The effect of human autonomy and robot work pace on perceived workload in human-robot collaborative assembly work

Collaborative robots (in short: cobots) have the potential to assist workers with physically or cognitive demanding tasks. However, it is crucial to recognize that such assistance can have both positive and negative effects on job quality. A key aspect of human-robot collaboration is the interdependence between human and robotic tasks. This interdependence influences the autonomy of the operator and can impact the work pace, potentially leading to a situation where the human's work pace becomes reliant on that of the robot. Given that autonomy and work pace are essential determinants of job quality, design decisions concerning these factors can greatly influence the overall success of a robot implementation. The impact of autonomy and work pace was systematically examined through an experimental study conducted in an industrial assembly task. 20 participants engaged in collaborative work with a robot under three conditions: human lead (HL), fast-paced robot lead (FRL), and slow-paced robot lead (SRL). Perceived workload was used as a proxy for job quality. To assess the perceived workload associated with each condition was assessed with the NASA Task Load Index (TLX). Specifically, the study aimed to evaluate the role of human autonomy by comparing the perceived workload between HL and FRL conditions, as well as the influence of robot pace by comparing SRL and FRL conditions. The findings revealed a significant correlation between a higher level of human autonomy and a lower perceived workload. Furthermore, a decrease in robot pace was observed to result in a reduction of two specific factors measuring perceived workload, namely cognitive and temporal demand. These results suggest that interventions aimed at increasing human autonomy and appropriately adjusting the robot's work pace can serve as effective measures for optimizing the perceived workload in collaborative scenarios.

The Relationships of Human-Cobot Interaction Fluency with Job Performance and Job Satisfaction among Cobot Operators-The Moderating Role of Workload

Modern factories are subject to rapid technological changes, including the advancement of robotics. A key manufacturing solution in the fourth industrial revolution is the introduction of collaborative robots (cobots), which cooperate directly with human operators while executing shared tasks. Although collaborative robotics has tangible benefits, cobots pose several challenges to human-robot interaction. Proximity, unpredictable robot behavior, and switching the operator's role from a co-operant to a supervisor can negatively affect the operator's cognitive, emotional, and behavioral responses, resulting in their lower well-being and decreased job performance. Therefore, proper actions are necessary to improve the interaction between the robot and its human counterpart. Specifically, exploring the concept of human-robot interaction (HRI) fluency shows promising perspectives. However, research on conditions affecting the relationships between HRI fluency and its outcomes is still in its infancy. Therefore, the aim of this cross-sectional survey study was twofold. First, the relationships of HRI fluency with job performance (i.e., task performance, organizational citizenship behavior, and creative performance) and job satisfaction were investigated. Second, the moderating role of the quantitative workload in these associations was verified. The analyses carried out on data from 200 male and female cobot operators working on the shop floor showed positive relationships between HRI fluency, job performance, and job satisfaction. Moreover, the study confirmed the moderating role of the quantitative workload in these relations. The results showed that the higher the workload, the lower the relationships between HRI fluency and its outcomes. The study findings are discussed within the theoretical framework of the Job Demands-Control-Support model.

My Caregiver the Cobot: Comparing Visualization Techniques to Effectively Communicate Cobot Perception to People with Physical Impairments

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they “see” the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot’s surrounding have been identified by their sensors. We compared the well-established visualizations Wedge and Halo against our proposed visualization Line in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that Line, a lower complexity visualization, results in significantly faster reaction times compared to Halo, and lower task load compared to both Wedge and Halo. Overall, users prefer Line as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and Line presents an easy-to-understand alternative.

Evaluation of Collaborative Robot Sustainable Integration in Manufacturing Assembly by Using Process Time Savings

Collaborative robots are enablers of flexibility in the current dynamic and uncertain manufacturing environment. Decision making on its implementation requires technical feasibility, involving productivity and workforce implications that should be faced in an integrated perspective in processes where many components of different materials are assembled in products of increasing diversity and complexity. This study introduces two new parametric models for collaborative robotics, formulated in order to evaluate the differential cost of assembly (economic dimension) and the differential income from taxes that supports short-term workforce displacement (social dimension) in cobot implementation. Updated techno-economical parameters are selected for assessing feasibility ranges of application in different production scenarios. Next, the influence curves of productivity gain for a feasible implementation of cobot establish thresholds for decision making under both criteria. The results show the need for productivity gains that are significantly lower in high-wage scenarios than in low-wage scenarios; however, in a joint approach, breakeven productivity gain is always higher for the social dimension threshold than for the economic requirement of cost-effective manufacturing, with a higher gap in low-wage cases. The detailed analysis of a real case study of cobot implementation for assembly demonstrates the practical application of models and potential for future research.

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

Analysis of the Impact of Human-Cobot Collaborative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements

Implementing Industry 4.0 and interconnected robotization in industrial enterprises drifts towards occupational changes. Nowadays, the task is to create cooperation and collaboration between a robot and a human in a common robotized workplace so that it is safe and effective. The type of robot, the robotic device that works in collaboration with a human operator, is called a cobot. In the case of a closer interaction of the robot or cobot with humans, it is necessary to consider where it is possible to replace human work entirely or where it is possible to merely supplement it. The most socially acceptable option is the implementation of robots only for the performance of supplementary tasks, since the traditional work positions of people in manufacturing processes would remain largely preserved. On the other hand, workplace robotization is particularly suitable for work environments with hazardous chemical substances that are carcinogenic and toxic to humans. Similarly, robotization helps to improve workplace ergonomics and also to avoid, for humans, very laborious and often repetitive work. The SWOT analysis (analysis of Strengths, Weaknesses, Opportunities, and Threats) was used as a relevant tool to assess various aspects of the impact of increasing robotization on working positions in industrial enterprises. SWOT analysis is an indicative assessment of the suitability of implementation of robots in a given workplace, which helps to create an optimal solution and indicate new areas of needed analysis and research directions.