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2. Modelling, Design and Simulation as-a-Service Based on Extended Reality (XR) in Industry 4.0

Author

Mourtzis, Dimitris, Ong, S. K., Wang, Xi Vincent, Panopoulos, Nikos, Stark, Rainer, Wang, Lihui, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, and Tolio, Tullio, editor

Published
2024
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3. Project-Based Collaborative Research and Training Roadmap for Manufacturing Based on Industry 4.0

Author

Chodnicki, Marek, Deja, Mariusz, Vosniakos, George-Christopher, Benardos, Panorios, Wang, Lihui, Wang, Xi Vincent, Braun, Thomas, Reimann, Robert, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Silva, Francisco J. G., editor, Pereira, António B., editor, and Campilho, Raul D. S. G., editor

Published
2024
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11. Learning accurate and efficient three-finger grasp generation in clutters with an auto-annotated large-scale dataset

Author

Zhou, Zhenning, Sun, Han, Wang, Xi Vincent, Zhang, Zhinan, Cao, Qixin, Zhou, Zhenning, Sun, Han, Wang, Xi Vincent, Zhang, Zhinan, and Cao, Qixin

Abstract

With the development of intelligent manufacturing and robotic technologies, the capability of grasping unknown objects in unstructured environments is becoming more prominent for robots with extensive applications. However, current robotic three-finger grasping studies only focus on grasp generation for single objects or scattered scenes, and suffer from high time expenditure to label grasp ground truth, making them incapable of predicting grasp poses for cluttered objects or generating large-scale datasets. To address such limitations, we first introduce a novel three-finger grasp representation with fewer prediction dimensions, which balances the training difficulty and representation accuracy to obtain efficient grasp performance. Based on this representation, we develop an auto-annotation pipeline and contribute a large-scale three-finger grasp dataset (TF-Grasp Dataset). Our dataset contains 222,720 RGB-D images with over 2 billion grasp annotations in cluttered scenes. In addition, we also propose a three-finger grasp pose detection network (TF-GPD), which detects globally while fine-tuning locally to predict high-quality collision-free grasps from a single-view point cloud. In sum, our work addresses the issue of high-quality collision-free three-finger grasp generation in cluttered scenes based on the proposed pipeline. Extensive comparative experiments show that our proposed methodology outperforms previous methods and improves the grasp quality and efficiency in clutters. The superior results in real-world robot grasping experiments not only prove the reliability of our grasp model but also pave the way for practical applications of three-finger grasping. Our dataset and source code will be released., QC 20240725

Published
2025
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22. Digital Twin-Based Services and Data Visualization of Material Handling Equipment in Smart Production Logistics Environment

Author

Jeong, Yongkuk, Flores-García, Erik, Kwak, Dong Hoon, Woo, Jong Hun, Wiktorsson, Magnus, Liu, Sichao, Wang, Xi Vincent, Wang, Lihui, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Kim, Duck Young, editor, von Cieminski, Gregor, editor, and Romero, David, editor

Published
2022
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40. A Framework of Data-Driven Dynamic Optimisation for Smart Production Logistics

Author

Liu, Sichao, Wang, Lihui, Wang, Xi Vincent, Wiktorsson, Magnus, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Lalic, Bojan, editor, Majstorovic, Vidosav, editor, Marjanovic, Ugljesa, editor, von Cieminski, Gregor, editor, and Romero, David, editor

Published
2020
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41. Overview of Human-Robot Collaboration in Manufacturing

Author

Wang, Lihui, Liu, Sichao, Liu, Hongyi, Wang, Xi Vincent, Chaari, Fakher, Series Editor, Haddar, Mohamed, Series Editor, Kwon, Young W., Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Wang, Lihui, editor, Majstorovic, Vidosav D., editor, Mourtzis, Dimitris, editor, Carpanzano, Emanuele, editor, Moroni, Govanni, editor, and Galantucci, Luigi Maria, editor

Published
2020
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42. Mutual-cognition for proactive human–robot collaboration: A mixed reality-enabled visual reasoning-based method.

Author

Li, Shufei, You, Yingchao, Zheng, Pai, Wang, Xi Vincent, and Wang, Lihui

Subjects
MIXED reality, ELECTRIC vehicle batteries, ROBOT motion, DIGITAL twins, VISUAL perception, REINFORCEMENT learning, SATISFACTION
Abstract

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents' operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent's actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation. [ABSTRACT FROM AUTHOR]

Published
2024
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