Article
Chemistry, Multidisciplinary
Niki Kousi, Christos Gkournelos, Sotiris Aivaliotis, Konstantinos Lotsaris, Angelos Christos Bavelos, Panagiotis Baris, George Michalos, Sotiris Makris
Summary: This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems, which dynamically updates the digital twin model and combines with artificial intelligence logic to derive alternative configurations of the production system. The application of this approach in the automotive industry is illustrated through a case study.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
Tie Zhang, Hanlei Sun, Yanbiao Zou
Summary: With the development of manufacturing industry towards personalization and flexibility, a human-robot collaboration system based on Electromyography (EMG) signals is proposed to enable the robot to understand human motion intention and play the guidance role of human tutor. The system improves the recognition accuracy of motion intention through modifying the Fast Orthogonal Search (FOS) algorithm and introduces evaluation methods from the perspective of ergonomics to balance tracking accuracy and collaboration comfort. The end-to-end collaborative control model based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm effectively saves physical power for the human tutor and exhibits good coordination between accurate tracking and comfortable collaboration.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Engineering, Industrial
Qibing Lv, Rong Zhang, Xuemin Sun, Yuqian Lu, Jinsong Bao
Summary: In response to the increasing demand for medical equipment production due to COVID-19, a new framework of human-robot collaborative assembly based on digital twin is proposed in this paper. By integrating data from digital twin spaces and using optimization models, the proposed framework improves assembly efficiency and safety.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Robotics
Martijn Cramer, Karel Kellens, Eric Demeester
Summary: In the era of mass customization in the manufacturing industry, the collaboration between humans and robots can help cope with increasing product diversity and fluctuating demands. To achieve natural collaboration, the robot needs to estimate the operator's assembly intentions and act accordingly.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Engineering, Manufacturing
Wenjun Xu, Siqi Feng, Bitao Yao, Zhenrui Ji, Zhihao Liu
Summary: This paper proposes an early turn-taking prediction method in HRC assembly tasks using spiking neural networks, which improves the efficiency of human-robot turn-taking recognition under human uncertainty.
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
(2023)
Article
Computer Science, Artificial Intelligence
Yaqian Zhang, Kai Ding, Jizhuang Hui, Jingxiang Lv, Xueliang Zhou, Pai Zheng
Summary: This paper proposes a human-object integrated approach for context-aware assembly intention recognition in human-robot collaborative (HRC) assembly. By integrating assembly action recognition and assembly part recognition, the accuracy of operator's intention recognition is improved. Experimental results show the feasibility and effectiveness of the proposed approach in accurately recognizing operator's intentions in complex and flexible assembly environments.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Chemistry, Analytical
Tsubasa Maruyama, Toshio Ueshiba, Mitsunori Tada, Haruki Toda, Yui Endo, Yukiyasu Domae, Yoshihiro Nakabo, Tatsuro Mori, Kazutsugu Suita
Summary: A digital twin-driven human-robot collaboration system was developed in this study, utilizing digital human technology for measuring and simulating worker motions and physical loads. The system demonstrated effectiveness in work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment.
Article
Engineering, Manufacturing
Zhang Ye, Luo Jingyu, Yang Hongwei
Summary: This study proposes an efficient, flexible, and adaptive approach using Digital Twin in a human-robot collaborative system to improve the efficiency and flexibility of the assembly process for complex-shaped architectures.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
(2022)
Article
Automation & Control Systems
Yi Sun, Weitian Wang, Yi Chen, Yunyi Jia
Summary: This article proposes a dual-input deep learning approach to assist humans in assembly tasks and utilizes online automated data labeling to reduce the training efforts.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Industrial
Xin Ma, Qinglin Qi, Jiangfeng Cheng, Fei Tao
Summary: Human-robot collaboration has broad applications and digital twin plays a critical role in enhancing physical-virtual interaction. However, there are challenges in the dynamic implementation of digital twin, such as building a model and maintaining consistency.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Regina Kyung-Jin Lee, Hao Zheng, Yuqian Lu
Summary: Future manufacturing will witness a shift towards collaboration and compassion in human-robot relationships. To enable seamless knowledge transfer, a unified knowledge representation system that can be shared by humans and robots is essential. The Human-Robot Shared Assembly Taxonomy (HR-SAT) proposed in this study allows comprehensive assembly tasks to be represented as a knowledge graph that is understandable by both humans and robots. HR-SAT incorporates rich assembly information and has diverse applications in process planning, quality checking, and human-robot collaboration.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Artificial Intelligence
Manuel Mueller, Tamas Ruppert, Nasser Jazdi, Michael Weyrich
Summary: Situation awareness is crucial for collaborative robots in human-centered, dynamic environments. This paper proposes a metric to measure the state of situation awareness and adapts it to the collaborative robot domain. The effectiveness of the proposed approach is evaluated using the Robotino platform, with results compared to existing research, highlighting the advantages of this approach. Expectations are that this method will significantly improve the performance of cobots in human-robot collaboration and enhance communication and understanding between humans and machines.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Automation & Control Systems
Tianjiao An, Yuexi Wang, Guangjun Liu, Yuanchun Li, Bo Dong
Summary: This article proposes a cooperative game-based approximate optimal control method for human-robot collaboration (HRC)-oriented modular robot manipulators (MRMs). The human motion intention estimation is developed using robot position measurements based on a harmonic drive compliance model. By employing the cooperative differential game strategy, the optimal control problem of HRC-oriented MRM systems is transformed into a cooperative game problem. The experiment results demonstrate the advantage of the proposed method.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Paraskevi Th. Zacharia, Elias K. Xidias, Andreas C. Nearchou
Summary: This article discusses the assembly line balancing problem in production lines with collaborative robots. Collaborative robots have the potential to improve automation, productivity, accuracy, and flexibility in manufacturing. The article explores the use of a problem-specific metaheuristic to solve this complex problem under uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Robotics
Ramin Jaberzadeh Ansari, Yiannis Karayiannidis
Summary: This study introduces a task-based role allocation control scheme which adjusts the robot's role in cooperative manipulation of a rigid-body object by identifying tasks that better reflect human intention. By distinguishing between rotation and translation tasks and tuning a similarity factor in an optimization problem, the robot's role can be fine-tuned during task execution.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
