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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http://dx.doi.org/10.3390/s23198026 | DOI Listing |
J Neuroeng Rehabil
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Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
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December 2024
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, PR China; Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, 5340 Xiping Road, Beichen District, Tianjin 300401, PR China. Electronic address:
Transition-metal-loaded carbon-based electrocatalysts are promising alternatives to conventional precious metal electrocatalysts for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in high-performance zinc-air batteries. However, efficiently doping transition-metal single atoms onto carbon-based frameworks is a significant challenge. Herein, an improved template-sacrificing method combining a two-step carbonization process is proposed to fabricate Cu/Co diatomic sites coanchored on a three-dimensional nitrogen-doped carbon-based framework.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
Department of Systems Engineering and Automation, University Carlos III of Madrid, Av. de la Universidad, 30, 28911 Leganes, Spain.
The concept of joint attention holds significant importance in human interaction and is pivotal in establishing rapport, understanding, and effective communication. Within social robotics, enhancing user perception of the robot and promoting a sense of natural interaction with robots becomes a central element. In this sense, emulating human-centric qualities in social robots, such as joint attention, defined as the ability of two or more individuals to focus on a common event simultaneously, can increase their acceptability.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
School of Artificial Intelligence, Tongmyong University, Busan 48520, Republic of Korea.
Depth estimation plays a pivotal role in advancing human-robot interactions, especially in indoor environments where accurate 3D scene reconstruction is essential for tasks like navigation and object handling. Monocular depth estimation, which relies on a single RGB camera, offers a more affordable solution compared to traditional methods that use stereo cameras or LiDAR. However, despite recent progress, many monocular approaches struggle with accurately defining depth boundaries, leading to less precise reconstructions.
View Article and Find Full Text PDFFront Robot AI
December 2024
School of Food Science and Environmental Health, Technological University Dublin, Dublin, Ireland.
Collaborative intelligence (CI) involves human-machine interactions and is deemed safety-critical because their reliable interactions are crucial in preventing severe injuries and environmental damage. As these applications become increasingly data-driven, the reliability of CI applications depends on the quality of data, shaping the system's ability to interpret and respond in diverse and often unpredictable environments. In this regard, it is important to adhere to data quality standards and guidelines, thus facilitating the advancement of these collaborative systems in industry.
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