scholarly journals Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4626
Author(s):  
Aitor Toichoa Eyam ◽  
Wael M. Mohammed ◽  
Jose L. Martinez Lastra
Keyword(s):  
Industry 4.0 ◽  
Communication Systems ◽  
Manufacturing Systems ◽  
Emotional State ◽  
Robotic Systems ◽  
Physical Interaction ◽  
Closing The Gap ◽  
New Challenges ◽  
Automation Level ◽  
And Control

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

New Challenges for Quality Assurance of Manufacturing Processes in Industry 4.0

2017 ◽  
Vol 261 ◽  
pp. 481-486 ◽  
Author(s):  
Béla Illés ◽  
Péter Tamás ◽  
Péter Dobos ◽  
Róbert Skapinyecz
Keyword(s):  
Quality Assurance ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Processes ◽  
Research Directions ◽  
Specific Cost ◽  
Relevant Role ◽  
New Challenges ◽  
Product And Process ◽  
Complex Manufacturing Systems

Nowadays the flexibility and specific cost of manufacturing have a relevant role in the competitiveness of the companies. In our opinion the most important objective of Industry 4.0 is the realization of intermittent manufacturing at mass production’s productivity and specific cost. This aim can be only reached by creating more complex manufacturing systems. The increase in manufacturing complexity results in new challenges in the quality assurance of manufacturing processes. We can collect new types of data that enable the improvement of product and process service quality. This paper introduces the essence of Industry 4.0, as well as the new challenges for the quality assurance of manufacturing processes. Possible research directions for overcoming challenges are also presented.

scholarly journals The importance of prediction methods in industry 4.0 on the example of steel industry

2019 ◽  
Vol 2 (1) ◽  
pp. 283-295
Author(s):  
Bożena Gajdzik
Keyword(s):  
Empirical Data ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Steel Production ◽  
Prediction Methods ◽  
Production Volume ◽  
Material Base ◽  
Production Processes ◽  
Business Objective ◽  
New Challenges

Abstract This paper presents the importance of the prediction of steel production in industry 4.0 along with forecasts for steel production in the world until 2022. In the last two decades, the virtual world has been increasingly entering production. Today’s manufacturing systems are becoming faster and more flexible – easily adaptable to new products. Steel is the basic structural material (base material) for many industrial sectors. Industries such as automotive, mechanical engineering, construction and transport use steel in their production processes. Prediction methods in cyber-physical production systems are gaining in importance. The task of prediction is to reduce risk in the decision-making process. In autonomous manufacturing systems in industry 4.0 the role of prediction is more active than passive. Forecasts have the following functions: warning, reaction, prevention, normative, etc. The growing number of customized solutions in industry 4.0 translates into new challenges in the production process. Manufacturers must respond to individual customer needs more quickly, be able to personalize products while reducing energy and resource costs (saving energy and resources can increase the product competitiveness). The modern market becomes increasingly unpredictable. Production prediction under such conditions should be carried out continuously, which is possible because there is more empirical data and access to data. Information from the ongoing monitoring of the company’s production is directly transferred to the prospective evaluation. In view of the contemporary reciprocal use of automation, data processing, data exchange and manufacturing techniques, there is greater access to external data, e.g. on production in different target markets and with global, international, national, regional coverage. Companies can forecast in real time, and the forecasts obtained give the possibility to quickly change their production. Industry 4.0 (from the business objective point of view) aims to provide companies with concrete economic benefits – primarily by reducing manufacturing costs, standardizing and stabilizing quality, increasing productivity. Industry 4.0 aims to create a given autonomous smart factory system in which machines, factory components and services communicate and cooperate with each other, producing a personalized product. The aim of this paper is to present new challenges in the production processes in relation to steel production, as well as to prepare and present forecasts of (quantitative) steel production of territorial, global and temporary range until 2022, taking into account the applied production technologies (BOF and EAF). For forecasting purposes, classic trend models and adaptive trend models were used. This methodology was used to build separate forecasts for: total steel production, BOF steel and EAF steel. Empirical data is world steel production in 2000-2017 (annual production volume in Mt).

scholarly journals Prognostics-aware multi-robot route planning to extend the lifetime

2021 ◽  
Author(s):  
U˘gur Yayan ◽  
◽  
Ahmet Yazıcı ◽  
˙Inci Sarıc¸ic¸ek ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Route Planning ◽  
The Self ◽  
Robotic Systems ◽  
Self Awareness ◽  
Intelligent Devices ◽  
Multi Robot

Transformation to Industry 4.0, manufacturing systems need more intelligent devices with capable of self-awareness. Prognostic-aware robotic systems are one of key components for the self-awareness in manufacturing. The prognostics-aware route planning is one of the key components for the success of the multi-robot team during the long-term and uninterrupted operations with also extending lifetime and reducing maintenances costs. In this study, a Prognostics-aware Multi-Robot Route Planning (P-MRRP) algorithm is proposed for extending lifetime of the robot team. In the P-MRRP algorithm, firstly routes are obtained from route set construction algorithm and most reliable route set is selected by calculating Probability of Route Completion (PoRC) according to reliability of the robot team. The proposed algorithm also considers effect of load during the route of robots. In this study, the reliability of the robot is updated considering both the travelled distances with route of robot and the load of robot between pickup and/or delivery nodes. The results of P-MRRP algorithm are compared with the results of classical MRRP. The performance of the algorithm shows that the lifetime of mobile robot team can be extended by using the P-MRRP algorithm.

Convergence in norming and control of modern wireless technologies electromagnetic fields

2020 ◽  
Vol 60 (9) ◽  
pp. 610-613
Author(s):  
Michail Yu. Maslov ◽  
Yuri M. Spodobaev
Keyword(s):  
Wireless Communication ◽  
Electromagnetic Fields ◽  
Communication Systems ◽  
Communication Technologies ◽  
Telecommunications Industry ◽  
High Tech ◽  
Wireless Technologies ◽  
Almost All ◽  
Network Technologies ◽  
And Control

Telecommunications industry evolution shows the highest rates of transition to high-tech systems and is accompanied by a trend of deep mutual penetration of technologies - convergence. The dominant telecommunication technologies have become wireless communication systems. The widespread use of modern wireless technologies has led to the saturation of the environment with technological electromagnetic fields and the actualization of the problems of protecting the population from them. This fundamental restructuring has led to a uniform dense placement of radiating fragments of network technologies in the mudflow areas. The changed parameters of the emitted fields became the reason for the revision of the regulatory and methodological support of electromagnetic safety. A fragmented structural, functional and parametric analysis of the problem of protecting the population from the technological fields of network technologies revealed uncertainty in the interpretation of real situations, vulnerability, weakness and groundlessness of the methodological basis of sanitary-hygienic approaches. It is shown that this applies to all stages of the electromagnetic examination of the emitting fragments of network technologies. Distrust arises on the part of specialists and the population in not only the system of sanitary-hygienic control, but also the safety of modern network technologies is being called into question. Growing social tensions and radio phobia are everywhere accompanying the development of wireless communication technologies. The basis for solving almost all problems of protecting the population can be the transfer of subjective methods and means of monitoring and sanitary-hygienic control of electromagnetic fields into the field of IT.

scholarly journals Open Source Control Device for Industry 4.0 Based on RAMI 4.0

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 869
Author(s):  
Pablo F. S. Melo ◽  
Eduardo P. Godoy ◽  
Paolo Ferrari ◽  
Emiliano Sisinni
Keyword(s):  
Open Source ◽  
Industry 4.0 ◽  
Product Life Cycle ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Control Device ◽  
Source Control ◽  
Reference Architecture ◽  
Architecture Model ◽  
Complex Relationships

The technical innovation of the fourth industrial revolution (Industry 4.0—I4.0) is based on the following respective conditions: horizontal and vertical integration of manufacturing systems, decentralization of computing resources and continuous digital engineering throughout the product life cycle. The reference architecture model for Industry 4.0 (RAMI 4.0) is a common model for systematizing, structuring and mapping the complex relationships and functionalities required in I4.0 applications. Despite its adoption in I4.0 projects, RAMI 4.0 is an abstract model, not an implementation guide, which hinders its current adoption and full deployment. As a result, many papers have recently studied the interactions required among the elements distributed along the three axes of RAMI 4.0 to develop a solution compatible with the model. This paper investigates RAMI 4.0 and describes our proposal for the development of an open-source control device for I4.0 applications. The control device is one of the elements in the hierarchy-level axis of RAMI 4.0. Its main contribution is the integration of open-source solutions of hardware, software, communication and programming, covering the relationships among three layers of RAMI 4.0 (assets, integration and communication). The implementation of a proof of concept of the control device is discussed. Experiments in an I4.0 scenario were used to validate the operation of the control device and demonstrated its effectiveness and robustness without interruption, failure or communication problems during the experiments.

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