Collaborative robots are increasingly common in modern production systems, since they allow to merge the productivity of automated systems with the flexibility and dexterity of manual ones. The direct interaction between the human and the robot can be the greatest advantage and the greatest limit of collaborative systems at the same time, depending on how it affects human factors like ergonomics and mental stress. This work presents
an overview of collaborative robotics considering three main dimensions: robot features, modern production systems characteristics and human factors. A literature review on how such dimensions interact is addressed and a discussion on the current state of the art is presented, showing the topics that have been already widely explored and the research gaps that should be fulfilled in the future.
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Collaborative robots (cobots) are increasingly installed at manufactory plants to keep up with the market demands, combining the benefits of advanced automation and flexible production machines. Yet, the workers' acceptance of this technology is crucial to fully exploit their potential and to make cobots a valuable support for operators themselves. The present paper reports and discusses the preliminary results of a semi-structured interview study, which investigates how adult workers of different age groups receive a cobot installed in an Adaptive Assembly Workstation (AAW), which is a cutting-edge tool customizable to match the physical features of the operator. More specifically, participants had the opportunity to experience a realistic working activity with the cobot before being interviewed. Data from the interviews indicate that participants considered the robot useful and safe. Additionally, some design proposals emerged.
CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); HCI design and evaluation methods; User studies; • Computer systems organization → Embedded and cyber-physical systems; Robotics; Robotic control.
Nowadays, in the context of Industry 4.0, advanced working environments aim at achieving a high degree of human–machine collaboration. This phenomenon occurs, on the one hand, through the correct interpretation of operators’ data by machines that can adapt their functioning to support workers, and on the other hand, by ensuring the transparency of the actions of the system itself. This study used an ad hoc system that allowed the co-registration of a set of participants’ implicit and explicit (I/E) data in two experimental conditions that varied in the level of mental workload (MWL). Findings showed that the majority of the considered I/E measures were able to discriminate the different task-related mental demands and some implicit measures were capable of predicting task performance in both tasks. Moreover, self-reported measures showed that participants were aware of such differences in MWL. Finally, the paradigm’s ecology highlights that task and environmental features may affect the reliability of the various I/E measures. Thus, these factors have to be considered in the design and development of advanced adaptive systems within the industrial context.
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