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Ghodsian N, Benfriha K, Olabi A, Gopinath V, Arnou A. Mobile Manipulators in Industry 4.0: A Review of Developments for Industrial Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:8026. [PMID: 37836855 PMCID: PMC10575048 DOI: 10.3390/s23198026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/11/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
In the realm of Industry 4.0, diverse technologies such as AI, Cyber-Physical Systems, IoT, and advanced sensors converge to shape smarter future factories. Mobile manipulators (MMs) are pivotal, fostering flexibility, adaptability, and collaboration in industrial processes. On one hand, MMs offer a remarkable level of flexibility, adaptability, and collaboration in industrial processes, facilitating swift production line changes and efficiency enhancements. On the other hand, their integration into real manufacturing environments requires meticulous considerations, such as safety, human-robot interaction, and cybersecurity. This article delves into MMs' essential role in achieving Industry 4.0's automation and adaptability by integrating mobility with manipulation capabilities. The study reviews MMs' industrial applications and integration into manufacturing systems. The most observed applications are logistics (49%) and manufacturing (33%). As Industry 4.0 advances, the paper emphasizes updating and aligning MMs with the smart factory concept by networks of sensors and the real-time analysis of them, especially for an enhanced human-robot interaction. Another objective is categorizing considerations for MMs' utilization in Industry 4.0-aligned manufacturing. This review methodically covers a wide range of considerations and evaluates existing solutions. It shows a more comprehensive approach to understanding MMs in Industry 4.0 than previous works. Key focus areas encompass perception, data analysis, connectivity, human-robot interaction, safety, virtualization, and cybersecurity. By bringing together different aspects, this research emphasizes a more integrated view of the role and challenges of MMs in the Industry 4.0 paradigm and provides insights into aspects often overlooked. A detailed and synthetic analysis of existing knowledge was performed, and insights into their future path in Industry 4.0 environments were provided as part of the contributions of this paper. The article also appraises initiatives in these domains, along with a succinct technology readiness analysis. To sum up, this study highlights MMs' pivotal role in Industry 4.0, encompassing their influence on adaptability, automation, and efficiency.
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Affiliation(s)
- Nooshin Ghodsian
- LCPI, Arts et Métiers Institute of Technology (AMIT), HESAM Université, 75013 Paris, France
| | - Khaled Benfriha
- LCPI, Arts et Métiers Institute of Technology (AMIT), HESAM Université, 75013 Paris, France
| | - Adel Olabi
- LISPEN, Arts et Métiers Institute of Technology (AMIT), HESAM Université, 59046 Lille, France
| | - Varun Gopinath
- Volvo Construction Equipment AB, 635 10 Eskilstuna, Sweden
| | - Aurélien Arnou
- OMRON Industrial Automation, 94130 Nogent-sur-Marne, France
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Lopez-de-Ipina K, Iradi J, Fernandez E, Calvo PM, Salle D, Poologaindran A, Villaverde I, Daelman P, Sanchez E, Requejo C, Suckling J. HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics. SENSORS (BASEL, SWITZERLAND) 2023; 23:1170. [PMID: 36772209 PMCID: PMC9920065 DOI: 10.3390/s23031170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/10/2023] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
Abstract
The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers' support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers' well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker's models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers' health information towards a successful risk management strategy for safe industrial Cobot environments.
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Affiliation(s)
- Karmele Lopez-de-Ipina
- Department of Psychiatry, University of Cambridge, Cambridge CB2 3PT, UK
- EleKin Lab, Systems Engineering and Automation, Computers’ Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain
| | - Jon Iradi
- EleKin Lab, Systems Engineering and Automation, Computers’ Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain
| | - Elsa Fernandez
- EleKin Lab, Systems Engineering and Automation, Computers’ Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain
| | - Pilar M. Calvo
- EleKin Lab, Systems Engineering and Automation, Computers’ Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain
| | - Damien Salle
- Tecnalia Research Centre, Tecnalia Industry and Transport Division, 20009 Donostia-San Sebastia, Spain
| | - Anujan Poologaindran
- Department of Psychiatry, University of Cambridge, Cambridge CB2 3PT, UK
- The Alan Turing Institute, British Library, London NW1 2DB, UK
| | - Ivan Villaverde
- Tecnalia Research Centre, Tecnalia Industry and Transport Division, 20009 Donostia-San Sebastia, Spain
| | - Paul Daelman
- Tecnalia Research Centre, Tecnalia Industry and Transport Division, 20009 Donostia-San Sebastia, Spain
| | - Emilio Sanchez
- Department of Mechanical Engineering and Materials, Engineering School, University of Navarra, TECNUN, 20018 Donostia-San Sebastian, Spain
- CEIT, Manufacturing Division, 20018 Donostia-San Sebastian, Spain
| | - Catalina Requejo
- Cajal Institute, Consejo Superior de Investigaciones Científicas (CSIC), 28002 Madrid, Spain
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB2 3PT, UK
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Augmented Reality-Based Interface for Bimanual Robot Teleoperation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094379] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Teleoperation of bimanual robots is being used to carry out complex tasks such as surgeries in medicine. Despite the technological advances, current interfaces are not natural to the users, who spend long periods of time in learning how to use these interfaces. In order to mitigate this issue, this work proposes a novel augmented reality-based interface for teleoperating bimanual robots. The proposed interface is more natural to the user and reduces the interface learning process. A full description of the proposed interface is detailed in the paper, whereas its effectiveness is shown experimentally using two industrial robot manipulators. Moreover, the drawbacks and limitations of the classic teleoperation interface using joysticks are analyzed in order to highlight the benefits of the proposed augmented reality-based interface approach.
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