Robotics and Computer-integrated Manufacturing最新文献

{"title":"GNN-LLM hybrid cognitive architectures for generative task adaptation in multi-human multi-robot collaborative disassembly","authors":"Xiaodong Tong,&nbsp;Ke Li,&nbsp;Jinsong Bao","doi":"10.1016/j.rcim.2025.103169","DOIUrl":"10.1016/j.rcim.2025.103169","url":null,"abstract":"<div><div>Traditional human-robot collaboration research has primarily focused on single human-robot dyads, yet faces significant challenges in addressing complex industrial scenarios characterized by concurrent multi-tasking, dynamic disturbances, and heterogeneous role coordination. Transitioning toward multi-human multi-robot collaboration (MHMRC) is crucial for achieving a significant leap in coordinated efficiency and manufacturing flexibility. To address this, we investigate a Hybrid Cognitive Digital Twin (HCDT) framework through generative knowledge-augmented paradigms. Our approach introduces a human-centric cognitive entity to generate task data and knowledge-driven strategies for MHMRC. This work demonstrates that integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) offers robust capabilities in comprehension, reasoning, ideally meeting MHMRC's requirements for handling unplanned operational variations as well as adapting to dynamic collaborative tasks. Furthermore, we demonstrate that compared to human-engineered precoding strategies, the HCDT-powered MHMRC system autonomously generates collaborative strategies for unscheduled tasks under more complex dynamic conditions and mission scenarios, enabling the execution of situations beyond conventional predefined patterns. The proposed methodology was validated in automotive lithium-ion battery (LIB) disassembly applications. Experimental results demonstrate its adaptability to dynamic collaborative tasks and generalization in generating strategies for unplanned operational variations within dynamic disassembly environments. This approach effectively overcomes various technical challenges to achieve autonomous collaboration in MHMRC systems through knowledge representation, task allocation, and collaborative optimization.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103169"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314951","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":"From perception to precision: Vision-based mobile robotic manipulation for assembly screwdriving","authors":"Aleksandar Stefanov,&nbsp;Miha Zorman,&nbsp;Sebastjan Šlajpah,&nbsp;Janez Podobnik,&nbsp;Matjaž Mihelj,&nbsp;Marko Munih","doi":"10.1016/j.rcim.2025.103148","DOIUrl":"10.1016/j.rcim.2025.103148","url":null,"abstract":"<div><div>Flexible manufacturing demands automation that is both precise and adaptable. However, tasks such as screwdriving are typically automated using costly, rigid robotic cells, making this approach impractical for low-volume, high-mix production. As a scalable solution, mobile manipulators offer a flexible alternative, but achieving the required precision for screwdriving remains challenging due to localization uncertainties. This paper addresses these limitations by presenting a vision-guided mobile robotic manipulation system that performs high-precision screwdriving using only monocular RGB imagery. The proposed pipeline integrates stationary and onboard cameras with perception algorithms for object identification and segmentation, pose estimation, and CAD-based screw hole localization, compensating for base misalignment and object placement variability. Experimental validation using ISO 9283 standard’s metrics demonstrates a translational accuracy between 0.21 mm and 0.50 mm across multiple screw positions. Additionally, the system achieves angular estimation errors as low as 0.07°to 0.20°, verifying its capability for sub-degree precision in orientation estimation. In 50 independent experiments involving a total of 400 screw insertions, the system achieved a 100 % success rate, confirming its reliability in practical conditions. These results confirm the feasibility of using RGB-only vision for precision screwdriving and highlight the mobile manipulation system’s scalability for real-world semi-structured manufacturing environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103148"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220393","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":"Identification and three-dimensional absorption of time-varying potential chatter during robotic milling","authors":"Jiawei Wu ,&nbsp;Rui Fu ,&nbsp;Xiaowei Tang ,&nbsp;Shihao Xin ,&nbsp;Fangyu Peng ,&nbsp;Chenyang Wang","doi":"10.1016/j.rcim.2025.103173","DOIUrl":"10.1016/j.rcim.2025.103173","url":null,"abstract":"<div><div>Robotic milling constitutes an important component of robotized intelligent manufacturing, gaining increasing popularity for subtractive manufacturing of large components. Extensive efforts have been devoted to the analysis and suppression of robot chatter to enhance milling efficiency and quality. However, the dynamic characteristics of robots are highly pose-dependent, leading to time-varying low-frequency chatter. Meanwhile, the low-frequency chatter is continuously influenced by the action of vibration suppression devices, making it challenging to consistently track and suppress time-varying chatter. To address this, this paper proposes a new concept, the potential chatter mode, to more accurately describe the target mode that requires attention in online chatter suppression. Inspired by the modulation mechanism between modal vibrations and spindle rotation during robotic milling, a potential chatter mode identification framework is developed. By investigating the distribution pattern of vibration spectra under the modulation mechanism, and integrating filtering, demodulation, signal decomposition, and vibration energy evaluation, it achieves the online identification of the time-varying frequency of potential chatter. Furthermore, the potential chatter exhibits a three-dimensional time-varying direction, whereas the existing suppression devices are generally designed to operate in one or two directions. This paper develops a novel three-dimensional orthogonal adaptive vibration absorber (TO-AVA) based on magnetorheological elastomers (MRE). By incorporating a parallel negative stiffness mechanism and parameter design, the TO-AVA can handle the three-dimensional time-varying direction of potential chatter. Validation experiments of robotic milling are conducted, which involves various process parameters and time-varying potential chatter across different directions, frequencies, and states. The results demonstrate that the developed framework can accurately identify time-varying potential chatter and effectively suppress it using the TO-AVA.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103173"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412100","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":"Accurate localization and girth weld grinding planning for an in-pipe machining robot of thin-walled conical pipe","authors":"Tian Lan ,&nbsp;Te Li ,&nbsp;Haibo Liu ,&nbsp;Shiyu Tian ,&nbsp;Kuo Liu ,&nbsp;Yongqing Wang","doi":"10.1016/j.rcim.2025.103124","DOIUrl":"10.1016/j.rcim.2025.103124","url":null,"abstract":"<div><div>In-pipe robots have been applied to detection, cleaning, welding, grinding, drilling, etc., which realizes the narrow space operation efficiently and economically. However, intelligent operation is still a problem, especially for high-precision machining of inner welds in thin-walled conical pipes, due to the low stiffness and precision of the robot machining system, uncertain robot position, and poor consistency of target characteristics. To address this problem, an intelligent girth weld grinding method of an in-pipe machining robot for thin-walled conical pipes is proposed. An in-pipe machining robot (named IPMR-I) with adaptive motion ability, controllable stiffness, and high machining precision is designed, which benefits by the designs of controllable contact forces and a high-precision three-axis machining mechanism. A weld locating method based on time-series point clouds, combined with an analytical pose model, is used for robot self-localization, obtaining the accurate pose of the robot relative to the weld region. Furthermore, an intelligent machining path planning method is proposed with the abilities of the weld boundary recognition, machining path generation, and optimization, which adaptively realizes the high machining quality and safety facing the welding irregularity (e.g., deformation and misalignment) inside thin-walled conical pipes. Several weld bead grinding experiments were conducted inside thin-walled conical pipes to verify the proposed methods’ validity. The results proved that IPMR-I with the proposed intelligent girth weld grinding method completed autonomous high-quality machining without manual intervention. Without damaging the base material, the maximum residual weld height is 0.18 mm, with the average residual height controlled at approximately 0.08 mm.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103124"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093757","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":"A generalised system for multi-mobile robot cooperation in smart manufacturing","authors":"Tianwei Zhang ,&nbsp;Ning Wang ,&nbsp;Yiming Yang ,&nbsp;Ziya Wang","doi":"10.1016/j.rcim.2025.103139","DOIUrl":"10.1016/j.rcim.2025.103139","url":null,"abstract":"<div><div>Since the advent of Industry 4.0, mobile collaborative robot technology has developed rapidly. However, several challenges still hinder the industrial application of mobile collaborative robots. These challenges include human–robot collaboration safety, the complexity of non-standard mobile collaborative task solutions, and the deployment timeliness of heterogeneous robots and mechanical structures. To address these challenges, this paper proposes a general and robust “cloud–edge-terminal-network-intelligence” multi-robot mobile collaboration system. This framework achieves efficient customised solutions by standardising and simplifying robot hardware and software integration. The solution focuses on three key issues: modular design, cloud–edge architecture in advanced manufacturing, and rapid deployment of heterogeneous multi-robots. The paper discusses robot safety and collaborative control issues and provides corresponding technical solutions.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103139"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118870","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":"Stable grasp generation enabled by part segmentation for real-world robotic applications","authors":"Zirui Guo,&nbsp;Xieyuanli Chen,&nbsp;Junkai Ren,&nbsp;Zhiqiang Zheng,&nbsp;Huimin Lu,&nbsp;Ruibin Guo","doi":"10.1016/j.rcim.2025.103170","DOIUrl":"10.1016/j.rcim.2025.103170","url":null,"abstract":"<div><div>Robotic manipulation necessitates the capability of advanced perception and grasp generation. Previous approaches for object perception in manipulation mainly rely on original point clouds captured from vision sensors, which exhibit inherent limitations in view perspectives and lack of further analysis of the sensor data. This research introduces implicit representation to facilitate part segmentation from imaging sensors, generating 3D models with structural information that provide grasp generation algorithms with more useful information. Regarding the robotic grasp, prior methods mostly rely on deep learning, which presents satisfactory performance on particular datasets yet raises concerns considering their generalization performance. Instead, this article proposes a novel grasp generation method based on 3D part segmentation, which circumvents the reliance on deep learning techniques. Extensive experimental results show that our approach can proficiently generate approximate part segmentation and high success rate grasps for various objects. By integrating part segmentation with grasp generation, the robot achieves accurate autonomous manipulation as shown in the supplementary video.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103170"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382962","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":"Force-sensing-based compliant error compensation control for robotic milling of weak-stiffness thin-walled components","authors":"Qunfei Gu ,&nbsp;Shun Liu ,&nbsp;Sun Jin ,&nbsp;Dong Liu","doi":"10.1016/j.rcim.2025.103121","DOIUrl":"10.1016/j.rcim.2025.103121","url":null,"abstract":"<div><div>Industrial robots hold considerable potential in the field of milling operations. However, due to their structural characteristics, significant machining errors often occur during the milling process. For the robotic milling, existing research rarely provides direct control strategies for machining error compensation, which limits the further application of industrial robots in high-precision machining tasks. To enhance the robotic milling accuracy, this paper proposes an error compensation control method based on force sensing. First, to predict the relationship between cutting forces and machining parameters, an improved cutting force model is developed by introducing the material removal parameter S_l. Combining the cutting force signals with the robot's position data, the machining error can be predicted. Furthermore, by considering the stiffness characteristics of both the robot and the workpiece, an error compensation control method is proposed. The initial milling trajectory is generated using the robot’s spatial pose and the workpiece model. Based on force sensing and the desired machining accuracy, the cutting parameters are adaptively adjusted. A data-driven adaptive parameter adjustment strategy is further proposed by integrating robot motion data, machining data, and cutting force signals. By adjusting the feed rate in different out-of-tolerance regions, a new compensated milling trajectory is generated to correct machining errors. To validate the effectiveness of the proposed method, robotic milling experiments were conducted on thin-walled light alloy workpieces and feature components. The experimental results demonstrate that the proposed approach significantly reduces machining errors in robotic milling, thereby improving both machining quality and efficiency. These results indicate that the proposed method has strong potential for high-precision robotic milling of complex thin-walled structures.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103121"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005338","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":"Intelligent support-free additive manufacturing path planning via method library and neural network","authors":"Zhengren Tong ,&nbsp;Lai Xu ,&nbsp;Xianglong Li ,&nbsp;Chen Yang ,&nbsp;Qinfeng Wang ,&nbsp;Hongyao Shen","doi":"10.1016/j.rcim.2025.103156","DOIUrl":"10.1016/j.rcim.2025.103156","url":null,"abstract":"<div><div>Support-free additive manufacturing achieves self-supporting fabrication by adjusting the platform’s posture, effectively reducing material waste and simplifying the post-processing stage. However, the diversity of industrial part geometries requires different approaches to plan the robotic manufacturing paths. Traditional approaches to selecting support-free manufacturing path planning methods rely heavily on expert knowledge. This paper proposes an intelligent path planning system based on a method library. The system utilizes a method library composed of seven approaches to achieve support-free additive manufacturing path planning of various types of parts. The additive manufacturing strategy matching neural network (AMMatcher) is employed to match the optimal path planning method from the method library to a given model and to identify its base surface. AMMatcher can analyze the multi-scale features of the model and leverage a cross-task attention mechanism to propagate classification features into the segmentation task, thereby improving network performance. A newly proposed support-free additive manufacturing model dataset (SFAMDataset) is used to evaluate the performance of AMMatcher and typical samples are validated through fabrication experiments on three different manufacturing platforms. Experimental results demonstrate that AMMatcher effectively identifies suitable manufacturing strategies for various model types and exhibits strong adaptability across different manufacturing platforms.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103156"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220967","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":"Sparse-VMICP: A weak feature point cloud registration algorithm for robotic vision measurement of large complex components","authors":"Xiaozhi Feng ,&nbsp;Tao Ding ,&nbsp;Hao Wu ,&nbsp;Di Li ,&nbsp;Ning Jiang ,&nbsp;Dahu Zhu","doi":"10.1016/j.rcim.2025.103175","DOIUrl":"10.1016/j.rcim.2025.103175","url":null,"abstract":"<div><div>High-precision three-dimensional (3D) measurement of large complex components (LCCs) such as vehicle bodies provides data benchmark for subsequent robotized manufacturing processes. A huge challenge in LCCs measurement is to register the adjacent point clouds with partial overlap, especially when the point cloud geometric features are weak. Despite the existing sparse iterative closest point (Sparse-ICP) registration algorithm uses <em>l_p</em> norm to reduce the influence of non-overlapping point clouds during the registration process, however sparse point pairs are prone to fall into local optimum, which causes the registration accuracy to be greatly affected by the initial pose. To overcome the challenging problem, we inherit the advantage of the Sparse-ICP algorithm that the point-to-point distance can suppress tangential slip in the smooth areas. On this basis, we introduce the constraint of point-to-plane distance variance minimization under sparse condition that can suppress the incorrect registration inclination caused by uneven point cloud density, and then present a hybrid algorithm termed as Sparse-VMICP for weak feature point cloud registration. The proposed algorithm aims to enhance the robotic vision measurement accuracy by suppressing registration inclination to adjust the local optimal solution. Robotic vision measurement experiments on two typical LCCs, including high-speed rail body and car bodywork are conducted to verify the superiority of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively reduce the accumulated registration errors in large-scale metrology, compared with other state-of-the-art algorithms, and the stitching measurement accuracy of LCCs can reach 0.012 mm.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103175"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447346","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":"From insight to autonomous execution: VLM-enhanced embodied agents towards digital twin-assisted human-robot collaborative assembly","authors":"Changchun Liu ,&nbsp;JiaYe Song ,&nbsp;Dunbing Tang ,&nbsp;Liping Wang ,&nbsp;Haihua Zhu ,&nbsp;Qixiang Cai","doi":"10.1016/j.rcim.2025.103176","DOIUrl":"10.1016/j.rcim.2025.103176","url":null,"abstract":"<div><div>In recent years, embodied intelligence has emerged as a practicable strategy for accomplishing human-level cognitive abilities, reasoning capacities, and execution capabilities within human-robot collaborative (HRC) assembly scenarios. As the physical instantiation of embodied intelligence, embodied agents remain largely in the exploratory phase; their practical application has yet to mature into a standardized paradigm. A key bottleneck lies in the lack of universally applicable enabling technologies, coupled with a disconnection from physical robot control systems. This deficiency necessitates repetitious training for a variety of functional models when operating in dynamic HRC environments, significantly hindering the ability of embodied agents to acclimate to complicated, dynamically changing collaborative settings. To address this challenge, this study proposes VLM-enhanced embodied agents, specifically tailored to support multimodal cognition, task reasoning, and autonomous execution in digital twin-assisted HRC assembly contexts. The framework is structured through several core steps to realize the full process closed loop from insight to autonomous execution of robots supported by embodied intelligent agents. First, a precise epsilon map relation between the embodied agent and the physical cobot is constructed, thereby enabling the digital characterization and functional capsulation of embodied agents. Building on this agent-based framework, a VLM is developed that integrates domain-specific knowledge with real-time scenario information. This dual-driven design endows the VLM with enhanced perceptual capabilities, allowing it to rapidly recognize and respond to dynamic changes in HRC scenarios. To provide a simulation and deduction engine for embodied reasoning of the assembly task, a digital twin model of the HRC scenario is built to serve as the “embodied brain”. Subsequently, these reasoning results are fed into the VLM serving as invoking parameters for the homologous sub-functional code module. This process facilitates the generation of complete robot motion code, enabling seamless physical execution and thus functioning as the “embodied neuron”. Finally, comparable experiments are conducted in an actual HRC assembly environment. The experimental results demonstrate that the proposed VLM-enhanced embodied agents have competitive advantages in multimodal cognition, task reasoning, and autonomous execution.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103176"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145441661","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}