in industrial work settings, an important psychological state to consider is workload. In fields which have seen increased levels of automation, the impacts on workload have been a topic of research for over 40 years. With no universally accepted and clear definition of workload [
12] this study uses the definition of workload used for the development of the widely used and verified NASA-Task Load IndeX (NASA-TLX): “the cost incurred by a human operator to achieve a particular level of importance” [
13].
Workload has been identified as a key factor to influence human performance, with an optimal level from which deviation can be detrimental [
14‐
17]. Too high and the person risks fatigue and illness, too low and the person may lose focus in the task. However, the specific elements of an automated system that influence workload levels, and the optimal levels required for specific tasks, are important to understand as these are linked to performance and accident prevention [
14]. For example, levels of automation have been shown to influence a person’s workload during driving [
18] and teleoperation tasks [
19,
20] but these findings cannot be directly extrapolated to other tasks. Additionally, relationships between a robot’s speed and a person’s psychological safety have been identified in social robotics research but without exploring workload specifically [
21‐
24]. Thus, although general trends can be identified, it is difficult to generalise or compare research findings outside of the specificities of respective studies, primarily due to differences in research methods and contexts. Early studies in the context of industrial robots for HRC have shown that working within a robot’s operating area increases workload [
25,
26], but few have studied the effects of changing robot attributes on workload. Therefore, to optimise the design of industrial HRC, it is important to identify which specific robot attributes lead to changes in operator workload and how they impact upon effective HRC.
Industrial HRC research using formal Ergonomics / Human Factors methods is uncommon. For example, Tan et al. [
9], studied the skin potential reflex (SPR) of participants collaborating with an industrial robot arm operating at 250, 500, and 1000 mm/s as an objective measure of workload and collected self-reported levels of ‘fear’ and ‘surprise’ as a subjective measure of workload—but provided no clear definitions of these constructs to participants or validation of how they reliably represent workload. They found both higher SPR and ‘fear’ and ‘surprise’ levels at speeds greater than 500 mm/s, suggesting that workload would not only be dependent on the task but also the robot’s speed, but findings may not truly reflect the construct of workload. Many psychological and affective states, such as ‘comfort’, ‘fear’, ‘surprise’, ‘anxiety’, etc., can be considered subjective manifestations of a persons’ psychological safety as we know that extreme levels can cause deleterious effects on wellbeing. Some of these have been found associated with workload, such as the decreasing comfort resulting in an increase in workload [
13,
27]. Therefore, although it is not yet possible to directly link these psychological / affective states and workload, they are likely to be a part of the relationship between robot attributes and psychological safety outcomes.
Butler and Agah [
28] studied the relationship between a mobile robot’s speed and human ‘comfort’ in a study which involved the robot approaching participants directly at a slow (254 mm/s) or a fast (1016 mm/s) speed condition, after which they rated their level of ‘comfort’ using a scale. This study established that participant ‘comfort’ decreased in the fast speed condition, suggesting an increase in workload may have also occurred. Similarly, Kulic and Croft [
29] gathered self-reported measures of ‘anxiety’, ‘calm’, and ‘surprise’ when a robot arm moved using 2 different path planners under 3 different speed settings (0.31, 1.57 and 3.14 rad/s). They found positive correlations between robot speed and participants’ ‘surprise’, and between speed and ‘anxiety’, and a negative correlation between speed and ‘calm’ levels. As with [
9], an increase in ‘surprise’ may have an effect on the person’s workload, suggesting a positive correlation between speed and workload, but the exact nature and strength of relationships between these psychological outcomes and workload is not yet confirmed. A study by Koppenborg et al., [
30] directly measured the influence of the changing speed of an industrial robot arm during HRC on workload in a virtual environment. The results showed a significant increase in workload from the low-speed setting (750 mm/s) to the high-speed setting (1400 mm/s). Although the study is limited by the lack of presence that an actual robot would provide, the results further reinforce link between workload and robot speed during HRC than previous work. It is also noteworthy that the majority of speeds used in these studies exceed the maximum 250 mm/s safety guidance limit for HRC. To the knowledge of the authors of the present paper, there are no studies to date that have assessed the effects of an industrial robot arm’s speed during HRC, within the speed envelope defined by ISO/TS 15,066:2016, which this paper seeks to address.
Compared to speed, the effect of the robot’s proximity in HRI has seen a greater amount of research. This is due to the prevalence of social robots that are, by definition, designed for close proximity. The majority of the studies reviewed for this paper developed their models around the Proxemics model by Hall [
31], which defines the socially acceptable distances around a person for Human–Human Interaction. Kim and Mutlu [
32] assessed the participant’s ‘comfort’, ‘pleasure’, and ‘likeability’ via questionnaire during multiple tasks when a mobile robot was at 0.46 and 1.2 m. They found that when co-operating with the robot, participants reported lower ‘comfort’ and ‘likeability’ when the robot was closer. This decrease in comfort with reduced separation was also reported with robot arms [
33,
34], as well as an increase in workload [
9].
A means of overcoming subjective uncertainty is to objectively measure participant’s changes in position. This is demonstrated in Stark et al.[
10], where the reactions of participants to a robot arm’s proximity during HRC were monitored. As the robot arm entered the personal zone of the person (0.45–1.2 m), they physically moved away suggesting they were not comfortable with that proximity. Walters et al., [
35] attempted to identify some of the factors that influence the accepted proximity, focusing on the participant’s level of control and their personality. When the robot is static and the participant is approaching, the majority (60%) would move to within the intimate zone (0.15–0.45 m) compared to only 38% of participants accepting of the robot approaching up to 0.5 m. This indicates that when participants are in control of the proximity, they feel more comfortable however, this was not supported in other studies [
36]. The robot’s size and “gaze” direction have also been shown to influence the allowable distance [
28,
37,
38]. From these studies, it appears that the proximity of the robot has an impact on person’s comfort during HRI and, therefore, potentially on their workload. However, the studies into the effects of an industrial robot arm’s proximity during HRC whilst adhering to the guidance set in ISO/TS 15,066:2016 are few and limited. With physical barriers being removed and the increased sophistication of collision avoidance algorithms for application in HRC, it is imperative that such attributes of the robot (and the psychological factors they impact upon) be further understood.
Although these comparisons are not direct between robot attributes and workload, they can be used to inform. They demonstrate that a person’s psychological safety can be influenced by changing robot speed and proximity. A major limitation in HRI and HRC studies is the lack of valid and reliable measures, and consistent experimental methods. Furthermore, HRC studies are rarely conducted under the industrial settings which they are intended for. Whilst some have used established reliable tools for subjective measures, such as the NASA TLX, others have not used rigorously tested scales. Alternatively, objective measures can be task specific, where the choice of which element of the activity is contributing to the workload of the task allows for subjectivity [
17,
39,
40]. Indeed, a great deal of research has not accounted for the possibility that results may reflect task characteristics and complexity rather than effects of robot attributes. This study aims to rectify this oversight, by having the impact of speed and proximity on a person’s psychological responses as the primary research focus. Furthermore, the dependent variables will use clear definitions for the psychological concepts that are adopted from previously used surveys where the focus has been on the increasing use of automation/robotics in an industrial setting.
A key challenge when measuring workload is that it can vary based on the individual, the task at hand, and the environment in which the task is being conducted [
40]. In order to understand workload, the different aspects which contribute to it should be considered. Cain [
12] postulated that these aspects can be divided into 3 categories: the amount of work and number of things to do, the subjective psychological experiences of the human, and the time required to complete the task. These criteria fall in line with the variables measured by the most commonly used subjective workload scales. Of these, two that are frequently compared are the NASA-Task Load IndeX (NASA-TLX) [
13] and the Subjective Workload Assessment Technique (SWAT) [
41]. In direct comparisons, there has been little evidence that either scale shows a greater sensitivity to changes in users’ workload, however, both showed higher sensitivity in comparison with other scales [
12,
42,
43]. A key difference is user acceptability, which is higher in NASA-TLX and attributed to the faster completion of the scale, therefore, this was chosen as the scale for this study. The NASA-TLX [
44] measures 6 subscales: Mental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Performance, Effort, and Frustration. The average of the scores obtained on each of those subscales provides an overall workload score [
44,
45]. For this study, the Raw TLX (RTLX) was used, in which the weighting of the subscales is omitted. This was chosen as the test is simpler to apply, and comparative studies show no consensus of an effect on the sensitivity of the scale [
45].
1.2 Trust
Another key psychological state which has been highly related to performance with automation is ‘trust’ [
46]. Like workload, trust is also problematic to define and there is no universal definition, so it is highly important to consider the context and system attributes for which it is being investigated. For this study, the definition used was “the attitude that an agent will help an individual’s goals in a situation characterised by uncertainty and vulnerability” [
46], where the agent is the robot. As the level of trust the operator has in the system increases, the efficiency of the system increases making it a desirable concept to better understand in industry [
47‐
49]. There is a limit to the system efficiency increasing, as increasing trust may result in passivity or complacency in the task, leading to difficulty in detecting changes/faults in the system. Too little trust, however, and the person will be more likely to interfere with the process [
47,
50]. As with workload, deviations from the optimal level can have detrimental effects.
Hancock et al. [
51] ran a meta-analysis and identified 3 main categories of factors which could affect a person’s trust during HRI: the human, the robot, and the environment. Amongst the factors falling under the Robot category, the robot’s performance had the largest impact. The robot’s attributes (proximity, shape, anthropomorphism, personality, and type) were also shown to be significant factors. MacArthur et al. [
34] manipulated the speed and proximity of a mobile robot approaching participants, after which they would then complete Human Robot Trust Scale and Negative Attitudes Towards Robots Scale surveys. They found that participants’ trust was reduced by decreasing proximity and increasing speed of the robot.
Whilst the trust scales mentioned above are sufficient for quantifying general trust in HRI, as outlined it is important to consider the impact of specific robot attributes where possible. A more focused measurement tool for industrial HRC was developed by Charalambous et al. [
52] to account for the impact of robot attributes on operator trust in collaborative tasks. As this scale was developed with industrial robot arms and end-effectors it was deemed the most relevant for this study.
As outlined, direct analysis of the relationship between robot speed and proximity with workload and trust has been highly limited and previous findings are largely incomparable. This highlights a gap in knowledge, which the present study aims to begin filling. It measures the impact of an industrial robot arm’s speed and proximity setting on a person’s workload and trust during a HRC task. Workload and trust have been identified as key metrics in task performance, whilst speed and proximity have been identified as two robot attributes which impact on a person’s psychology during HRI. Based on our current knowledge, the aim of the present study is to determine if there is a link between the speed and proximity setting of an industrial robot during HRC with a person’s workload and trust. This will be tested by the following hypotheses: