How to compete with robots by assessing job automation risks and resilient alternatives

The assistance of BAZAR robot promotes improved upper limb motor coordination in workers performing an actual use-case manual material handling

This study aims at evaluating upper limb muscle coordination and activation in workers performing an actual use-case manual material handling (MMH). The study relies on the comparison of the workers' muscular activity while they perform the task, with and without the help of a dual-arm cobot (BAZAR). Eleven participants performed the task and the flexors and extensors muscles of the shoulder, elbow, wrist, and trunk joints were recorded using bipolar electromyography. The results showed that, when the particular MMH was carried out with BAZAR, both upper limb and trunk muscular co-activation and activation were decreased. Therefore, technologies that enable human-robot collaboration (HRC), which share a workspace with employees, relieve employees of external loads and enhance the effectiveness and calibre of task completion. Additionally, these technologies improve the worker's coordination, lessen the physical effort required to interact with the robot, and have a favourable impact on his or her physiological motor strategy. Practitioner summary: Upper limb and trunk muscle co-activation and activation is reduced when a specific manual material handling was performed with a cobot than without it. By improving coordination, reducing physical effort, and changing motor strategy, cobots could be proposed as an ergonomic intervention to lower workers' biomechanical risk in industry.

Interdisciplinary evaluation of a robot physically collaborating with workers

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.

Digitalisierung und De-Karbonisierung – die zentralen Herausforderungen der Medizin

Vor rund einem halben Jahr, im April 2022, veröffentlichten Schweizer Forscher der Eidgenössischen Technische Hochschule Lausanne im Magazin „Science Robotics“1 eine Studie, die beschreibt, welche Berufsbilder von der Digitalisierung und dem Einsatz Künstlicher Intelligenz besonders bedroht sind. Die Untersuchung analysiert auf Basis von rund 1.000 Berufsbildern des amerikanischen Arbeitsministeriums die Gefahr, perspektivisch durch Roboter oder den Einsatz Künstlicher Intelligenz ersetzt zu werden. Dazu haben die Wissenschaftler einen Automatisierungs-Risiko-Index errechnet, der untersucht, welche Fähigkeiten für den jeweiligen Job nötig sind und welche auch Maschinen ausführen können. Schlachter und Fleischverpacker haben demnach das größte Risiko, von Robotern ersetzt zu werden, rund 78 Prozent der für die Ausübung der Tätigkeit notwendigen Fähigkeiten haben die Maschinen bereits heute. Am anderen, vermeintlich sicheren Ende der Skala finden sich die Physiker mit einem Automatisierungs-Index von 48 Prozent. Das heißt aber im Umkehrschluss: Fast die Hälfte der Fähigkeiten werden ebenfalls bereits heute von Maschinen erreicht.

Answering the great automation question

Modeling an automation risk index for job profiles provides insights into worker reallocation and informs retraining policy.