scholarly journals Human Robot Interaction – learning how to integrate collaborative robots into manual assembly lines

2019 ◽  
Vol 31 ◽  
pp. 26-31 ◽  
Author(s):  
Henning Oberc ◽  
Christopher Prinz ◽  
Paul Glogowski ◽  
Kai Lemmerz ◽  
Bernd Kuhlenkötter
Procedia CIRP ◽  
2019 ◽  
Vol 81 ◽  
pp. 1429-1434 ◽  
Author(s):  
Niki Kousi ◽  
Christos Stoubos ◽  
Christos Gkournelos ◽  
George Michalos ◽  
Sotiris Makris

2020 ◽  
Vol 110 (03) ◽  
pp. 146-150
Author(s):  
Marco Baumgartner ◽  
Tobias Kopp ◽  
Steffen Kinkel

Die industrielle Mensch-Roboter-Interaktion (MRI) eignet sich nach Einschätzung von Experten vor allem für die spezifischen Produktionsbedingungen kleiner und mittlerer Unternehmen (KMU). Nichtsdestotrotz finden sich MRI-Lösungen derzeit vorwiegend in Großunternehmen. Eine empirische Befragung von 81 Vertretern deutscher Industrieunternehmen legt die Vermutung nahe, dass es sich hierbei nicht nur um ein Umsetzungsdefizit handelt. Vielmehr scheinen KMU die Potenziale von MRI-Lösungen systematisch zu unterschätzen.   According to experts, industrial human-robot interaction (HRI) is particularly suitable for the specific production conditions of small and medium-sized enterprises (SMEs). Nevertheless, HRI solutions are currently mainly found in large companies. An empirical survey of 81 representatives of German industrial companies suggests that this is not just due to barriers in implementing collaborative robots. On the contrary, SMEs seem to systematically underestimate the potential of HRI solutions.


2021 ◽  
Vol 24 (4) ◽  
pp. 180-199
Author(s):  
R. R. Galin ◽  
V. V. Serebrennyj ◽  
G. K. Tevyashov ◽  
A. A. Shiroky

Purpose or research is to find solvable tasks for increasing the effectiveness of collaborative interaction between people and robots in ergatic robotic systems, or, in other words, in collaborative robotic systems. Methods. A comprehensive analysis of works published in highly rated peer-reviewed open-access scientific publications was carried out to achieve this goal. Main terms and concepts of collaborative robotics are described in § 1 and their current understanding in the research community is also described. The structure of workspaces in interaction zone of a person and robot is described. The criteria for assigning robot to the class of collaborative ones are also described. The criteria for safe interaction of a person and robot in a single workspace is described in § 2. Various grounds for classifying human-robot interactions in collaborative RTAs are described in § 3. Results. A significant part of published works about collaborative robotics is devoted to the organization of safe man and robot interaction. Less attention is paid to the effectiveness improvement of such interaction. An up-to-date task in the problem of efficiency improvement of collaborative robotic systems is the identification of tasks that have already been solved in other areas - in particular, in the field of organizational systems management. The possibility of using the term "team" for collaborative robots in a collaborative PTC is stated in § 4. A formal problem setting of optimal distribution in teamwork of collaborative robots, similar to the problem of heterogeneous team formation in the theory of organizational systems management is proposed in § 5. Conclusions. Proposed task setting of optimal distribution of works in collaborative robots’ team shows possibility of using results obtained in group of mathematical models of commands formation and functioning for control of collaborative robotic systems in order to increase efficiency of people and robots interaction. It is prospectively to continue the search for adapting models and governance mechanisms to the theory of organizational system management and integrated activities methodology.


Author(s):  
Christos Papadopoulos ◽  
Ioannis Mariolis ◽  
Angeliki Topalidou-Kyniazopoulou ◽  
Grigorios Piperagkas ◽  
Dimosthenis Ioannidis ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6674
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Sang Jun Lee

Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robots to prevent fatal accidents. This paper proposes a deep learning method for identifying external collisions in 6-DoF articulated robots. The proposed method expands the idea of CollisionNet, which was previously proposed for collision detection, to identify the locations of external forces. The key contribution of this paper is uncertainty-aware knowledge distillation for improving the accuracy of a deep neural network. Sample-level uncertainties are estimated from a teacher network, and larger penalties are imposed for uncertain samples during the training of a student network. Experiments demonstrate that the proposed method is effective for improving the performance of collision identification.


2019 ◽  
Vol 109 (09) ◽  
pp. 694-698
Author(s):  
H. Petruck ◽  
A. Mertens ◽  
V. Nitsch

Dieser Beitrag beschreibt eine Möglichkeit, den Faktor Mensch bei der Planung manueller Montagevorgänge in der Mensch-Roboter-Kollaboration (MRK) zu berücksichtigen. Das Ziel ist die echtzeitfähige Prädiktion der Dauer des Montageprozesses auf Mikro-, Meso- und Makro-Ebene der Produktion auf Basis von Methods-Time Measurement (MTM). Darauf aufbauend soll durch Adaption der Montagedauer eine ergonomische Interaktion zwischen Mensch und Roboter geschaffen werden.   This paper describes one way to consider human factors when planning manual assembly processes in human-robot collaboration (HRC). The goal is the real-time prediction of the duration of the assembly process on the micro-, meso- and macro-level of the production based on Methods-Time Measurement (MTM). This prediction is then used for an ergonomic human-robot interaction design by adapting the predicted assembly durations.


2009 ◽  
Author(s):  
Matthew S. Prewett ◽  
Kristin N. Saboe ◽  
Ryan C. Johnson ◽  
Michael D. Coovert ◽  
Linda R. Elliott

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