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Robotics
Special Issues
Industrial Robotics in Industry 4.0
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Industrial Robotics in Industry 4.0

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Industrial Robots and Automation".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 17665

Special Issue Editors

Dr. Wilfried Lepuschitz

E-Mail Website
Guest Editor
Practical Robotics Institute Austria (PRIA), Wexstraße 19-23, 1200 Vienna, Austria
Interests: industrial robotics; semantic systems; knowledge modelling; ontologies; industrial automation; manufacturing systems; educational robotics
Prof. Dr. Markus Vincze

E-Mail Website
Guest Editor
Automation and Control Institute, Vienna University of Technology, Gusshausstrasse 27-29 / E376, 1040 Vienna, Austria
Interests: robot vision; service robots; object detection; scene understanding; robots at home
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas Meurer

E-Mail Website
Guest Editor
Chair of Automatic Control, Christian-Albrechts-Unversität of Kiel Kaiserstraße 2, R. F-113, 24143 Kiel, Germany
Interests: control theory; flexible structures; robotics; multi-agent systems; observer design
Dr. Munir Merdan

E-Mail Website
Guest Editor
Practical Robotics Institute Austria (PRIA), Wexstraße 19-23, 1200 Vienna, Austria
Interests: industrial robotics; educational robotics; artificial intelligence; advanced manufacturing

Special Issue Information

Dear Colleagues,

Industrial robots represent one of the key drivers of Industry 4.0. In recent years, they have significantly evolved, becoming more productive, agile, safer, and collaborative. In this context, numerous approaches and technological innovations have been introduced in this domain to tackle diverse problems and challenges in a variety of industries and applications.

The goal of this Special Issue is to present state-of-the-art contributions to industrial robot technology and present innovative developments, methodologies, and trends in research and real-world applications.

This Special Issue will highlight advances in the following areas but is not limited to them:

  • Design and modeling of robotic systems;
  • Robot kinematics/dynamics/control;
  • AI technologies;
  • Human-robot interactions;
  • Cooperative robots;
  • Flexible grippers and tactile sensing;
  • Robot vision;
  • Motion planning and optimization;
  • Experimental evaluation and benchmarking;
  • Robotic applications.

Dr. Wilfried Lepuschitz
Prof. Dr. Markus Vincze
Prof. Dr. Thomas Meurer
Dr. Munir Merdan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Design and modeling of robotic systems
  • Robot kinematics/dynamics/control
  • AI technologies
  • Human-robot interactions
  • Cooperative robots
  • Flexible grippers and tactile sensing
  • Robot vision
  • Motion planning and optimization
  • Experimental evaluation and benchmarking
  • Robotic applications

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

12 pages, 3965 KiB  
Article
Integrating the Generative Adversarial Network for Decision Making in Reinforcement Learning for Industrial Robot Agents
by Neelabh Paul, Vaibhav Tasgaonkar, Rahee Walambe and Ketan Kotecha
Robotics 2022, 11(6), 150; https://doi.org/10.3390/robotics11060150 - 9 Dec 2022
Cited by 2 | Viewed by 2625
Abstract
Many robotics systems carrying certain payloads are employed in manufacturing industries for pick and place tasks. The system experiences inefficiency if more or less weight is introduced. If a different payload is introduced (either due to a change in the load or a [...] Read more.
Many robotics systems carrying certain payloads are employed in manufacturing industries for pick and place tasks. The system experiences inefficiency if more or less weight is introduced. If a different payload is introduced (either due to a change in the load or a change in the parameters of the robot system), the robot must be re-trained with the new weight/parameters and the new network must be trained. Parameters such as the robot weight, length of limbs, or new payload may vary for an agent depending on the circumstance. Parameter changes pose a problem to the agent in achieving the same goal it is expected to achieve with the original parameters. Hence, it becomes mandatory to re-train the agent with the new parameters in order for it to achieve its goal. This research proposes a novel framework for the adaption of varying conditions on a robot agent in a given simulated environment without any retraining. Utilizing the properties of Generative Adversarial Network (GAN), the agent is able to train only once with reinforcement learning and by tweaking the noise vector of the generator in the GAN network, the agent can adapt to new conditions accordingly and demonstrate similar performance as if it were trained with the new physical attributes using reinforcement learning. A simple CartPole environment is considered for the experimentation, and it is shown that with the propose approached the agent remains stable for more iterations. The approach can be extended to the real world in the future. Full article
(This article belongs to the Special Issue Industrial Robotics in Industry 4.0)
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24 pages, 5772 KiB  
Article
Elasto-Geometrical Model-Based Control of Industrial Manipulators Using Force Feedback: Application to Incremental Sheet Forming
by Marwan Johra, Eric Courteille, Dominique Deblaise and Sylvain Guégan
Robotics 2022, 11(2), 48; https://doi.org/10.3390/robotics11020048 - 12 Apr 2022
Cited by 3 | Viewed by 3224
Abstract
This paper aims to improve the positioning accuracy of serial industrial manipulators using force feedback in manufacturing processes by implementing an elasto-geometrical model-based control. Initially, the real-time position control strategy using a force feedback to elastically correct the Tool Center Point (TCP) pose [...] Read more.
This paper aims to improve the positioning accuracy of serial industrial manipulators using force feedback in manufacturing processes by implementing an elasto-geometrical model-based control. Initially, the real-time position control strategy using a force feedback to elastically correct the Tool Center Point (TCP) pose of serial industrial manipulators is detailed. To continue, an efficient model structure identification and calibration is proposed to shorten the elasto-geometrical modeling process. The Virtual Joint Method (VJM) is chosen to iterate and complete the robot stiffness modeling. This method considers that the elastic deformations are only localized at the joints of the robot. An appropriate and original test-model approach allows a minimum of optimization iterations to find the best compromise between complexity and accuracy of the modeling. The proposed approach is illustrated in detail by the Stäubli TX200 robot modeling. Finally, the reliability and responsiveness of the developed control framework is then evaluated through experimental tests in an Incremental Sheet Forming (ISF) context. An average improvement of 70% in trajectory-tracking accuracy is achieved during these tests. Overall, the high accuracy and responsiveness of the developed system demonstrate a promising potential for deploying industrial manipulators to a cost-effective manufacturing processes in industry 4.0. Full article
(This article belongs to the Special Issue Industrial Robotics in Industry 4.0)
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21 pages, 8040 KiB  
Article
Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces
by Lee Clift, Divya Tiwari, Chris Scraggs, Windo Hutabarat, Lloyd Tinkler, Jonathan M. Aitken and Ashutosh Tiwari
Robotics 2022, 11(1), 25; https://doi.org/10.3390/robotics11010025 - 11 Feb 2022
Viewed by 3334
Abstract
This work proposes a novel solution for detecting and tracing spatially varying edges of large manufacturing workpieces, using a consumer grade RGB depth camera, with only a partial view of the workpiece and without prior knowledge. The proposed system can visually detect and [...] Read more.
This work proposes a novel solution for detecting and tracing spatially varying edges of large manufacturing workpieces, using a consumer grade RGB depth camera, with only a partial view of the workpiece and without prior knowledge. The proposed system can visually detect and trace various edges, with a wide array of degrees, to an accuracy of 15 mm or less, without the need for any previous information, setup or planning. A combination of physical experiments on the setup and more complex simulated experiments were conducted. The effectiveness of the system is demonstrated via simulated and physical experiments carried out on both acute and obtuse edges, as well as typical aerospace structures, made from a variety of materials, with dimensions ranging from 400 mm to 600 mm. Simulated results show that, with artificial noise added, the solution presented can detect aerospace structures to an accuracy of 40 mm or less, depending on the amount of noise present, while physical aerospace inspired structures can be traced with a consistent accuracy of 5 mm regardless of the cardinal direction. Compared to current industrial solutions, the lack of required planning and robustness of edge detection means it should be able to complete tasks more quickly and easily than the current standard, with a lower financial and computational cost than the current techniques being used within. Full article
(This article belongs to the Special Issue Industrial Robotics in Industry 4.0)
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18 pages, 1319 KiB  
Article
Impact of Cycle Time and Payload of an Industrial Robot on Resource Efficiency
by Florian Stuhlenmiller, Steffi Weyand, Jens Jungblut, Liselotte Schebek, Debora Clever and Stephan Rinderknecht
Robotics 2021, 10(1), 33; https://doi.org/10.3390/robotics10010033 - 12 Feb 2021
Cited by 11 | Viewed by 5848
Abstract
Modern industry benefits from the automation capabilities and flexibility of robots. Consequently, the performance depends on the individual task, robot and trajectory, while application periods of several years lead to a significant impact of the use phase on the resource efficiency. In this [...] Read more.
Modern industry benefits from the automation capabilities and flexibility of robots. Consequently, the performance depends on the individual task, robot and trajectory, while application periods of several years lead to a significant impact of the use phase on the resource efficiency. In this work, simulation models predicting a robot’s energy consumption are extended by an estimation of the reliability, enabling the consideration of maintenance to enhance the assessment of the application’s life cycle costs. Furthermore, a life cycle assessment yields the greenhouse gas emissions for the individual application. Potential benefits of the combination of motion simulation and cost analysis are highlighted by the application to an exemplary system. For the selected application, the consumed energy has a distinct impact on greenhouse gas emissions, while acquisition costs govern life cycle costs. Low cycle times result in reduced costs per workpiece, however, for short cycle times and higher payloads, the probability of required spare parts distinctly increases for two critical robotic joints. Hence, the analysis of energy consumption and reliability, in combination with maintenance, life cycle costing and life cycle assessment, can provide additional information to improve the resource efficiency. Full article
(This article belongs to the Special Issue Industrial Robotics in Industry 4.0)
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