Pub Date : 2025-12-24DOI: 10.1016/j.rcim.2025.103210
Zichuan Chai , Wenlei Xiao , Gang Zhao , Tianze Qiu , Yan Liu , Songyuan Xue , Oluwasheyi Oyename , Zheng Shi
The integration of AI into next-generation CAM systems has attracted significant research interest. Wherein, automatic feature recognition is a critical prerequisite before machining paths could be generated accordingly. Consequently, researchers have increasingly leveraged deep learning methodologies for geometric feature recognition from B-rep models. However, research targeting the recognition of machining features that ensure compatibility with downstream CAM toolpath generation remains limited. This paper proposes a multi-scale fusion graph neural network framework that embeds STEP-NC machining features to enhance their potency on the subsequent toolpath generation. Initially, feature semantics are extracted in accordance with the STEP-NC ISO 14649 standard, and a fusion network is constructed by integrating the adjacent-face aggregation of the GIN with the multi-head self-attention mechanism of the Graph Transformer. In the output layer, fine-grained label decomposition is performed based on standard definitions, enabling concurrent prediction of feature categories and their associated EXPRESS representations. Following pre-training, the model undergoes unsupervised fine-tuning on unlabeled real-world workpiece data to improve its generalization performance in practical manufacturing scenarios. Experimental results achieve over 85% recognition accuracy for real-part machining features in the automated manufacturing tasks.
将人工智能集成到下一代CAM系统中已经引起了极大的研究兴趣。其中，自动特征识别是加工轨迹生成的关键前提。因此，研究人员越来越多地利用深度学习方法从B-rep模型中识别几何特征。然而，针对加工特征的识别，以确保与下游凸轮刀具轨迹生成的兼容性的研究仍然有限。本文提出了一种嵌入STEP-NC加工特征的多尺度融合图神经网络框架，以增强其在后续刀具路径生成中的效力。首先，根据STEP-NC ISO 14649标准提取特征语义，并将GIN的邻接面聚合与Graph Transformer的多头自关注机制相结合，构建融合网络。在输出层中，基于标准定义执行细粒度标签分解，支持对特征类别及其相关EXPRESS表示进行并发预测。在预训练之后，该模型对未标记的真实工件数据进行无监督微调，以提高其在实际制造场景中的泛化性能。实验结果表明，在自动化制造任务中，该方法对实零件加工特征的识别准确率达到85%以上。
{"title":"Graph-based multi-scale fusion learning for STEP-NC machining feature recognition","authors":"Zichuan Chai , Wenlei Xiao , Gang Zhao , Tianze Qiu , Yan Liu , Songyuan Xue , Oluwasheyi Oyename , Zheng Shi","doi":"10.1016/j.rcim.2025.103210","DOIUrl":"10.1016/j.rcim.2025.103210","url":null,"abstract":"<div><div>The integration of AI into next-generation CAM systems has attracted significant research interest. Wherein, automatic feature recognition is a critical prerequisite before machining paths could be generated accordingly. Consequently, researchers have increasingly leveraged deep learning methodologies for geometric feature recognition from B-rep models. However, research targeting the recognition of machining features that ensure compatibility with downstream CAM toolpath generation remains limited. This paper proposes a multi-scale fusion graph neural network framework that embeds STEP-NC machining features to enhance their potency on the subsequent toolpath generation. Initially, feature semantics are extracted in accordance with the STEP-NC ISO 14649 standard, and a fusion network is constructed by integrating the adjacent-face aggregation of the GIN with the multi-head self-attention mechanism of the Graph Transformer. In the output layer, fine-grained label decomposition is performed based on standard definitions, enabling concurrent prediction of feature categories and their associated EXPRESS representations. Following pre-training, the model undergoes unsupervised fine-tuning on unlabeled real-world workpiece data to improve its generalization performance in practical manufacturing scenarios. Experimental results achieve over 85% recognition accuracy for real-part machining features in the automated manufacturing tasks.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103210"},"PeriodicalIF":11.4,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823067","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}
Pub Date : 2025-12-23DOI: 10.1016/j.rcim.2025.103213
Ruihao Kang , Junshan Hu , Zhengping Li , Liangxiang Wang , Jincheng Yang , Wei Tian
Digital Twin (DT) technology is pushing manufacturing toward higher intelligence and adaptability. However, existing DT modeling methods still rely heavily on customization, lacking universality and scalability for assembly-oriented manufacturing systems. To address this limitation, this paper proposes a modular DT control framework that couples graphical interaction with reusable functional modules. Based on the classical five-dimensional DT model, the virtual entity is refined into geometric and physical models, and the service system is expanded into behavior and task models, enabling a clearer description and direct correspondence between system structure and operational logic. A behavior-oriented modeling workflow and a data-mapping mechanism are established to enhance scenario adaptability and reduce modeling effort. A graphical DT modeling platform is developed on top of this framework. Multiple robotic manufacturing prototypes, including robotic drilling, robotic gluing, and hybrid drilling systems, are constructed to assess the generality and reconfigurability of the proposed approach. A drilling experiment is performed on the robotic drilling system to validate the DT-based control execution mechanism. The resulting holes exhibit an average positioning error of 0.23 mm and a diameter error of 0.012 mm, both meeting aerospace drilling requirements. This confirms that virtual task commands can be accurately executed on physical system under the proposed DT framework. Overall, the DT prototype implementations and drilling experiment jointly verify the scalability of the framework and its DT-based control capability, providing a practical approach for the rapid development and deployment of DT prototypes in aircraft assembly systems.
{"title":"A digital twin modeling framework with graphical software for rapid development of aircraft assembly systems","authors":"Ruihao Kang , Junshan Hu , Zhengping Li , Liangxiang Wang , Jincheng Yang , Wei Tian","doi":"10.1016/j.rcim.2025.103213","DOIUrl":"10.1016/j.rcim.2025.103213","url":null,"abstract":"<div><div>Digital Twin (DT) technology is pushing manufacturing toward higher intelligence and adaptability. However, existing DT modeling methods still rely heavily on customization, lacking universality and scalability for assembly-oriented manufacturing systems. To address this limitation, this paper proposes a modular DT control framework that couples graphical interaction with reusable functional modules. Based on the classical five-dimensional DT model, the virtual entity is refined into geometric and physical models, and the service system is expanded into behavior and task models, enabling a clearer description and direct correspondence between system structure and operational logic. A behavior-oriented modeling workflow and a data-mapping mechanism are established to enhance scenario adaptability and reduce modeling effort. A graphical DT modeling platform is developed on top of this framework. Multiple robotic manufacturing prototypes, including robotic drilling, robotic gluing, and hybrid drilling systems, are constructed to assess the generality and reconfigurability of the proposed approach. A drilling experiment is performed on the robotic drilling system to validate the DT-based control execution mechanism. The resulting holes exhibit an average positioning error of 0.23 mm and a diameter error of 0.012 mm, both meeting aerospace drilling requirements. This confirms that virtual task commands can be accurately executed on physical system under the proposed DT framework. Overall, the DT prototype implementations and drilling experiment jointly verify the scalability of the framework and its DT-based control capability, providing a practical approach for the rapid development and deployment of DT prototypes in aircraft assembly systems.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103213"},"PeriodicalIF":11.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823074","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}
Pub Date : 2025-12-22DOI: 10.1016/j.rcim.2025.103205
Quan Xiao , Congjun Ma , Xuke Zhong , Yuqi Zhu , Xingxing You , Songyi Dian
In-situ inspection and maintenance of gas-insulated switchgear (GIS) are critical for ensuring power grid security and stable operation, as it can significantly reduce the current maintenance cycles which is extensive and costly due to the GIS disassembly and cleaning. However, navigating in/out via inspection ports to perform inspection and maintenance tasks in confined environments(e.g., arc-extinguishing chambers) is fairly challenging. This study proposes a novel multi-segment extra-slender (3 segments, diameter-to-length ratio 0.04) cable-driven mobile continuum robot (CDMCR) designed to enter confined spaces, execute maintenance tasks, and perform required joint configurations. The coupling between the mobile platform and the cable-driven continuum arm introduces significant redundancy. This redundancy complicates multi-constrained motion planning and reduces computational efficiency when exploring compact unstructured environments. To address this, we developed a real-time motion planner that incorporates mechanical configuration constraints, actuator limits, obstacle avoidance, and arc-surface constraints. The planner generates coordinated base and joint motions that track smooth end-effector trajectories. This enables global path planning from arbitrary initial states in prior-known scenes. Subsequently, an improved Follow-the-Leader (FTL) algorithm, inspired by the natural movement of snakes, ensures self-collision avoidance during end-path tracking. Laboratory and field evaluations demonstrate effective workspace coverage, comprehensive visual inspection capability within high-voltage GIS compartments, and robust success in solving random 6-DOF targets with responsive computation—validating both the robotic architecture and the proposed planning framework for practical power-equipment maintenance.
{"title":"Design, modeling and motion planning of a mobile continuum robot for in-situ inspection and maintenance in gas-insulated switchgear","authors":"Quan Xiao , Congjun Ma , Xuke Zhong , Yuqi Zhu , Xingxing You , Songyi Dian","doi":"10.1016/j.rcim.2025.103205","DOIUrl":"10.1016/j.rcim.2025.103205","url":null,"abstract":"<div><div>In-situ inspection and maintenance of gas-insulated switchgear (GIS) are critical for ensuring power grid security and stable operation, as it can significantly reduce the current maintenance cycles which is extensive and costly due to the GIS disassembly and cleaning. However, navigating in/out via inspection ports to perform inspection and maintenance tasks in confined environments(e.g., arc-extinguishing chambers) is fairly challenging. This study proposes a novel multi-segment extra-slender (3 segments, diameter-to-length ratio <span><math><mo><</mo></math></span>0.04) cable-driven mobile continuum robot (CDMCR) designed to enter confined spaces, execute maintenance tasks, and perform required joint configurations. The coupling between the mobile platform and the cable-driven continuum arm introduces significant redundancy. This redundancy complicates multi-constrained motion planning and reduces computational efficiency when exploring compact unstructured environments. To address this, we developed a real-time motion planner that incorporates mechanical configuration constraints, actuator limits, obstacle avoidance, and arc-surface constraints. The planner generates coordinated base and joint motions that track smooth end-effector trajectories. This enables global path planning from arbitrary initial states in prior-known scenes. Subsequently, an improved Follow-the-Leader (FTL) algorithm, inspired by the natural movement of snakes, ensures self-collision avoidance during end-path tracking. Laboratory and field evaluations demonstrate effective workspace coverage, comprehensive visual inspection capability within high-voltage GIS compartments, and robust success in solving random 6-DOF targets with responsive computation—validating both the robotic architecture and the proposed planning framework for practical power-equipment maintenance.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103205"},"PeriodicalIF":11.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813786","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}
Pub Date : 2025-12-22DOI: 10.1016/j.rcim.2025.103208
Duidi Wu , Qianyou Zhao , Yuliang Shen , Junlai Li , Pai Zheng , Jin Qi , Jie Hu
Industrial assembly represents a core of modern manufacturing but poses significant challenges to the reliability and adaptability of robot systems. As manufacturing shifts toward intelligent production, there is an urgent need for efficient human-to-robot skill transfer methods for mutual cognition. However, current embodied intelligence research has primarily focused on household tasks, while human-level performance in dexterous and long-horizon tasks remains largely unexplored within real-world industrial applications. To bridge this gap, we propose a skill transfer framework and establish a contact-rich assembly benchmark. It integrates an MR-assisted digital twin system for low-cost and diverse demonstrations, an end-to-end generative visuomotor imitation learning policy for continuous action, and primitive skills covering industrially-inspired tasks such as peg insertion, gear meshing, and disassembly. Experiments across six tasks demonstrate high success rates and robust positional generalization. This study explores a novel pathway, it is hoped that it will provide valuable insights for future human–robot collaboration, and serve as a critical precursor for the integration of physical intelligence with generative AI. The project website is available at: https://h2r-mrsta.github.io/.
{"title":"A mixed reality-assisted human-to-robot skill transfer approach for contact-rich assembly via visuomotor primitives","authors":"Duidi Wu , Qianyou Zhao , Yuliang Shen , Junlai Li , Pai Zheng , Jin Qi , Jie Hu","doi":"10.1016/j.rcim.2025.103208","DOIUrl":"10.1016/j.rcim.2025.103208","url":null,"abstract":"<div><div>Industrial assembly represents a core of modern manufacturing but poses significant challenges to the reliability and adaptability of robot systems. As manufacturing shifts toward intelligent production, there is an urgent need for efficient human-to-robot skill transfer methods for mutual cognition. However, current embodied intelligence research has primarily focused on household tasks, while human-level performance in dexterous and long-horizon tasks remains largely unexplored within real-world industrial applications. To bridge this gap, we propose a skill transfer framework and establish a contact-rich assembly benchmark. It integrates an MR-assisted digital twin system for low-cost and diverse demonstrations, an end-to-end generative visuomotor imitation learning policy for continuous action, and primitive skills covering industrially-inspired tasks such as peg insertion, gear meshing, and disassembly. Experiments across six tasks demonstrate high success rates and robust positional generalization. This study explores a novel pathway, it is hoped that it will provide valuable insights for future human–robot collaboration, and serve as a critical precursor for the integration of physical intelligence with generative AI. The project website is available at: <span><span>https://h2r-mrsta.github.io/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103208"},"PeriodicalIF":11.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813784","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}
Due to the complex structures and heterogeneous information inherent in End-of-Life (EOL) products, determining optimal disassembly solutions based on Human-Robot Collaboration (HRC) remains a challenging task. As structural and functional uncertainties in EOL products increase, traditional disassembly approaches struggle to meet the practical disassembly demands. Although various algorithms have been proposed for optimizing disassembly processes, significant challenges persist. These include the limited adaptability of existing models and difficulties in representing dynamic structured information effectively. To address these challenges, this study proposes a novel method combining knowledge graph-driven neural networks with an information decomposition module. This mechanism enables the network to discover structural semantic information and relational connections, facilitating the prediction of optimal disassembly strategies and enhancing the process reasoning capability of EOL product data and knowledge. Similarly, the proposed method provides reliable decision support for HRC disassembly task allocations and tool selections, enabling efficient and safe disassembly operations within complex disassembly processes. Finally, we demonstrate the method’s efficacy by using an example of an EOL battery pack, reasoning optimal disassembly strategies and potential process relations in the complex HRC disassembly scenario.
{"title":"Knowledge graph-driven process reasoning of human-robot collaborative disassembly strategy for end-of-life products","authors":"Jinhua Xiao , Zhiwen Zhang , Yu Zheng , Peng Wu , Sergio Terzi , Marco Macchi","doi":"10.1016/j.rcim.2025.103211","DOIUrl":"10.1016/j.rcim.2025.103211","url":null,"abstract":"<div><div>Due to the complex structures and heterogeneous information inherent in End-of-Life (EOL) products, determining optimal disassembly solutions based on Human-Robot Collaboration (HRC) remains a challenging task. As structural and functional uncertainties in EOL products increase, traditional disassembly approaches struggle to meet the practical disassembly demands. Although various algorithms have been proposed for optimizing disassembly processes, significant challenges persist. These include the limited adaptability of existing models and difficulties in representing dynamic structured information effectively. To address these challenges, this study proposes a novel method combining knowledge graph-driven neural networks with an information decomposition module. This mechanism enables the network to discover structural semantic information and relational connections, facilitating the prediction of optimal disassembly strategies and enhancing the process reasoning capability of EOL product data and knowledge. Similarly, the proposed method provides reliable decision support for HRC disassembly task allocations and tool selections, enabling efficient and safe disassembly operations within complex disassembly processes. Finally, we demonstrate the method’s efficacy by using an example of an EOL battery pack, reasoning optimal disassembly strategies and potential process relations in the complex HRC disassembly scenario.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103211"},"PeriodicalIF":11.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784996","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}
Pub Date : 2025-12-20DOI: 10.1016/j.rcim.2025.103209
Lei Guo , Zeqiang Zhang , Haolin Song , Yan Li
Human-robot collaborative maximizes the respective strengths of humans and robots, driving profound transformations in green intelligent manufacturing and supporting efficient completion of diverse disassembly tasks in remanufacturing. However, existing studies mainly focus on single End-of-Life (EOL) product scenarios. With the increasing variety and volume of EOL products, traditional single-line layouts and disassembly modes struggle to meet the demands of large-scale, multi-type product disassembly. To address this, this paper proposes a human-robot collaborative parallel two-sided destructive disassembly line balancing problem (HRC-PTDDLBP) for multi-product, multi-line scenarios. Firstly, a mixed-integer linear programming model is established for HRC-PTDDLBP to minimize weighted workstation count, smoothness index, and safety risk. To effectively derive the Pareto-optimal solutions, an improved Augmented ε-Constraint method (AUGMECON-2) is developed, which introduces slack variables and adaptive ε-step parameters to enhance convergence stability and solution diversity while avoiding weakly Pareto-optimal points. Secondly, an improved multi-objective discrete water wave optimization algorithm is developed for efficient model solving. The algorithm constructs the initial population based on task priorities and component non-disassemblability, incorporates a decoding strategy considering direction and task attribute conflicts, and enhances search performance through refined crossover, local search, and restart strategies. The model and algorithm correctness are validated within the GUROBI commercial solver’s scope. Benchmarking against seven state-of-the-art multi-objective algorithms under two-sided, human-robot non-destructive, and destructive disassembly modes, the proposed approach demonstrates superior performance. Finally, application to disassembly cases of discarded printers and televisions further validates the method. Compared with the second-best algorithm, the smoothness index is reduced by 87.0%, and safety risk is improved by 20.22%, alongside significant gains in line length reduction and idle time minimization. These results illustrate the comprehensive advantages of the proposed method in multi-product, multi-line human-robot collaborative disassembly line balancing, offering a practical and adaptable solution for real-world disassembly systems.
{"title":"Industrial application of a human-robot collaborative parallel two-sided destructive disassembly line balancing problem in multi-product, multi-line layouts","authors":"Lei Guo , Zeqiang Zhang , Haolin Song , Yan Li","doi":"10.1016/j.rcim.2025.103209","DOIUrl":"10.1016/j.rcim.2025.103209","url":null,"abstract":"<div><div>Human-robot collaborative maximizes the respective strengths of humans and robots, driving profound transformations in green intelligent manufacturing and supporting efficient completion of diverse disassembly tasks in remanufacturing. However, existing studies mainly focus on single End-of-Life (EOL) product scenarios. With the increasing variety and volume of EOL products, traditional single-line layouts and disassembly modes struggle to meet the demands of large-scale, multi-type product disassembly. To address this, this paper proposes a human-robot collaborative parallel two-sided destructive disassembly line balancing problem (HRC-PTDDLBP) for multi-product, multi-line scenarios. Firstly, a mixed-integer linear programming model is established for HRC-PTDDLBP to minimize weighted workstation count, smoothness index, and safety risk. To effectively derive the Pareto-optimal solutions, an improved Augmented ε-Constraint method (AUGMECON-2) is developed, which introduces slack variables and adaptive ε-step parameters to enhance convergence stability and solution diversity while avoiding weakly Pareto-optimal points. Secondly, an improved multi-objective discrete water wave optimization algorithm is developed for efficient model solving. The algorithm constructs the initial population based on task priorities and component non-disassemblability, incorporates a decoding strategy considering direction and task attribute conflicts, and enhances search performance through refined crossover, local search, and restart strategies. The model and algorithm correctness are validated within the GUROBI commercial solver’s scope. Benchmarking against seven state-of-the-art multi-objective algorithms under two-sided, human-robot non-destructive, and destructive disassembly modes, the proposed approach demonstrates superior performance. Finally, application to disassembly cases of discarded printers and televisions further validates the method. Compared with the second-best algorithm, the smoothness index is reduced by 87.0%, and safety risk is improved by 20.22%, alongside significant gains in line length reduction and idle time minimization. These results illustrate the comprehensive advantages of the proposed method in multi-product, multi-line human-robot collaborative disassembly line balancing, offering a practical and adaptable solution for real-world disassembly systems.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103209"},"PeriodicalIF":11.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796201","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}
Pub Date : 2025-12-18DOI: 10.1016/j.rcim.2025.103207
Jianhao Lv, Jiahui Si, Wenchao Li, Ding Gao, Jinsong Bao
The inherent limitations of single-agent systems in tackling complex tasks, combined with the inefficiencies of traditional multi-agent paradigms—where task decomposition requires distribution among multiple robots, resulting in resource redundancy and escalated costs. To address this critical constraint, a graph-driven Single-Robot Multi-Cognitive Agent System architecture is proposed. Firstly, scene graphs are constructed to transform unstructured visual data from the environment into graph-based triplets. By aligning these triplets with pre-constructed knowledge graphs, historical memories are activated through graph matching to inform system decision-making with precedented insights. Then, an attention-driven collaboration mechanism dynamically designates leader and supporter roles among the different agents, ensuring adaptive role assignment based on contextual demands. Complementing this, a global optimization framework facilitates the collective evolution of the Single-Robot Multi-Cognitive Agent System, enhancing both individual agent performance and inter-agent collaboration. Finally, the Model Context Protocol orchestrates robotic execution by harmonizing external resource utilization with computational processes, ensuring seamless translation of decision outputs into physical actions. Experimental results demonstrate that the method exhibits strong robustness and generalizability in dynamic disassembly queries.
{"title":"Graph-driven Single-Robot Multi-Cognitive Agent System architecture for human–robot collaborative disassembly","authors":"Jianhao Lv, Jiahui Si, Wenchao Li, Ding Gao, Jinsong Bao","doi":"10.1016/j.rcim.2025.103207","DOIUrl":"10.1016/j.rcim.2025.103207","url":null,"abstract":"<div><div>The inherent limitations of single-agent systems in tackling complex tasks, combined with the inefficiencies of traditional multi-agent paradigms—where task decomposition requires distribution among multiple robots, resulting in resource redundancy and escalated costs. To address this critical constraint, a graph-driven Single-Robot Multi-Cognitive Agent System architecture is proposed. Firstly, scene graphs are constructed to transform unstructured visual data from the environment into graph-based triplets. By aligning these triplets with pre-constructed knowledge graphs, historical memories are activated through graph matching to inform system decision-making with precedented insights. Then, an attention-driven collaboration mechanism dynamically designates leader and supporter roles among the different agents, ensuring adaptive role assignment based on contextual demands. Complementing this, a global optimization framework facilitates the collective evolution of the Single-Robot Multi-Cognitive Agent System, enhancing both individual agent performance and inter-agent collaboration. Finally, the Model Context Protocol orchestrates robotic execution by harmonizing external resource utilization with computational processes, ensuring seamless translation of decision outputs into physical actions. Experimental results demonstrate that the method exhibits strong robustness and generalizability in dynamic disassembly queries.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103207"},"PeriodicalIF":11.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785002","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}
Pub Date : 2025-12-16DOI: 10.1016/j.rcim.2025.103204
Yusen Wan, Xu Chen
A critical capability for intelligent manufacturing is the ability for robotic systems to understand the spatial operation environment – the ability for robots to precisely recognize and estimate the spatial positions and orientations of objects in industrial scenes. Existing scene reconstruction methods are designed for general settings with low precision needs of objects and abundant data. However, manufacturing hinges on high object precision and operates with limited data. Addressing such challenges and limitations, we propose a novel Learning-based Scene Point-cloud Registration framework for automatic industrial scene reconstruction (iLSPR). The proposed iLSPR framework leverages point cloud representation and integrates three key innovations: (i) a Multi-Feature Robust Point Matching Network (MF-RPMN) that learns from both raw data and deep features of the objects to accurately align point clouds, (ii) a Geometric-Primitive-based Data Generation (GPDG) method for efficient synthetic data generation, and (iii) a digital model library of industrial target objects. During operation, vision sensors capture point clouds in the scenes, and the iLSPR method registers high-fidelity object models in the scenes using MF-RPMN, pre-trained with GPDG-generated data. We introduce an Industrial Scene Object Point-cloud Registration (ISOPR) dataset in IsaacSim to benchmark performance. Experimental results demonstrate that iLSPR significantly outperforms existing methods in accuracy and robustness. We further validate the approach on a real-world robotic manufacturing system, demonstrating reliable digital reconstruction of industrial scenes.
{"title":"iLSPR: A Learning-based Scene Point-cloud Registration method for robotic spatial awareness in intelligent manufacturing","authors":"Yusen Wan, Xu Chen","doi":"10.1016/j.rcim.2025.103204","DOIUrl":"10.1016/j.rcim.2025.103204","url":null,"abstract":"<div><div>A critical capability for intelligent manufacturing is the ability for robotic systems to understand the spatial operation environment – the ability for robots to precisely recognize and estimate the spatial positions and orientations of objects in industrial scenes. Existing scene reconstruction methods are designed for general settings with low precision needs of objects and abundant data. However, manufacturing hinges on high object precision and operates with limited data. Addressing such challenges and limitations, we propose a novel <strong>L</strong>earning-based <strong>S</strong>cene <strong>P</strong>oint-cloud <strong>R</strong>egistration framework for automatic <strong>i</strong>ndustrial scene reconstruction (iLSPR). The proposed iLSPR framework leverages point cloud representation and integrates three key innovations: (i) a <strong>M</strong>ulti-<strong>F</strong>eature <strong>R</strong>obust <strong>P</strong>oint <strong>M</strong>atching <strong>N</strong>etwork (MF-RPMN) that learns from both raw data and deep features of the objects to accurately align point clouds, (ii) a <strong>G</strong>eometric-<strong>P</strong>rimitive-based <strong>D</strong>ata <strong>G</strong>eneration (GPDG) method for efficient synthetic data generation, and (iii) a digital model library of industrial target objects. During operation, vision sensors capture point clouds in the scenes, and the iLSPR method registers high-fidelity object models in the scenes using MF-RPMN, pre-trained with GPDG-generated data. We introduce an <strong>I</strong>ndustrial <strong>S</strong>cene <strong>O</strong>bject <strong>P</strong>oint-cloud <strong>R</strong>egistration (ISOPR) dataset in IsaacSim to benchmark performance. Experimental results demonstrate that iLSPR significantly outperforms existing methods in accuracy and robustness. We further validate the approach on a real-world robotic manufacturing system, demonstrating reliable digital reconstruction of industrial scenes.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103204"},"PeriodicalIF":11.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785000","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}
Pub Date : 2025-12-13DOI: 10.1016/j.rcim.2025.103203
Hongquan Gui , Ming Li
Modeling human behavior is paramount in the process of human-robot interaction (HRI). Human motion during HRI is uncertain. Even when the same individual performs the same action, it is impossible to ensure that the motion trajectory will be identical every time. These factors make modeling human behavior extremely challenging. Beyond human uncertainty, there are dynamic temporal spatial dependencies in HRI. Effectively capturing uncertainty while fully integrating temporal spatial features presents a significant challenge. Moreover, representing human behavior solely through human skeleton is insufficient. Recently, human digital twins have been developed to represent human geometry. However, current human digital twins are not well-suited for dynamic HRI scenarios, as they struggle to accurately depict high-dimensional human parameters, leading to issues such as nonlinear mapping and joint drift. In summary, existing methods find it difficult to address the human uncertainty, the temporal spatial dependencies, and the high-dimensional human parameters. To address the above challenges, this study proposes a temporal spatial human digital twin (TSHDT) for modeling human behavior in HRI. The TSHDT is based on predicted human skeletons and integrates forward and inverse kinematics along with diffusion prior distribution to represent high-dimensional human parameters, thus preventing joint drift and nonlinear mapping between joints. In developing the TSHDT, we introduce the human robot temporal spatial (HRTS) diffusion model to mitigate the uncertainty in human motion. The unique diffusion and denoising processes of the HRTS diffusion model can effectively submerge uncertainty in noise and accurately predict human motion during subsequent denoising steps. To ensure that the denoising process favors accuracy over diversity, we propose the temporal spatial fusion graph convolutional network (TSFGCN) to capture temporal spatial features between humans and robots, embedding them into the HRTS diffusion model. Finally, the effectiveness of the TSHDT was validated via predictive collision detection in human-robot fabric cutting experiments. Results demonstrate that the proposed method accurately models human behavior in collision detection experiments, achieving outstanding F1 scores.
{"title":"A temporal spatial human digital twin approach for modeling human behavior with uncertainty","authors":"Hongquan Gui , Ming Li","doi":"10.1016/j.rcim.2025.103203","DOIUrl":"10.1016/j.rcim.2025.103203","url":null,"abstract":"<div><div>Modeling human behavior is paramount in the process of human-robot interaction (HRI). Human motion during HRI is uncertain. Even when the same individual performs the same action, it is impossible to ensure that the motion trajectory will be identical every time. These factors make modeling human behavior extremely challenging. Beyond human uncertainty, there are dynamic temporal spatial dependencies in HRI. Effectively capturing uncertainty while fully integrating temporal spatial features presents a significant challenge. Moreover, representing human behavior solely through human skeleton is insufficient. Recently, human digital twins have been developed to represent human geometry. However, current human digital twins are not well-suited for dynamic HRI scenarios, as they struggle to accurately depict high-dimensional human parameters, leading to issues such as nonlinear mapping and joint drift. In summary, existing methods find it difficult to address the human uncertainty, the temporal spatial dependencies, and the high-dimensional human parameters. To address the above challenges, this study proposes a temporal spatial human digital twin (TSHDT) for modeling human behavior in HRI. The TSHDT is based on predicted human skeletons and integrates forward and inverse kinematics along with diffusion prior distribution to represent high-dimensional human parameters, thus preventing joint drift and nonlinear mapping between joints. In developing the TSHDT, we introduce the human robot temporal spatial (HRTS) diffusion model to mitigate the uncertainty in human motion. The unique diffusion and denoising processes of the HRTS diffusion model can effectively submerge uncertainty in noise and accurately predict human motion during subsequent denoising steps. To ensure that the denoising process favors accuracy over diversity, we propose the temporal spatial fusion graph convolutional network (TSFGCN) to capture temporal spatial features between humans and robots, embedding them into the HRTS diffusion model. Finally, the effectiveness of the TSHDT was validated via predictive collision detection in human-robot fabric cutting experiments. Results demonstrate that the proposed method accurately models human behavior in collision detection experiments, achieving outstanding F1 scores.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103203"},"PeriodicalIF":11.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731184","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}
Pub Date : 2025-12-13DOI: 10.1016/j.rcim.2025.103206
Weimin Jing , Yong Yan , Yonghui Zhang , Xiang Ji , Wen Huang , Youling Chen , Huan Zhang
In the context of cloud manufacturing service, scheduling manufacturing tasks is a crucial area of research because it directly influences service quality and efficiency. As intelligent manufacturing technologies advance, edge manufacturing service providers have gained increasingly flexible ability, enabling them to adjust local production schedules to adapt cloud manufacturing tasks. However, because local manufacturing information is private, traditional centralized cloud manufacturing scheduling methods cannot fully leverage edge flexibility to collaboratively schedule multi-source manufacturing tasks (including both cloud manufacturing tasks and local tasks of edge service providers) without risking the disclosure of sensitive information, thereby limiting improvements in both performance and efficiency of cloud manufacturing service. Therefore, we propose a privacy-preserving cloud-edge collaborative decision-making approach based on auction theory to schedule multi-source manufacturing tasks. First, a mathematical model that accounts for the objectives of both service providers and demanders is established to characterize the collaborative scheduling of multi-sourced tasks. Subsequently, a cloud-edge collaborative scheduling decision framework is introduced. Building upon this, a multi-stage scheduling method based on combinatorial iterative auctions is proposed, featuring novel bidding with a flexible execution timeline and distributed winner determination process incorporating bid consolidation mechanisms to enhance the efficiency of cloud-edge collaborative decision. Finally, to validate the superiority of the proposed method, computational experiments are conducted, comparing it with traditional centralized manufacturing task scheduling methods. The results present that the proposed method not only completes cloud manufacturing tasks within a relatively shorter makespan but also provides higher-value manufacturing services to demanders. Moreover, as the cloud manufacturing task load increases, this advantage becomes even more pronounced.
{"title":"Auction-based privacy-preserving cloud-edge collaborative scheduling considering flexible service ability for multi-source manufacturing tasks","authors":"Weimin Jing , Yong Yan , Yonghui Zhang , Xiang Ji , Wen Huang , Youling Chen , Huan Zhang","doi":"10.1016/j.rcim.2025.103206","DOIUrl":"10.1016/j.rcim.2025.103206","url":null,"abstract":"<div><div>In the context of cloud manufacturing service, scheduling manufacturing tasks is a crucial area of research because it directly influences service quality and efficiency. As intelligent manufacturing technologies advance, edge manufacturing service providers have gained increasingly flexible ability, enabling them to adjust local production schedules to adapt cloud manufacturing tasks. However, because local manufacturing information is private, traditional centralized cloud manufacturing scheduling methods cannot fully leverage edge flexibility to collaboratively schedule multi-source manufacturing tasks (including both cloud manufacturing tasks and local tasks of edge service providers) without risking the disclosure of sensitive information, thereby limiting improvements in both performance and efficiency of cloud manufacturing service. Therefore, we propose a privacy-preserving cloud-edge collaborative decision-making approach based on auction theory to schedule multi-source manufacturing tasks. First, a mathematical model that accounts for the objectives of both service providers and demanders is established to characterize the collaborative scheduling of multi-sourced tasks. Subsequently, a cloud-edge collaborative scheduling decision framework is introduced. Building upon this, a multi-stage scheduling method based on combinatorial iterative auctions is proposed, featuring novel bidding with a flexible execution timeline and distributed winner determination process incorporating bid consolidation mechanisms to enhance the efficiency of cloud-edge collaborative decision. Finally, to validate the superiority of the proposed method, computational experiments are conducted, comparing it with traditional centralized manufacturing task scheduling methods. The results present that the proposed method not only completes cloud manufacturing tasks within a relatively shorter makespan but also provides higher-value manufacturing services to demanders. Moreover, as the cloud manufacturing task load increases, this advantage becomes even more pronounced.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103206"},"PeriodicalIF":11.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753457","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}
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