Human-robot collaboration on an assembly work with extended cognition approach

Üstünel, Zeynep; Gündüz, Tülin

doi:10.1299/jamdsm.2017jamdsm0057

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…The evaluation of mental workload is a key issue to research and develop for human machine interfaces, as well as to find levels of comfort, satisfaction, efficiency, and security in the workplace [17]. Moreover, some researchers explain in detail how important the cognitive load and the mental workload are in the design of workplaces for assembly tasks [18]. A laboratory scenario in a manufacturing context has been designed to create an environment where humans can work side-by-side with robots in close proximity [19].…”

Section: Objectivementioning

confidence: 99%

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

2021

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In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

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Section: Objectivementioning

confidence: 99%

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

2021

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“…Their results highlight respective research gaps in mental workload, especially with Industry 4.0 technologies or assistive technologies in general. Only three papers are included in the synthesis of their review [3][4][5][6].…”

Section: Wording For Search In Title-abstract-abstractmentioning

confidence: 99%

“…Participants Planned The aim is to achieve a similar number of participants to similar research projects. Wu et al (2016) and Ustunel and Gunduz (2017) performed their experiment with around 40 participants, and Gueltieri et al (2022) had only 14 (see Table4)[3][4][5][6].…”

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confidence: 99%

Making Cognitive Ergonomics in the Human–Computer Interaction of Manufacturing Execution Systems Assessable: Experimental and Validation Approaches to Closing Research Gaps

Dörner,

Bures,

Simon

et al. 2024

Machines

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Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed.

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“…The evaluation of mental workload is a key issue to research and develop for human machine interfaces, as well as to find levels of comfort, satisfaction, efficiency, and security in the workplace [98]. Moreover, some researchers explain in detail how important the cognitive load and the mental workload are in the design of workplaces for assembly tasks [114]. A laboratory scenario in a manufacturing context has been designed to create an environment where humans can work side-by-side with robots in close proximity [117].…”

Section: Cognitive Interactionmentioning

confidence: 99%

A socio-technical approach for assistants in human-robot collaboration in industry 4.0

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The introduction of technologies disruptive of Industry 4.0 in the workplace integrated through human cyber-physical systems causes operators to face new challenges. These are reflected in the increased demands presented in the operator's capabilities physical, sensory, and cognitive demands. In this research, cognitive demands are the most interesting. In this perspective, assistants are presented as a possible solution, not as a tool but as a set of functions that amplify human capabilities, such as exoskeletons, collaborative robots for physical capabilities, virtual and augmented reality for sensory capabilities. Perhaps chatbots and softbots for cognitive capabilities, then the need arises to ask ourselves: How can operator assistance systems 4.0 be developed in the context of industrial manufacturing? In which capacities does the operator need more assistance? From the current paradigm of systematization, different approaches are used within the context of the workspace in industry 4.0. Thus, the functional resonance analysis method (FRAM) is used to model the workspace from the sociotechnical system approach, where the relationships between the components are the most important among the functions to be developed by the human-robot team. With the use of simulators for both robots and robotic systems, the behavior of the variability of the human-robot team is analyzed. Furthermore, from the perspective of cognitive systems engineering, the workspace can be studied as a joint cognitive system, where cognition is understood as distributed, in a symbiotic relationship between the human and technological agents. The implementation of a case study as a human-robot collaborative workspace allows evaluating the performance of the human-robot team, the impact on the operator's cognitive abilities, and the level of collaboration achieved in the human-robot team through a set of metrics and proven methods in other areas, such as cognitive systems engineering, human-machine interaction, and ergonomics. We conclude by discussing the findings and outlook regarding future research questions and possible developments. La introducción de tecnologías disruptivas de Industria 4.0 en el lugar de trabajo integradas a través de sistemas ciberfísicos humanos hace que los operadores enfrenten nuevos desafíos. Estos se reflejan en el aumento de las demandas presentadas en las capacidades físicas, sensoriales y cognitivas del operador. En esta investigación, las demandas cognitivas son las más interesantes. En esta perspectiva, los asistentes se presentan como una posible solución, no como una herramienta sino como un conjunto de funciones que amplifican las capacidades humanas, como exoesqueletos, robots colaborativos para capacidades físicas, realidad virtual y aumentada para capacidades sensoriales. Quizás chatbots y softbots para capacidades cognitivas, entonces surge la necesidad de preguntarnos: ¿Cómo se pueden desarrollar los sistemas de asistencia al operador 4.0 en el contexto de la fabricación industrial? ¿En qué capacidades el operador necesita más asistencia? A partir del paradigma actual de sistematización, se utilizan diferentes enfoques dentro del contexto del espacio de trabajo en la industria 4.0. Así, se utiliza el método de análisis de resonancia funcional (FRAM) para modelar el espacio de trabajo desde el enfoque del sistema sociotécnico, donde las relaciones entre los componentes son las más importantes entre las funciones a desarrollar por el equipo humano-robot. Con el uso de simuladores tanto para robots como para sistemas robóticos se analiza el comportamiento de la variabilidad del equipo humano-robot. Además, desde la perspectiva de la ingeniería de sistemas cognitivos, el espacio de trabajo puede ser estudiado como un sistema cognitivo conjunto, donde la cognición se entiende distribuida, en una relación simbiótica entre los agentes humanos y tecnológicos. La implementación de un caso de estudio como un espacio de trabajo colaborativo humano-robot permite evaluar el desempeño del equipo humano-robot, el impacto en las habilidades cognitivas del operador y el nivel de colaboración alcanzado en el equipo humano-robot a través de un conjunto de métricas y métodos probados en otras áreas, como la ingeniería de sistemas cognitivos, la interacción hombre-máquina y la ergonomía. Concluimos discutiendo los hallazgos y las perspectivas con respecto a futuras preguntas de investigación y posibles desarrollos.

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Human-robot collaboration on an assembly work with extended cognition approach

Cited by 10 publications

References 36 publications

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Making Cognitive Ergonomics in the Human–Computer Interaction of Manufacturing Execution Systems Assessable: Experimental and Validation Approaches to Closing Research Gaps

A socio-technical approach for assistants in human-robot collaboration in industry 4.0

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