Robotics and Computer-integrated Manufacturing最新文献_第3页

A generalised system for multi-mobile robot cooperation in smart manufacturing 智能制造中多移动机器人协作的通用系统

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-23 DOI: 10.1016/j.rcim.2025.103139

Tianwei Zhang , Ning Wang , Yiming Yang , Ziya Wang

引用次数: 0

Integrated scheduling of green production with flexible preventive maintenance and customer-side workers by a learning-based coevolutionary algorithm 基于学习的协同进化算法的柔性预防性维护和客户端工人的绿色生产集成调度

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-19 DOI: 10.1016/j.rcim.2025.103140

Jingxing Zhang , Qianwang Deng , Qiang Luo , Kaidan Deng , Mengqi Liao , Yong Lei

{"title":"Integrated scheduling of green production with flexible preventive maintenance and customer-side workers by a learning-based coevolutionary algorithm","authors":"Jingxing Zhang , Qianwang Deng , Qiang Luo , Kaidan Deng , Mengqi Liao , Yong Lei","doi":"10.1016/j.rcim.2025.103140","DOIUrl":"10.1016/j.rcim.2025.103140","url":null,"abstract":"<div><div>Previous production scheduling studies on integrating preventive maintenance (PM) plans have overlooked the impact of arranging customer-side workers on the coupling of spare part delivery times, potentially leading to inefficient solutions. To address this gap, this study expands an integrated scheduling model of green two-stage hybrid flowshop production with flexible PM mode and customer-side workers for spare part replacement services. The model arranges tasks for the customer-side workers based on the relationship between delivery timetables, worker selection, replacement sequences, and equipment due time windows, aiming to maximize the total customer satisfaction. Another objective is to minimize the total energy consumption during production, maintenance and idle processes. To solve the large-scale instances, a double deep Q-network-based coevolutionary algorithm (shorten to DDQCA) is proposed, incorporating a six-layer chromosome encoding scheme. In DDQCA, double deep Q-networks are trained online to guide the selection of crossover methods for coevolution. Additionally, the DDQCA incorporates a hybrid initialization operator, two objectives-oriented local search methods and a proposition-based PM strategy to enhance search performance. Finally, comprehensive experiments are conducted to validate the effectiveness of the algorithm. In addition, the results also demonstrate that the proposed integrated scheduling with flexible PM mode can improve energy efficiency but significantly customer satisfaction compared to the classical mode.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103140"},"PeriodicalIF":11.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093755","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

Digital twin and AI-driven robotic embodied control system: a novel adaptive learning and decision optimization method 数字孪生和人工智能驱动的机器人嵌入控制系统：一种新的自适应学习和决策优化方法

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-17 DOI: 10.1016/j.rcim.2025.103138

Hao Li, Xingyou He, Yonglei Wu, Gen Liu, Haoqi Wang, Xiaoyu Wen, Linli Li

{"title":"Digital twin and AI-driven robotic embodied control system: a novel adaptive learning and decision optimization method","authors":"Hao Li, Xingyou He, Yonglei Wu, Gen Liu, Haoqi Wang, Xiaoyu Wen, Linli Li","doi":"10.1016/j.rcim.2025.103138","DOIUrl":"10.1016/j.rcim.2025.103138","url":null,"abstract":"<div><div>Traditional robot control methods often encounter limitations such as lengthy development cycles and insufficient flexibility when addressing dynamic production environments and complex task requirements. To overcome these challenges, this paper constructs an integrated robot embodied control (EC) system that organically combines digital twins (DT), machine vision, and deep reinforcement learning (DRL). The method follows a closed-loop perception-decision-action framework. First, machine vision senses the environment in real time and precisely maps the 3D pose of the target object to the DT space. Second, DRL is conducted in the DT environment for training and strategy optimization. Finally, continuous state synchronization between the physical robot and the DT enables cross-environment policy transfer and online optimization. Taking the robotic arm pressing an emergency stop button as a representative task scenario, experimental results show that the system achieves a task success rate of 88% in the DT environment and 73% in the real physical environment, which was further improved to 76% through fine-tuning. In an extended lamp switch task, the success rate reached 79%, further verifying the generality and cross-environment adaptability of the framework. Overall, this integrated system significantly enhances the intelligence and operational efficiency of robotic systems, demonstrating its potential for achieving programming-free autonomous control in complex industrial environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103138"},"PeriodicalIF":11.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093823","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

Automated multimodal process knowledge graph construction for intelligent process planning with Cross-Modal Transformers 基于跨模态变压器的智能工艺规划的自动化多模态工艺知识图谱构建

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-17 DOI: 10.1016/j.rcim.2025.103141

Qingfeng Xu , Chao Zhang , Dongxu Ma , Jiacheng Li , Jiewu Leng , Guanghui Zhou

{"title":"Automated multimodal process knowledge graph construction for intelligent process planning with Cross-Modal Transformers","authors":"Qingfeng Xu , Chao Zhang , Dongxu Ma , Jiacheng Li , Jiewu Leng , Guanghui Zhou","doi":"10.1016/j.rcim.2025.103141","DOIUrl":"10.1016/j.rcim.2025.103141","url":null,"abstract":"<div><div>Intelligent process planning is pivotal in modern manufacturing systems, enabling efficient, precise, and flexible production by optimizing resource allocation, enhancing machining accuracy, and shortening production cycles. Knowledge graphs integrate multi-source heterogeneous data to support this process, yet traditional single-modal approaches hinder the exploration of complex relationships in multimodal data, falling short of the needs for complex part planning. This paper examines machining features, the foundational units of process planning, and introduces an automatic construction method for a Multimodal Process Knowledge Graph (MPKG) tailored to intelligent process planning, powered by Cross-Modal Transformers. We developed the MF36 dataset, encompassing 36 machining features with 3D models, engineering views, and descriptive texts. A cross-modal framework integrating LERT-CRF and PA-ViT models automates the extraction and fusion of multimodal process knowledge, with PA-ViT’s pooling attention mechanism markedly boosting machining feature recognition accuracy. Experiments demonstrate superior performance over baselines, achieving F1 scores of 0.895 in entity extraction and 0.877 in image recognition. A case study validates the method’s reliability for precise process recommendations, providing fresh insights into advancing intelligent process planning.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103141"},"PeriodicalIF":11.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093756","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

Design, modelling, and implementation of a novel parallel end effector for robotic grinding 一种新型机器人磨削并联末端执行器的设计、建模和实现

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-17 DOI: 10.1016/j.rcim.2025.103122

Zhisen Li , Yunong Li , Fei Liu , Peng Xu , Bing Li

引用次数: 0

Accurate localization and girth weld grinding planning for an in-pipe machining robot of thin-walled conical pipe 薄壁锥形管管内加工机器人的精确定位与环焊缝磨削规划

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-16 DOI: 10.1016/j.rcim.2025.103124

Tian Lan , Te Li , Haibo Liu , Shiyu Tian , Kuo Liu , Yongqing Wang

{"title":"Accurate localization and girth weld grinding planning for an in-pipe machining robot of thin-walled conical pipe","authors":"Tian Lan , Te Li , Haibo Liu , Shiyu Tian , Kuo Liu , 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":"2025-09-16","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}

引用次数: 0

Increasing dynamic accuracy of machine tools using predictive feedforward optimization with hybrid modeling 基于混合建模的预测前馈优化提高机床动态精度

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-16 DOI: 10.1016/j.rcim.2025.103137

Haijia Xu , Christoph Hinze , Andrea Iannelli , Zexu Zhou , Alexander Verl

{"title":"Increasing dynamic accuracy of machine tools using predictive feedforward optimization with hybrid modeling","authors":"Haijia Xu , Christoph Hinze , Andrea Iannelli , Zexu Zhou , Alexander Verl","doi":"10.1016/j.rcim.2025.103137","DOIUrl":"10.1016/j.rcim.2025.103137","url":null,"abstract":"<div><div>The paper presents an online optimization-based feedforward design framework using hybrid modeling to increase the dynamic accuracy of machine tools. Designed for use in dynamics simulation and feedforward compensation, the hybrid model combines a physics-based model of the multibody dynamics and a data-driven Gaussian process regressor of the output discrepancy. The feedforward control is based on the predictor–simulator separation, where the accurate but tractable nonlinear hybrid model is used for dynamics simulation, and the linearized predictor is adopted for optimal feedforward design with a receding horizon approach based on convex programming. This strategy allows the advanced modeling techniques to be used for real-time dynamics compensation in an open-loop fashion, where the associated convex optimization problem can be solved efficiently and reliably. We propose a methodological approach that covers the entire design procedure from dynamics modeling to control architecture selection and parameter tuning, providing an end-to-end strategy for practical applications. The algorithm is validated on a real-time industrial CNC machine, where the average computation time is 63 <span><math><mi>μ</mi></math></span>s on an Intel i5 CPU. Compared to the industry standard baseline feedforward control, the proposed feedforward framework reduces the mean absolute contour error by 46.1% and 56.8% for constant velocity tracking and freeform butterfly path following, respectively. Even with a mismatch of 30 % in the model parameters, the presented feedforward still reduces the error by 38.5% compared to the baseline.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103137"},"PeriodicalIF":11.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093792","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

Force-based adaptive deposition in multi-axis additive manufacturing: Low porosity for enhanced strength 多轴增材制造中基于力的自适应沉积：低孔隙率增强强度

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-13 DOI: 10.1016/j.rcim.2025.103123

Yuming Huang , Renbo Su , Kun Qian , Tianyu Zhang , Yongxue Chen , Tao Liu , Guoxin Fang , Weiming Wang , Charlie C.L. Wang

{"title":"Force-based adaptive deposition in multi-axis additive manufacturing: Low porosity for enhanced strength","authors":"Yuming Huang , Renbo Su , Kun Qian , Tianyu Zhang , Yongxue Chen , Tao Liu , Guoxin Fang , Weiming Wang , Charlie C.L. Wang","doi":"10.1016/j.rcim.2025.103123","DOIUrl":"10.1016/j.rcim.2025.103123","url":null,"abstract":"<div><div>Multi-axis additive manufacturing enhances mechanical strength by aligning printed layers with principal stress directions. However, this benefit introduces a key challenge: non-uniform layer thickness becomes inevitable due to surface curvature and deposition angle variations. Moreover, unpredictable errors in material deposition – such as inaccurate extrusion control, collapse of earlier deposited layers, or machine malfunctions – can accumulate throughout the build. These issues are difficult to model accurately in advance, making purely offline planning impractical for ensuring consistent print quality, especially in complex geometries. To address this issue, we propose a force-based adaptive deposition method that actively minimizes porosity during filament-based multi-axis AM. Our closed-loop control strategy dynamically adjusts the printhead’s motion speed based on real-time force feedback, while maintaining constant extrusion speed. Unlike geometry-driven offline planning approaches, our method compensates for thickness variation and process uncertainties, resulting in improved filament bonding. Experiments show up to a 72.1% increase in failure load compared to baseline methods, with similar or lower part weights. The approach also enhances robustness against extrusion irregularities, ensuring more consistent mechanical performance.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103123"},"PeriodicalIF":11.4,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047107","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

Robotic disassembly sequence dynamic planning under uncertain irremovable condition using dueling deep Q-network based on digital twin 基于数字孪生的深度q网络的不确定不可拆卸条件下机器人拆卸序列动态规划

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-11 DOI: 10.1016/j.rcim.2025.103132

Jiayi Liu , Zhenlu Xu , Wenjun Xu , Lei Qi , Yuning Han , Zude Zhou

{"title":"Robotic disassembly sequence dynamic planning under uncertain irremovable condition using dueling deep Q-network based on digital twin","authors":"Jiayi Liu , Zhenlu Xu , Wenjun Xu , Lei Qi , Yuning Han , Zude Zhou","doi":"10.1016/j.rcim.2025.103132","DOIUrl":"10.1016/j.rcim.2025.103132","url":null,"abstract":"<div><div>As an important step in recycling the end-of-life products, robotic disassembly can reduce human labor costs and robotic disassembly sequence planning helps to improve efficiency. The irremovable condition of components is uncertain and must be recognized during the robotic disassembly process. This uncertainty leads to the inapplicability of the optimal disassembly solution generated by pre-planning method, which impossibly considers the accurate irremovable condition prior to disassembly. To address the robotic disassembly sequence dynamic planning problem under the uncertain irremovable condition, this paper leverages a digital twin model and a dueling deep-Q network to dynamically generate the optimal solutions according to the recognized irremovable condition. First, a digital twin framework is proposed and the digital twin of the robotic disassembly process is built. Next, a dueling deep-Q network is utilized to solve the proposed problem. Case studies on a camera and a gear pump are conducted to validate the proposed method. Experimental analyses include the connection tests, the error assessments of the digital twin, and the performance evaluations of the algorithm under different scenarios. Results demonstrate that the trained model dynamically generates superior disassembly sequences tailored to the recognized irremovable condition within a reasonable time compared with the other algorithms.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103132"},"PeriodicalIF":11.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047106","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

Master-slave dual-arm learning from demonstration assembly based on modified triple reversible dynamic motion primitives 基于改进三可逆动态运动原语的示范装配主从双臂学习

IF 11.4 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-09-10 DOI: 10.1016/j.rcim.2025.103118

Xiangfei Li, Yuhui Jian, Huan Zhao, Yuwei Shan, Han Ding

{"title":"Master-slave dual-arm learning from demonstration assembly based on modified triple reversible dynamic motion primitives","authors":"Xiangfei Li, Yuhui Jian, Huan Zhao, Yuwei Shan, Han Ding","doi":"10.1016/j.rcim.2025.103118","DOIUrl":"10.1016/j.rcim.2025.103118","url":null,"abstract":"<div><div>For the peg-in-hole assembly tasks with small clearance, the traditional robot trajectory generation methods rely heavily on expert knowledge, which is complex and costly. In contrast, learning from demonstration method does not require expert knowledge, and can enable the robots to quickly learn and encode assembly trajectories. However, due to the rich contact states, easy jamming and collaborative constraints during the assembly process, dual arm learning from demonstration assembly remains a challenging task. For the reason, this paper first proposes a new dynamic motion primitives method that has both global asymptotic stability and reversibility, which can switch the forward and reverse directions of the trajectory generation process at any time to alleviate the jamming problem. Then, a uniform and concise robot pose synchronization description approach based on triple position reversible dynamic motion primitives is given. On this basis, through utilizing triple reversible dynamic motion primitives for the dual arm collaborative trajectory learning, and introducing slave-arm force coupling terms to modify them for trajectory compliance, a master-slave dual-arm learning from demonstration assembly algorithm is provided. Finally, based on two UR5 robots, a series of assembly experiments with three different shapes of pegs and holes are carried out, which confirm the effectiveness of the proposed method.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103118"},"PeriodicalIF":11.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027306","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