Robotics and Computer-integrated Manufacturing最新文献

{"title":"A Physical-simulation synergy approach for high-uniformity robotic gluing","authors":"Zhaoyang Liao ,&nbsp;Shufei Li ,&nbsp;Fengyuan Xie ,&nbsp;Guilin Yang ,&nbsp;Xubin Lin ,&nbsp;Zhihao Xu ,&nbsp;Xuefeng Zhou","doi":"10.1016/j.rcim.2025.102961","DOIUrl":"10.1016/j.rcim.2025.102961","url":null,"abstract":"<div><div>Traditional robotic gluing techniques suffer from uneven adhesive distribution and low coverage rates, particularly on complex surfaces and under varying process parameters, which impede their application in smart manufacturing. To overcome these limitations, this work presents a physical-simulation synergy approach for predicting glue line dimensions and optimizing toolpath planning, aimed at improving gluing quality and production efficiency. A predictive model is developed in the simulation layer using the Whale Optimization Algorithm combined with Gaussian Process Regression to accurately capture the nonlinear relationships between key process parameters and glue line dimensions. Building on this, a surrogate model is introduced to simulate glue line distribution after compression. To ensure full coverage and high uniformity, a high-uniformity toolpath planning strategy is implemented, utilizing growth-based Hilbert curves and conformal mapping to generate efficient gluing toolpaths on complex surfaces in physical environments. Experimental results validate the effectiveness of the proposed method in accurately predicting glue dimensions, enhancing coverage, and improving adhesive performance, demonstrating its suitability for applications involving complex surface geometries.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102961"},"PeriodicalIF":9.1,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic multi-tour order picking in an automotive-part warehouse based on attention-aware deep reinforcement learning","authors":"Xiaohan Wang ,&nbsp;Lin Zhang ,&nbsp;Lihui Wang ,&nbsp;Enrique Ruiz Zuñiga ,&nbsp;Xi Vincent Wang ,&nbsp;Erik Flores-García","doi":"10.1016/j.rcim.2025.102959","DOIUrl":"10.1016/j.rcim.2025.102959","url":null,"abstract":"<div><div>Dynamic order picking has usually demonstrated significant impacts on production efficiency in warehouse management. In the context of an automotive-part warehouse, this paper addresses a dynamic multi-tour order-picking problem based on a novel attention-aware deep reinforcement learning-based (ADRL) method. The multi-tour represents that one order-picking task must be split into multiple tours due to the cart capacity and the operator’s workload constraints. First, the multi-tour order-picking problem is formulated as a mathematical model, and then reformulated as a Markov decision process. Second, a novel DRL-based method is proposed to solve it effectively. Compared to the existing DRL-based methods, this approach employs multi-head attention to perceive warehouse situations. Additionally, three improvements are proposed to further strengthen the solution quality and generalization, including (1) the extra location representation to align the batch length during training, (2) the dynamic decoding to integrate real-time information of the warehouse environment during inference, and (3) the proximal policy optimization with entropy bonus to facilitate action exploration during training. Finally, comparison experiments based on thousands of order-picking instances from the Swedish warehouse validated that the proposed ADRL could outperform the other twelve DRL-based methods at most by 40.6%, considering the optimization objective. Furthermore, the performance gap between ADRL and seven evolutionary algorithms is controlled within 3%, while ADRL can be hundreds or thousands of times faster than these EAs regarding the solving speed.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102959"},"PeriodicalIF":9.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly detection for high-speed machining using hybrid regularized support vector data description","authors":"Zhipeng Ma ,&nbsp;Ming Zhao ,&nbsp;Xuebin Dai ,&nbsp;Yang Chen","doi":"10.1016/j.rcim.2025.102962","DOIUrl":"10.1016/j.rcim.2025.102962","url":null,"abstract":"<div><div>Process monitoring in high-speed machining (HSM) is essential to guarantee product quality and improve manufacturing efficiency. Nevertheless, the data acquired from practical machining processes are completely unlabeled and severely unbalanced, which may be seriously insufficient to support deep learning-based anomaly detection. Furthermore, the collected signals are inevitably contaminated by environmental noises and uncertain factors. How to remove these disturbances according to data distribution characteristics remains a challenging issue. To tackle these limitations, a novel interpretable machine learning approach, called hybrid regularized support vector data description (H-SVDD), is proposed for unsupervised anomaly detection during HSM. In this work, an adaptive local kernel density estimate is first constructed to eliminate outlier interferences, and assigns interpretable weights to optimize the SVDD for improving detection accuracy. Subsequently, by introducing the <em>l_p</em>-norm penalty mechanism, a generalized probability density regularized SVDD is innovatively designed to enhance the descriptive capability for complex machining processes. Finally, a hyperparameter tuning strategy based on Bayesian optimization is developed to improve generalizability and stability. The data collected from CNC machines are used to verify the superiority of the proposed method. Experimental results show that the proposed H-SVDD has higher detection accuracy than current SVDD methods and eliminates false alarms caused by noise interferences. This work may provide a useful solution for independently perceiving the health conditions of HSM.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102962"},"PeriodicalIF":9.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Vision robot online packing platform for deep reinforcement learning","authors":"Xingyu Mu ,&nbsp;Quanmin Kan ,&nbsp;Yong Jiang ,&nbsp;Chao Chang ,&nbsp;Xincheng Tian ,&nbsp;Lelai Zhou ,&nbsp;Yongguo Zhao","doi":"10.1016/j.rcim.2024.102941","DOIUrl":"10.1016/j.rcim.2024.102941","url":null,"abstract":"<div><div>In modern logistics and manufacturing, online mixed palletizing stands as one of the key automation technologies, facing challenges brought by the diversity of packages and real-time demand. However, traditional palletizing methods typically rely on preset rules, making them ill-suited to handle diverse bins in dynamic, real-time environments. This limitation becomes especially pronounced when dealing with complex palletizing tasks. To optimize the accuracy and operational efficiency of the palletizing process, this study, based on 3D vision technology and deep reinforcement learning techniques, designs a bin positioning algorithm utilizing projected bounding boxes. Additionally, spatial rotation position encoding is integrated into the decision-making process of the online palletizing network, designing a deep reinforcement learning algorithm for online mixed palletizing based on a masked attention mechanism. The paper also introduces a novel heuristic method—Boundary Point, which updates the palletizing state chain using key-point heuristics and “spatial” heuristics, and employs a pointer network for tail node selection. Experimental results demonstrate that the proposed method significantly improves average space utilization across the RS, CUT1, and CUT2 datasets. Finally, a 3D vision-based robotic online mixed palletizing experimental platform is designed and built, proving the effectiveness and application potential of the proposed algorithm.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102941"},"PeriodicalIF":9.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration and calibration of an in situ robotic manufacturing system for high-precision machining of large-span spacecraft brackets with associated datum","authors":"Yan Zheng,&nbsp;Wei Liu,&nbsp;Yang Zhang,&nbsp;Lei Han,&nbsp;Junqing Li,&nbsp;Yongkang Lu","doi":"10.1016/j.rcim.2024.102928","DOIUrl":"10.1016/j.rcim.2024.102928","url":null,"abstract":"<div><div>In this research, a robotic in situ manufacturing system based on the integration of measurement and machining technology is developed to address the challenge of achieving high precision and efficiency in the manufacturing of spacecraft brackets in large-scale scenarios. First, a robotic in-situ manufacturing system is established, that integrates globally unified measurement data, high-precision conversion of machining datums, and closed-loop control of end machining processes. A multiparameter optimal fitting calibration method is subsequently employed to calibrate several key parameters of the end-effector within the integrated measurement and machining process, ensuring the initial geometric accuracy of the manufacturing system. The experimental results indicate that within a 2-meter range, the average absolute error is 0.024 mm, with both the standard deviation and root mean square error not exceeding 0.038 mm. The overall system has an average error of 0.083 mm and a maximum error of 0.096 mm. Additionally, experiments are conducted in a laboratory setting simulating the manufacturing of large-span datum-associated spacecraft brackets, validating the high precision and effectiveness of the in situ robotic manufacturing system.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102928"},"PeriodicalIF":9.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KineNN: Kinematic Neural Network for inverse model policy based on homogeneous transformation matrix and dual quaternion","authors":"Mochammad Rizky Diprasetya ,&nbsp;Johannes Pöppelbaum ,&nbsp;Andreas Schwung","doi":"10.1016/j.rcim.2024.102945","DOIUrl":"10.1016/j.rcim.2024.102945","url":null,"abstract":"<div><div>The modeling and control of a robot manipulator can be challenging considering different robot architectures and different tasks. In this paper, we introduce a novel framework for data based control of robot operating tasks using a novel, invertible neural network called Kinematic Neural Network (KineNN). To this end, we present two KineNN architectures based on the Rigid Body Transformation in the form of either the Homogeneous Transformation Matrix (HTM) or Dual Quaternion (DQ). The KineNN serves two purposes in our approach. First, it acts as the forward kinematic model of a robot within a model based reinforcement learning architecture where the output is the end effector position and orientation of the robot manipulator with given joint angles of the robot. Second, KineNN’s inverted architecture is used within the policy network making the policy network aware of the actual robot architecture, which allows for an disentanglement of robot kinematics and task specific control resulting in improved training performance. Within the approach both policy and model NN share their parameters. The proposed framework was tested and evaluated on a Universal Robot (UR) 5. The results show that the architecture can successfully capture the robot kinematics and predict the world model state. The inverse model with shared parameters within the policy network outperforms a training without this sharing. We further conduct a transfer learning where we modify the arm lengths and number of joints. In this experiment, transferring KineNNs parameters yielded faster convergence in comparison to re-training a model from scratch.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102945"},"PeriodicalIF":9.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety-efficiency integrated assembly: The next-stage adaptive task allocation and planning framework for human–robot collaboration","authors":"Ruihan Zhao ,&nbsp;Sichen Tao ,&nbsp;Pengzhong Li","doi":"10.1016/j.rcim.2024.102942","DOIUrl":"10.1016/j.rcim.2024.102942","url":null,"abstract":"<div><div>Human–robot collaboration (HRC) has sparked a new wave of the resilient, sustainable, and human-centric industrial revolution. In HRC-enabled manufacturing, safety is a constant focus since the traditional physical barriers between robots and humans are removed. Meanwhile, ensuring efficiency has also gained considerable attention with the rise in production demands and market competition. However, the integration and balance between these two critical prerequisites are overlooked in the task allocation and planning phase of the HRC assembly. In this paper, an adaptive task allocation and planning framework is proposed by simultaneously considering the safety and efficiency of HRC assembly. First, an execution time optimization strategy (ETO) is presented. This strategy adaptively adjusts safety paradigms in the spatio-temporal shared workspace. Thereby, the execution time of assembly tasks is well optimized while ensuring human–robot safety. Second, based on ETO and collaborative assembly task requirements, the safety-efficiency integrated assembly planning problem (SEAPP) is proposed and modeled. Third, compared to conventional single-objective HRC assembly planning problems, the solution space of SEAPP is significantly expanded, leading to a substantial increase in optimization complexity. Thus, a novel constrained multi-objective co-evolutionary algorithm, called GDCA, is proposed. By combining the complementary advantages of different evolutionary operators, GDCA enhances the diversity of solutions while ensuring convergence of optimization process. In comparison with variants that rely solely on a single evolutionary strategy, GDCA maintains better and more stable performance, validating the effectiveness of the co-evolutionary process. GDCA is also compared with four state-of-the-art constrained multi-objective optimization algorithms across a variety of SEAPP instances, widely spanning different task scales and durations. Across 20 assembly instances, GDCA shows superior <span><math><mrow><mi>I</mi><mspace></mspace><mi>G</mi><mspace></mspace><mi>D</mi></mrow></math></span> over NSGA-II, PPS, BiCo, and MCCMO in 17, 19, 18, and 17 instances, and better <span><math><mrow><mi>H</mi><mspace></mspace><mi>V</mi></mrow></math></span> in 17, 19, 17, and 17 instances, respectively. Furthermore, the feasibility and effectiveness of the proposed SEAPF are validated by an industrial challenge in the HRC computer assembly. While ensuring safety, it significantly reduces the total assembly completion time, achieving a 6.2% time reduction over the SSM paradigm and 47.8% over the PFL paradigm, respectively.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102942"},"PeriodicalIF":9.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twin-based architecture for wire arc additive manufacturing using OPC UA","authors":"Mohammad Mahruf Mahdi ,&nbsp;Mahdi Sadeqi Bajestani ,&nbsp;Sang Do Noh ,&nbsp;Duck Bong Kim","doi":"10.1016/j.rcim.2024.102944","DOIUrl":"10.1016/j.rcim.2024.102944","url":null,"abstract":"<div><div>This paper presents a digital twin (DT)-based architecture for wire arc additive manufacturing (WAAM) utilizing Open Platform Communications Unified Architecture (OPC UA) for enhanced communication, security, and real-time control. DT is explored at both enterprise management and individual asset scales, providing a comprehensive framework for process optimization. The proposed architecture integrates advanced 3D visualization, real-time defect prediction using convolutional neural networks (CNNs), and structured data management. A practical case study involving a 6-degree-of-freedom (DOF) industrial robotic arm demonstrates the application of the architecture in a WAAM deposition scenario. The architecture's effectiveness is evaluated, focusing on anomaly detection, joint angle accuracy, and communication reliability, highlighting the integration of computer vision and cloud-based data storage. The results indicate significant improvements in defect detection, process monitoring, and real-time interaction between the physical entity and the DT, underscoring the potential of the proposed DT architecture.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102944"},"PeriodicalIF":9.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven modeling and integrated optimization of machining quality and energy consumption for internal gear power honing process","authors":"You Zhang ,&nbsp;Congbo Li ,&nbsp;Ying Tang ,&nbsp;Huajun Cao ,&nbsp;Guibao Tao","doi":"10.1016/j.rcim.2024.102943","DOIUrl":"10.1016/j.rcim.2024.102943","url":null,"abstract":"<div><div>The internal gear power honing process is increasingly used in the gear machining of electric vehicles due to superior tooth surface quality. Most of the existing work only investigates the quality improvement of gear machining processes, and focuses little attention on energy saving. However, the total rated power of multi-axis motion for gear honing process reaches 60 kW, which has great energy-saving potential. To this end, this article proposes a data-driven modeling and integrated optimization method of machining quality and energy consumption for internal gear power honing process. The machining quality formation mechanism and energy consumption characteristics of gear honing process are first analyzed. A gradient-enhanced Kriging (GEK) method is then used to establish data-driven tooth profile form deviation model and energy consumption model. Furthermore, an integrated honing process optimization model considering tooth profile form deviation and energy consumption is constructed. An improved multi-objective coati optimization algorithm (IMOCOA) is used to solve the optimization problem. The experimental results show that the R-square of the GEK model reaches 0.99, which has superior modeling accuracy compared with other methods. The optimization results demonstrate that compared with the empirical scheme, the proposed integrated optimization model reduces the tooth profile form deviation and energy consumption by 38.46 % and 10.26 %, respectively. Moreover, the developed IMOCOA also presents competitive algorithm performance. The proposed integrated optimization scheme significantly balances honing machining quality and energy consumption.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"93 ","pages":"Article 102943"},"PeriodicalIF":9.1,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviewing human-robot collaboration in manufacturing: Opportunities and challenges in the context of industry 5.0","authors":"Mandeep Dhanda,&nbsp;Benedict Alexander Rogers,&nbsp;Stephanie Hall,&nbsp;Elies Dekoninck,&nbsp;Vimal Dhokia","doi":"10.1016/j.rcim.2024.102937","DOIUrl":"10.1016/j.rcim.2024.102937","url":null,"abstract":"<div><div>Industry 4.0 (I4.0) has been characterized by the increasing use of automation, artificial intelligence, and big data in manufacturing. It has brought different machines, tools, robots and devices together through integration with cyber-physical systems as well as Internet of Things and computer systems. This has dramatically improved efficiency, productivity, and flexibility of automated systems, but it has also raised concerns about the impact of automation on jobs, the ethical considerations and the future of work in general. Industry 5.0 (I5.0) is the next manufacturing paradigm evolution and builds on I4.0 with the addition of ‘people’, in which robots will be designed to work alongside humans in a safe and efficient manner. Human-robot collaboration (HRC) is its key enabler. In manufacturing, HRC has the potential to improve safety, efficiency, and productivity by allowing humans to focus on tasks that require creativity, judgment, and flexibility, while robots perform more repetitive and dangerous tasks.</div><div>This paper explores the concept of HRC and its advancement within 21st century industry. It identifies the opportunities and challenges arising from the interactions between robots and humans in manufacturing applications, assembly, and inspection. It also highlights the significance of HRC in I4.0 and its potential in I5.0. In addition, the role of artificial intelligence, machine learning, large language models, information modelling (ontologies) and new emerging digital technologies (augmented reality, virtual reality, digital twins, cyber-physical system) in the development of HRC and I5.0 is documented and discussed adding new perspectives to the growing literature in this area.</div><div>This investigation sheds light on the emerging paradigms that have come about as parts of I5.0 and the transformative role of human-robot interaction in shaping the future of manufacturing. This critical review provides a realistic picture of manufacturing automation and the benefits and weaknesses of current HRC systems. It presents a researched view on the concept, needs, enabling technologies and system frameworks of human-robot interaction in manufacturing, providing a practical vision and research agenda for future work in this area and its associated systems.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"93 ","pages":"Article 102937"},"PeriodicalIF":9.1,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}