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The effects of role transitions and adaptation in human-cobot collaboration

Authors :
Lorenzo Vianello
Serena Ivaldi
Alexis Aubry
Luka Peternel
Centre de Recherche en Automatique de Nancy (CRAN)
Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN)
Inria Nancy - Grand Est
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS)
Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Istituto Italiano di Tecnologia (IIT)
Jozef Stefan Institute [Ljubljana] (IJS)
Delft University of Technology (TU Delft)
Université de Lorraine (UL)
Action Exploratoire C-SHIFT dans le cadre de l'initiative Lorraine Université d'Excellence (LUE)
Source :
Journal of Intelligent Manufacturing, Journal of Intelligent Manufacturing, 2023, ⟨10.1007/s10845-023-02104-5⟩
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

International audience; Collaborative robots (cobots) have the potential to augment the productivity and life quality of human operators in the context of Industry 4.0 by providing them with physical assistance. For this reason, it is necessary to define the relationship between humans and cobots and to study how the two agents adapt to each other. However, to the best of our knowledge, literature is still missing insight into how humans perceive and react to changes in the cobot behavior (e.g. changes in the learned trajectory and in the role the robot assumes). Specifically, a study of how humans adapt to changing roles and control strategies of collaborating robots is missing. To fill this gap, we propose a human study in which 16 participants executed a collaborative human-robot sawing task where the cobot altered between three different control strategies. We examinedhuman adaptation when cobot suddenly changed the control strategy from one to another, resulting in six experimental conditions. The experiments were performed on a setup involving Kuka LBR iiwa robotic arm. The results suggest that transition influences movement performance in the early stages and at steady state, subjects prefer to abandon modes that require more effort and they adapt faster to energy-demanding modes. Finally, for the specific task we studied, subjects tend to prefer collaborative modes to ones in which the robot assumes a fixed role.

Details

Language :
English
ISSN :
09565515 and 15728145
Database :
OpenAIRE
Journal :
Journal of Intelligent Manufacturing, Journal of Intelligent Manufacturing, 2023, ⟨10.1007/s10845-023-02104-5⟩
Accession number :
edsair.doi.dedup.....9f6bc1f84c2b65261e0fe6beeceb9c38
Full Text :
https://doi.org/10.1007/s10845-023-02104-5⟩