Towards seamless human robot collaboration: integrating multimodal interaction

Papanastasiou, Stergios; Kousi, Niki; Karagiannis, Panagiotis; Gkournelos, Christos; Papavasileiou, Apostolis; Dimoulas, Konstantinos; Baris, Konstantinos; Koukas, Spyridon; Michalos, George; Makris, Sotiris

doi:10.1007/s00170-019-03790-3

Cited by 116 publications

(56 citation statements)

References 30 publications

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“…Thus, effective teamwork, as in the case of HRC, requires awareness of each member of the system [9]. This is essential for both: establishing a safe environment, as well as a task planning/organization [10]. Ideally, the robot and the human would "understand" each other, which is particularly challenging due to the complementary differences [9].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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Manufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through Human-Robot Collaboration (HRC). HRC requires effective task distribution according to each parties’ distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. Thus, this challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. This paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated Machine Learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict Perceived Workload during two scenarios, namely a manual and an HRC assembly task. Perceived Workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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“…In contrast to the safety-rated stop, the presence of persons does not necessarily lead to a stop in operation, but initially results in a slowing of the robot's movements. Ensuring safety by power and force limiting means that human safety is achieved by dimensioning the engine of the robot in such a way that the forces do not exceed a predefined restriction [34]. This on the other hand severely limits payload and speed [29].…”

Section: Industrial Human-robot Interactionmentioning

confidence: 99%

Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Kopp

Baumgartner

Kinkel

2020

Int J Adv Manuf Technol

116

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Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies (n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. Finally, we argue for more application-oriented research that focuses on practically relevant factors to guide HRI research, inform cobot development, and support companies in overcoming apparent barriers.

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“…Papanastasiou et al [65] discuss the challenges in the collaboration between human operators and industrial robots for assembly operations focusing on safety and simplified interaction involving perception technologies for the robot in conjunction with wearable devices used by the operators. A robot manual guidance module and a vision system for recognition and tracking of objects are integrated with human wearable devices accompanied by augmented reality technology to support the operators in terms of production and safety aspects.…”

Section: Editorial Overview Of This Special Issue Papers and Their Comentioning

confidence: 99%

Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

Cohen

Faccio

Pilati

et al. 2019

Int J Adv Manuf Technol

142

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The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

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Towards seamless human robot collaboration: integrating multimodal interaction

Cited by 116 publications

References 30 publications

An Adaptive Human Sensor Framework for Human-Robot Collaboration

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

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