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Automation, Volume 5, Issue 1 (March 2024) – 3 articles

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14 pages, 598 KiB  
Article
Automated Detection of Train Drivers’ Head Movements: A Proof-of-Concept Study
by David Schackmann and Esther Bosch
Automation 2024, 5(1), 35-48; https://doi.org/10.3390/automation5010003 - 18 Mar 2024
Viewed by 504
Abstract
With increasing automation in the rail sector, the train driver’s task changes from full control to a supervisory position. This bears the risk of monotony and subsequent changes in visual attention, possibly for the worse. Similar to concepts in car driving, one solution [...] Read more.
With increasing automation in the rail sector, the train driver’s task changes from full control to a supervisory position. This bears the risk of monotony and subsequent changes in visual attention, possibly for the worse. Similar to concepts in car driving, one solution for this could be driver state monitoring with triggered interventions in case of declining task attention. Previous research on train drivers’ visual attention has used eye tracking. In contrast, head tracking is easier to realize within the train driver cabin. This study set out to test whether head tracking is a feasible alternative to eye tracking and can provide similar findings. Based on previous eye-tracking research, we compared differences in head movements in automated vs. manual driving, and for different levels of driving speed and driving experience. We conducted a study with 25 active train drivers in a high-fidelity train simulator. Statistical analyses revealed no significant difference in the vertical head movements between automation levels. There was a significant difference in the horizontal head movements, with train drivers looking more to the right for manual driving. We found no significant influence of driving speed and experience on head movements. Safety implications and the feasibility of head tracking as an alternative to eye tracking are discussed. Full article
(This article belongs to the Topic Vehicle Safety and Automated Driving)
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22 pages, 5448 KiB  
Article
Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot
by Khaled H. Mahmoud, G. T. Abdel-Jaber and Abdel-Nasser Sharkawy
Automation 2024, 5(1), 13-34; https://doi.org/10.3390/automation5010002 - 14 Mar 2024
Viewed by 557
Abstract
In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no [...] Read more.
In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature. Full article
(This article belongs to the Collection Smart Robotics for Automation)
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12 pages, 1131 KiB  
Article
Virtual Commissioning of Linked Cells Using Digital Models in an Industrial Metaverse
by Marco Ullrich, Rashik Thalappully, Frieder Heieck and Bernd Lüdemann-Ravit
Automation 2024, 5(1), 1-12; https://doi.org/10.3390/automation5010001 - 02 Feb 2024
Viewed by 1134
Abstract
Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are [...] Read more.
Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are able to holistically simulate complete factories are limited in terms of the modelling details required for the analysis of single manufacturing cells (e.g., manufacturer-independence of the individual digital twins) and their ability for virtual commissioning. This paper presents three options for realising a virtual commissioning of linked cells using a 3D integration platform with NVIDIA Omniverse, consisting of two different digital models fused into a combined model, also representing material flow. First, with a source/sink solution and unidirectional connector controlled by OPC UA; secondly, with a bidirectional connector, developed in the course of this elaboration, and an extension of the 3D integration platform controlled by Apache Kafka; thirdly, with a bidirectional connector and using only an extension of the 3D integration platform. The research demonstrates that virtually commissioning multiple linked digital twins from different manufacturers in a 3D platform with material flow makes a significant contribution to the industrial metaverse. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
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