Robotics and Computer-integrated Manufacturing最新文献

Dynamic control with a remote center-of-motion constraint for human–robot collaboration 基于远程运动中心约束的人机协作动态控制

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-31 DOI: 10.1016/j.rcim.2025.103060

Junchen Wang , Siqin Yang , Heng Liu , Chunheng Lu , Yu Shen

{"title":"Dynamic control with a remote center-of-motion constraint for human–robot collaboration","authors":"Junchen Wang , Siqin Yang , Heng Liu , Chunheng Lu , Yu Shen","doi":"10.1016/j.rcim.2025.103060","DOIUrl":"10.1016/j.rcim.2025.103060","url":null,"abstract":"<div><div>This paper presents a novel dynamics-based human–robot collaboration (HRC) control method with a remote center-of-motion (RCM) constraint. The existing works rely on prescribed main task trajectories and regard the RCM constraint as a secondary task, making them inapplicable in the fully interactive mode under HRC. Our work imposes a virtual RCM constraint on the interactive HRC process so that the robot’s motion conforms to human intentions while keeping the robot’s end-effector shaft always passing through a fixed (RCM) point. In our approach, the task coordinates of the RCM constraint and its Jacobian matrix are formulated, and a task control law with a computed torque controller is proposed to guarantee the convergence of the RCM error. In the null space of the RCM constraint, a mass-damping impedance control law is used to make the robot motion conform to human interactions. To address the uncertainties of both the dynamic model and external interactions of the robot, a nonlinear disturbance observer is employed to estimate the lumped disturbance projected to the task space of the RCM for steady error elimination. We also show that the robot RCM task approaches a singularity as the RCM error approaches zero. A least-squares damping inversion method is used to map the task-space motion to the joint space near the singularity. Experiments are performed to validate the effectiveness of our method, and the results show that the maximum RCM error is less than 0.85 mm during fast HRC interactions and converges to zero when the interactions cease.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103060"},"PeriodicalIF":9.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178269","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}

引用次数: 0

A novel open and efficient robot development framework based on data distribution service orchestration for agile manufacturing 面向敏捷制造的基于数据分布服务编排的新型开放高效机器人开发框架

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-29 DOI: 10.1016/j.rcim.2025.103067

Le Qi , Xiaogang Zhang , Haoran Tan , Hua Chen , Yaonan Wang

{"title":"A novel open and efficient robot development framework based on data distribution service orchestration for agile manufacturing","authors":"Le Qi , Xiaogang Zhang , Haoran Tan , Hua Chen , Yaonan Wang","doi":"10.1016/j.rcim.2025.103067","DOIUrl":"10.1016/j.rcim.2025.103067","url":null,"abstract":"<div><div>To address the challenges of openness and development efficiency in heterogeneous robotic systems for agile manufacturing, this paper proposes a novel development framework leveraging Data Distribution Service (DDS) orchestration. This framework is designed to enhance system openness, streamline development, and ensure high-performance communication critical for manufacturing agility. Firstly, the paper proposes an orchestration-driven, service-oriented architecture with a robust foundation for efficient development and integration. To further enable intuitive and efficient development, a visual modeling description method based on data flow and workflow paradigms is proposed, facilitating the online orchestration of DDS services. Additionally, a model-driven, contract-based approach for dynamic proxy service construction has been developed, allowing DDS services to be exposed as standardized Web Services, which significantly improves system openness within broader manufacturing networks. Finally, a design method for the orchestration execution engine based on Apache Camel is proposed to support the dynamic execution of DDS orchestration and proxy tasks. Comparative experiments with Robot Operating System 2 (ROS2) validate the proposed framework’s significant advantages in terms of openness, development flexibility, and efficiency, demonstrating its suitability for agile manufacturing scenarios.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103067"},"PeriodicalIF":9.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167829","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}

引用次数: 0

Joint optimization of production scheduling and worker allocation under a resource dedication policy in aircraft assembly lines 资源分配政策下飞机装配线生产调度与工人分配的联合优化

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-24 DOI: 10.1016/j.rcim.2025.103047

Xiong Zheng , Fei Qiao , Shiqi You , Xi Vincent Wang , Lihui Wang , Junkai Wang

{"title":"Joint optimization of production scheduling and worker allocation under a resource dedication policy in aircraft assembly lines","authors":"Xiong Zheng , Fei Qiao , Shiqi You , Xi Vincent Wang , Lihui Wang , Junkai Wang","doi":"10.1016/j.rcim.2025.103047","DOIUrl":"10.1016/j.rcim.2025.103047","url":null,"abstract":"<div><div>Worker resources are crucial in aircraft final assembly lines (AFAL), which are characterized by extensive manual assembly tasks. The features of AFAL, including resource constraints, makespan balancing, and flexibility in resource allocation, present greater challenges than conventional scheduling problems. This paper addresses the joint optimization problem of worker allocation under a resource dedication policy and scheduling of multi-mode tasks in the AFAL. Bi-objective with lexicographic order of minimizing the cycle time and total worker investment is considered, and an integer programming model is developed to formulate this problem. We propose a resource reallocation embedded genetic algorithm (RReGA) to solve this optimization challenge effectively. Initially, hybrid dispatch rules (HDRs) are employed to estimate the resource-makespan mapping of each workstation, yielding a high-quality initial resource allocation solution. Leveraging these mappings, a resource reallocation method, composed of a resource transfer strategy and a resource recovery strategy, is embedded in the evolutionary process of the genetic algorithm (GA) searching for scheduling solutions at the workstation. The resource transfer strategy is responsible for dynamic resource transfer across workstations, following a novel transfer principle to optimize the cycle time; while the resource recovery strategy aims to meet makespan constraints with the fewest workers to minimize cost. The efficacy and superior performance of the proposed algorithm are validated through comprehensive comparison and ablation experiments, as well as an unbalanced case study.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103047"},"PeriodicalIF":9.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123675","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}

引用次数: 0

A parallel network model: Intelligent monitoring of tool wear under variable working conditions 一种并行网络模型：变工况下刀具磨损的智能监测

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-24 DOI: 10.1016/j.rcim.2025.103065

Jiacheng Sun , Zhenyu Liu , Dong Wang , Chan Qiu , Hui Liu , Kun Huang , Jianrong Tan

{"title":"A parallel network model: Intelligent monitoring of tool wear under variable working conditions","authors":"Jiacheng Sun , Zhenyu Liu , Dong Wang , Chan Qiu , Hui Liu , Kun Huang , Jianrong Tan","doi":"10.1016/j.rcim.2025.103065","DOIUrl":"10.1016/j.rcim.2025.103065","url":null,"abstract":"<div><div>Accurate monitoring of tool wear states and wear values is crucial for reducing machine tool failures and ensuring machining accuracy and efficiency. However, wear monitoring faces significant challenges due to the imbalance of wear samples and the dynamic changes in the coupling relationships among multi-source sensing signals. Additionally, varying processing conditions further complicate the accurate tracking of wear. To address these challenges, an evolutionary spatio-temporal parallel network model is proposed. The model first employs a cyclic consistency classification enhancement network to accurately identify the real-time wear state of the tool. Then, it utilizes a parallel network to uncover the spatio-temporal coupling relationships within multi-source sensing data. Based on this, an evolutionary monitoring mechanism drives the continuous evolution and update of the model, adapting to real-time wear state and working condition changes, thus achieving precise tool wear monitoring under variable working conditions. Our self-built grinding wheel wear dataset and PHM2010 milling public dataset are used to verify the effectiveness of the method. Experimental results demonstrate that the proposed method improves prediction accuracy by 55.85 %, 10.26 %, and 50.14 % over existing methods on the C1, C4, and C6 datasets of PHM2010, respectively, while achieving a remarkable accuracy advantage of over 96.63 % in grinding wheel wear prediction.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103065"},"PeriodicalIF":9.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123676","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}

引用次数: 0

Robot assistance primitives with force-field guidance for shared task collaboration 为共享任务协作提供力场引导的机器人辅助原语

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-23 DOI: 10.1016/j.rcim.2025.103061

Sophokles Ktistakis , Lucas Gimeno , Fatima-Zahra Laftissi , Alexis Hoss , Antonio De Donno , Mirko Meboldt

{"title":"Robot assistance primitives with force-field guidance for shared task collaboration","authors":"Sophokles Ktistakis , Lucas Gimeno , Fatima-Zahra Laftissi , Alexis Hoss , Antonio De Donno , Mirko Meboldt","doi":"10.1016/j.rcim.2025.103061","DOIUrl":"10.1016/j.rcim.2025.103061","url":null,"abstract":"<div><div>This paper proposes a novel framework for human-robot collaboration (HRC) that addresses the critical need for robots to effectively collaborate with humans on shared tasks within unstructured and dynamic environments. While prior research focused on safety-related aspects, such as collision avoidance in shared workspaces, the task-oriented aspects of human-robot collaboration remain largely underexplored. To address this gap, our framework introduces Robot Assistance Primitives (RAPs). These low-level robot actions integrate both safety and task-related behaviours, enabling the robot to function as a collaborative \"third hand\", and provide assistance across the full spectrum of both physical and contactless interactions. A key component of our approach is an extension of impedance control with virtual force fields, which unifies task-related interactions and safety-related aspects within a single control scheme. The framework leverages a state-of-the-art visual perception pipeline that constructs and tracks real-time 3D digital representations of the workspace and the human operator. Additionally, an Augmented Reality Head-Mounted Display (AR-HMD) facilitates multimodal task programming through user gaze, gestures, and speech, as well as providing visual feedback to foster trust during interactions. We validate the feasibility of the proposed framework and conduct a user study to further evaluate user interactions in a collaborative soldering and assembly task. This research not only addresses limitations of current HRC frameworks but also paves the way for exploring novel collaborative applications.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103061"},"PeriodicalIF":9.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116845","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}

引用次数: 0

The collaboration scale: A novel approach for assessing robotic systems collaboration capabilities 协作规模：一种评估机器人系统协作能力的新方法

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-19 DOI: 10.1016/j.rcim.2025.103062

Federico Barravecchia, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini

{"title":"The collaboration scale: A novel approach for assessing robotic systems collaboration capabilities","authors":"Federico Barravecchia, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini","doi":"10.1016/j.rcim.2025.103062","DOIUrl":"10.1016/j.rcim.2025.103062","url":null,"abstract":"<div><div>In the transformative landscape of Industry 4.0 and the impending transition to Industry 5.0, the paradigm of collaborative robotics is emerging as a cornerstone, combining human and robotic distinctive abilities. This intersection is leading to a new era of 'human-centric' manufacturing, where the integration of human with robots is not just an option, but a need. In particular, the shift towards Industry 5.0 highlights the return of the human element to technological processes, emphasising adaptability, customization, and collaboration between humans and machines.</div><div>In this context, this study introduces the <em>Collaboration Scale,</em> a metric designed to evaluate the collaborative capabilities of robotic systems within this human-centred framework. This scale provides clear levels of collaboration across five foundational dimensions: <em>Situation awareness, Adaptivity, Communication, Learning</em>, and <em>Mobility</em>.</div><div>The proposed scale has three objectives: (i) establishing a common language for practitioners and researchers, (ii) promoting innovation and standardisation in collaborative robotics, and (iii) providing a practical tool for assessing and comparing the collaborative capabilities of different systems.</div><div>The framework aims to bridge the gap between current capabilities and future aspirations in robotics, while also promoting a human-centric approach for Industry 5.0.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103062"},"PeriodicalIF":9.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084529","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}

引用次数: 0

Enhancing accuracy in Mobile Manipulators: Challenges, current solutions and future needs 提高移动机械手的准确性：挑战，当前解决方案和未来需求

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-16 DOI: 10.1016/j.rcim.2025.103041

Naroa Núñez-Calvo , Gorka Sorrosal , Itziar Cabanes , Aitziber Mancisidor , Jorge Rodríguez-Guerra

引用次数: 0

Machine learning and deep learning applications in the automotive manufacturing industry: A systematic literature review and industry insights

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-13 DOI: 10.1016/j.rcim.2025.103034

Soufiane Douimia , Abdelghani Bekrar , Abdessamad Ait El Cadi , Yassin El Hillali , David Fillon

{"title":"Machine learning and deep learning applications in the automotive manufacturing industry: A systematic literature review and industry insights","authors":"Soufiane Douimia , Abdelghani Bekrar , Abdessamad Ait El Cadi , Yassin El Hillali , David Fillon","doi":"10.1016/j.rcim.2025.103034","DOIUrl":"10.1016/j.rcim.2025.103034","url":null,"abstract":"<div><div>In the context of the automotive manufacturing industry, complexity and the extensive data generated during production pose significant challenges. With ongoing technological advancements, effectively harnessing and analyzing this data has become increasingly critical. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to manage complexity and leverage data for enhanced decision-making and process optimization. This systematic literature review examines the application of ML and DL in automotive manufacturing, focusing on application domains, ML/DL model mapping, current trends, and effective implementation practices. Out of 2786 articles, 257 were analyzed, revealing key research areas: equipment optimization (31%), quality enhancement (26%), supply chain optimization (21%), and production efficiency (17%). Energy management was notably underrepresented (4%), indicating a significant opportunity for advancing energy efficiency and decarbonization efforts. Additionally, the review highlighted significant challenges in data management, including data quality, integration, and interoperability issues, which critically affect the successful deployment of ML and DL technologies. Insights from the review were shared with senior management at Toyota Motor Manufacturing France, aligning closely with their strategic vision for digital transformation. Successful implementation of ML and DL hinges on three essential pillars: standardization of manufacturing processes and data, robust IoT and big data infrastructure, and comprehensive human resource development. Embracing these pillars is crucial to navigating complexity, realizing AI’s full potential, and advancing efficiency, sustainability, and innovation in automotive manufacturing.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103034"},"PeriodicalIF":9.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943072","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}

引用次数: 0

Point-driven toolpath curve and orientation smoothing in robotic belt grinding for turbine blade 汽轮机叶片机器人带磨削中点驱动的刀路曲线及方向平滑

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-09 DOI: 10.1016/j.rcim.2025.103046

Ziling Wang, Lai Zou, Jiantao Li, Junjie Zhang, Wenxi Wang

{"title":"Point-driven toolpath curve and orientation smoothing in robotic belt grinding for turbine blade","authors":"Ziling Wang, Lai Zou, Jiantao Li, Junjie Zhang, Wenxi Wang","doi":"10.1016/j.rcim.2025.103046","DOIUrl":"10.1016/j.rcim.2025.103046","url":null,"abstract":"<div><div>The presence of noise or other abnormal points in the measured point clouds of the turbine blade can result in local discontinuities of the tool path curves, fitted by the machining path points generated by slicing the measured point cloud. In addition, the fluctuation exists in the tool orientation vectors corresponding to the cutter-contact (CC) points in the toolpath curves. These issues can lead to poor smoothing of robotic motion during the grinding process, thereby affecting the quality of the blade grinding. To overcome the above problems, a novel toolpath smoothing method for the measured point cloud model of the turbine blade is proposed. In this method, the initial path points are firstly generated by slicing point clouds of the turbine blade. Next spline segments replace the abnormal points in the initial path points and obtain the smooth toolpath curves. Then, for the initial tool orientation vectors distributed at the smoothed toolpath curves, an objective function by considering the directional deviation between tool orientation vectors and slicing planes is established to reduce the macro fluctuation of these vectors. Based on this, another objective function is established to filter out micro fluctuation among these vectors by considering the energy model of the motion surface of the grinding tool. The surface contour smoothness of the blade with the proposed method is improved by over 20 % compared to other toolpath planning methods.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103046"},"PeriodicalIF":9.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924145","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}

引用次数: 0

Enhancing trajectory tracking accuracy of industrial robots through temporal–spatial mapping and multi-measurement alignment 通过时空映射和多测量对准提高工业机器人轨迹跟踪精度

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-08 DOI: 10.1016/j.rcim.2025.103039

Chengzhi Wang , Tianjiao Zheng , Tian Xu , Shize Zhao , Ziyuan Yang , Sikai Zhao , Hegao Cai , Jie Zhao , Yanhe Zhu

{"title":"Enhancing trajectory tracking accuracy of industrial robots through temporal–spatial mapping and multi-measurement alignment","authors":"Chengzhi Wang , Tianjiao Zheng , Tian Xu , Shize Zhao , Ziyuan Yang , Sikai Zhao , Hegao Cai , Jie Zhao , Yanhe Zhu","doi":"10.1016/j.rcim.2025.103039","DOIUrl":"10.1016/j.rcim.2025.103039","url":null,"abstract":"<div><div>Robotic machining and automatic offline programming has been developing rapidly over the last decade, yet the absolute accuracy of industrial robots significantly impacts the processing performance, limiting their application in high-precision manufacturing fields. For dynamic non-contact continuous robotic machining tasks, such as laser cutting, precise trajectory tracking performance is especially critical. In this paper, a parallel tracking error compensation framework is proposed to improve the tracking performance of industrial robots, based on temporal–spatial mapping and multi-measurement alignment (TSM-MMA). Major tracking errors originate from nonlinear motor control lag and non-geometric motion transmission errors. The proposed method incorporates both servomotor encoder feedback and laser tracker measurements, enabling parallel distinction and compensation of these errors across trials. Typical linear and circular trajectories are analyzed using TSM to normalize multi-sensor data. Gaussian process regression (GPR) is employed in the MMA process to model the regularity of repetitive measurements, facilitating targeted error compensation. Physical experiments are conducted with an EFORT ER14-1400 robot and a Leica AT960 laser tracker to validate the effectiveness of the proposed framework.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103039"},"PeriodicalIF":9.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916894","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}

引用次数: 0