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. 2023 Oct 11;18(10):e0291410.
doi: 10.1371/journal.pone.0291410. eCollection 2023.

Interdisciplinary evaluation of a robot physically collaborating with workers

Andrea Cherubini  1 , Benjamin Navarro  1 , Robin Passama  1 , Sonny Tarbouriech  1 , Shirley A Elprama  2 , An Jacobs  2 , Susanne Niehaus  3 , Sascha Wischniewski  3 , Freek J Tönis  4 , Pim L Siahaya  4 , Giorgia Chini  5 , Tiwana Varrecchia  5 , Alberto Ranavolo  5
Affiliations

Interdisciplinary evaluation of a robot physically collaborating with workers

Andrea Cherubini et al. PLoS One. .

Abstract

Collaborative Robots-CoBots-are emerging as a promising technological aid for workers. To date, most CoBots merely share their workspace or collaborate without contact, with their human partners. We claim that robots would be much more beneficial if they physically collaborated with the worker, on high payload tasks. To move high payloads, while remaining safe, the robot should use two or more lightweight arms. In this work, we address the following question: to what extent can robots help workers in physical human-robot collaboration tasks? To find an answer, we have gathered an interdisciplinary group, spanning from an industrial end user to cognitive ergonomists, and including biomechanicians and roboticists. We drew inspiration from an industrial process realized repetitively by workers of the SME HANKAMP (Netherlands). Eleven participants replicated the process, without and with the help of a robot. During the task, we monitored the participants' biomechanical activity. After the task, the participants completed a survey with usability and acceptability measures; seven workers of the SME completed the same survey. The results of our research are the following. First, by applying-for the first time in collaborative robotics-Potvin's method, we show that the robot substantially reduces the participants' muscular effort. Second: we design and present an unprecedented method for measuring the robot reliability and reproducibility in collaborative scenarios. Third: by correlating the worker's effort with the power measured by the robot, we show that the two agents act in energetic synergy. Fourth: the participant's increasing level of experience with robots shifts his/her focus from the robot's overall functionality towards finer expectations. Last but not least: workers and participants are willing to work with the robot and think it is useful.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The gear shaping scenario and its components.
(A) Real scenario at SME HANKAMP with (B) layout. (C) Mock-up scenario at Université de Montpellier, with (D) layout and (E) detail of the deburring phase in collaboration with BAZAR. The detail of the gear shaping process is given in Fig 2.
Fig 2
Fig 2. Comparison between the current process and our experiment.
The table reports all tasks (realized by either human or robot) in: the current industrial process, the laboratory mock-up process without the robot, and the laboratory mock-up process with the robot.
Fig 3
Fig 3. Mindmap of the experimental procedure used in this work.
Red: Involved agents (robot and worker). Green: data measured on the agents Blue: features of the human-robot collaboration derived from the measured data.
Fig 4
Fig 4. Potvin results with (wB) and without (woB) CoBot.
(A) Tolerance Limit Value (TLV) curves and mean duty cycle (%DC) along participants, in function of the maximal voluntary contraction (%MVC) of six right upper arm muscles (BBCL: biceps brachii caput longum; TBCL: triceps brachii caput longum; AD: anterior deltoideus; PD: posterior deltoideus; FCR: flexor carpi radialis; ECR: extensor carpi radialis). (B) TLV curves with mean and SD of the %DC among all participants in function of %MVC (light grey: woB, dark grey: wB). * Significant differences.
Fig 5
Fig 5
(A) Mean curve of BAZAR power (in W) among all subjects. (B) Mean curve of Biceps brachii caput longum (BBCL) %MVC among all subjects. (C) Correlation among mean values of BAZAR power and BBCL %MVC.
Fig 6
Fig 6
(A) Qualitative rendering of BAZAR operation with the trajectories of the gear center for each of the 33 experiments. (B) Joint angle trajectories q1…q15 (in rad) across all experiments with median (dark blue) +/- range (light blue). Top to bottom and left to right: heading of the base, left arm joints and right arm joints. (C) Boxplots of the precisions (distances from the median) of the 15 joint angles, in the same order as in (B).
Fig 7
Fig 7. Results on usability aspects; comparison between participants and workers.
(A) Level of prior experience is higher for workers. (B) Differences regarding Dialogue principles: participants value most Suitability for the task, while workers most expect Adaptability and Ease of Learning. (C) Similar perception of mass-media reportage: reports are mostly positive and the media can influence people in their attitude towards robots.
Fig 8
Fig 8. Acceptance measures for workers and participants.
See this image and copyright information in PMC

References

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