Robotics and Computer-integrated Manufacturing最新文献

{"title":"A hierarchical human behavior modeling framework for safe and efficient human-robot collaborative assembly","authors":"Guoyi Xia ,&nbsp;Zied Ghrairi ,&nbsp;Aaron Heuermann ,&nbsp;Klaus-Dieter Thoben","doi":"10.1016/j.rcim.2025.103202","DOIUrl":"10.1016/j.rcim.2025.103202","url":null,"abstract":"<div><div>Human-robot collaboration (HRC) offers promising solutions in industrial assembly by combining human dexterity and robot efficiency. However, the proximity of the human and robot raises safety concerns that can limit efficiency. Previous studies have typically addressed safety or efficiency separately, relying on incomplete models of human behavior, which has led to robot control strategies with limited adaptability. To bridge this gap, this paper proposes a hierarchical human behavior modeling framework that integrates human motion prediction (HMP) and human action segmentation. The proposed model captures both the fine-grained motion dynamics and the higher-level task structure, enabling a more complete and context-aware understanding of human behavior. The model can enhance robot decision-making for both proactive safety mechanisms and dynamic task allocation. HMP compares three predictive models (Convolutional Neural Networks - Long Short-Term Memory (CNN-LSTM), Spatial-Temporal Graph Convolutional Networks (ST-GCN), Transformer). CNN-LSTM and ST-GCN outperformed Transformer, demonstrating better short-term predictive accuracy. Human action segmentation includes feature extraction, dimensionality reduction, clustering, and two-stage temporal segmentation. The HMP (CNN-LSTM) based features achieve the highest clustering performance. Two-stage segmentation demonstrates high accuracy, achieving normalized edit distances (NED) of 0.029 and 0.07 for task-level and sub-task-level segmentation, respectively. Evaluation results show that proactive collision avoidance using predicted motions increased safety distance (from 0.4633 m to 0.4717 m), while dynamic task allocation based on action segmentation improves robot efficiency (from 84.95 % to 98.56 %). These results validate the effectiveness of the proposed hierarchical human behavior modeling framework in simultaneously enhancing safety and efficiency in HRC assembly.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103202"},"PeriodicalIF":11.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731795","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":"Target-oriented collision-free robot grasping using task-attendance teachers-student knowledge distillation for various dense-clutter scenarios","authors":"Shaodong Li ,&nbsp;Cheng Xiang ,&nbsp;Wei Du ,&nbsp;Xi Liu ,&nbsp;Huajian Song ,&nbsp;Feng Shuang","doi":"10.1016/j.rcim.2025.103201","DOIUrl":"10.1016/j.rcim.2025.103201","url":null,"abstract":"<div><div>The target-oriented collision-free robot grasping in dense-clutter scenarios has made significant progress. However, the prior studies primarily focus on the complex problem brought about by an increase in object quantity. Actually, the various scenarios will also result in the challenge. That means skill models will inevitably start to obtain bloat, as more subtasks appear. Our previous study presents a multi-teacher distillation strategy, thereby effectively avoiding bloat. Unfortunately, it still has trouble in execution capability when the scenarios have more subtasks and highly similar subtasks. The root cause is attributed to the inadequate capability of distilled student in subtask identification. It is naive to develop the subtask classifier based on the traditional data-driven way when human experience fails to work. Therefore, we propose a dataset-free classifier by migrating the classification capability of teachers to the student. Then, we further propose a task-attendance teachers-student knowledge distillation strategy to afford the various scenarios involving more and highly similar subtasks, thus significantly enhancing the performance of grasping. And we also leverage the language instruction to ensure the mask map of target object through LLM, improving the intuitiveness of human-robot cooperation. Extensive comparative experiment verifies the advantage of our framework. We measure the capability of teachers-student distillation, and value of dataset-free classifier in our framework. Importantly, the performance of segmentation strategy is tested under ambiguous instruction, visual occlusion, and color conflict. Furthermore, the excellent generalization and robustness are exhibited according to the real-world experiments involving unseen object, unseen task modality, and disturbance existing.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103201"},"PeriodicalIF":11.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731770","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":"Design of support head with joint-synchronized structure and tuned mass damper for robotic mirror milling","authors":"Kun Chen ,&nbsp;Chenghao Huang ,&nbsp;Haonan Ma ,&nbsp;Peng Xu ,&nbsp;Bing Li","doi":"10.1016/j.rcim.2025.103200","DOIUrl":"10.1016/j.rcim.2025.103200","url":null,"abstract":"<div><div>Machined thin-walled parts are critical to lightweight aerospace structures; however, their low stiffness poses significant challenges in controlling deformation and vibrations during their machining. To counteract these force-induced errors, robotic mirror milling has emerged as a promising technique, because a support robot can actively neutralize the cutting forces. However, the support robot requires an appropriate end-effector to protect the workpiece surface, while providing stable support. In this study, a support head with plastic rollers and a multi-branched tuned mass damper (TMD) is developed. The support head, synchronized with the Joint 6 of the support robot, contacts the thin-walled part via plastic rollers. A follow-up motion plan algorithm is proposed for the support head to synchronize its motion with the milling tool. The multi-branched TMD is designed to suppress the vibrations of the thin-walled parts and support head in different directions, thus ensuring an effective contact between them. A frequency-response function model for the TMD is proposed for parameter design. Simulations and experiments confirm that the designed motion-planning algorithm reduces the velocity of Joint 6 by 78.6 %, while the TMD decreases the machined surface roughness by 32.8 % and 21.4 % at axial depths of cut of 1.0 and 1.5 mm, respectively. The results demonstrate that the joint-synchronized support head, combined with the multi-branched TMD, effectively ensures stable support to the workpiece, while preventing damage to its surface.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103200"},"PeriodicalIF":11.4,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697314","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":"Time–torque coordinated optimization for trajectory planning of industrial robots","authors":"Zeyun Xiao,&nbsp;Danfeng Sun,&nbsp;Donglai Zhu,&nbsp;Yong Wang,&nbsp;Yi Yan,&nbsp;Huifeng Wu","doi":"10.1016/j.rcim.2025.103199","DOIUrl":"10.1016/j.rcim.2025.103199","url":null,"abstract":"<div><div>Trajectory optimization is vital for improving the operational efficiency and reliability of industrial robotic arms. However, increasing task execution speed frequently induces sharp fluctuations in joint torque, which elevates energy consumption, places excessive stress on actuators, and excites structural vibrations. To tackle these issues, this study introduces a unified time–torque optimization framework that combines predictive modeling with heuristic search. The framework adopts a neural network incorporating frequency-domain correlation and a delay-aware mechanism to model dynamic torque variations. Based on the predicted torque profiles, the robot’s motion trajectory is parameterized by quintic polynomials, and a multi-objective loss function is constructed to jointly minimize execution time and torque variation rate. Particle Swarm Optimization (PSO) is employed to perform a global search for intermediate joint velocities and accelerations, improving convergence toward near-optimal solutions. Experiments on a six-axis industrial robotic platform demonstrate that the proposed method effectively reduces execution time and smooths torque transitions, confirming its practicality for industrial applications.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103199"},"PeriodicalIF":11.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651051","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 lightweight detection network integrating multi-scale semantic refinement for steel strip defects","authors":"Baicheng Bian ,&nbsp;Long Chen ,&nbsp;Zongwang Han ,&nbsp;Shiqing Wu ,&nbsp;WeiDong Li ,&nbsp;Hongguang Chen","doi":"10.1016/j.rcim.2025.103188","DOIUrl":"10.1016/j.rcim.2025.103188","url":null,"abstract":"<div><div>Real-time surface defect detection in steel strips requires high accuracy under computational constraints. Existing methods often struggle to detect low-contrast defects in complex backgrounds while maintaining computational efficiency. This paper proposes MSR-Det, a lightweight network built on a multi-scale semantic refinement framework to address this challenge. The core innovations include: a Hierarchical Texture Enhancement Module (HTEM) that strengthens discriminative textural patterns, a Specific Cross-Level Interaction Module (SCLIM) that facilitates granular feature fusion across network depths, and a Feature Enhancement Module (FEM) that refines defect representations through deep-to-shallow semantic guidance. The architecture further incorporates two key modules: a Lightweight Large-Kernel Bottleneck Module (LLKBM) for efficient receptive field expansion, and an Adaptive Gating Spatial Pyramid Pooling (AGSPP) for dynamic multi-scale context integration. Extensive experiments on NEU-DET, GC10-DET, and our industrial SS-DET dataset demonstrate that MSR-Det achieves state-of-the-art performance of 86.6% Mean Average Precision at 50% Intersection over Union (<span><math><msub><mrow><mtext>mAP</mtext></mrow><mrow><mn>50</mn></mrow></msub></math></span>) on SS-DET with only 5.0M parameters, also attaining a real-time speed of 180 FPS. This work provides a robust and practical solution for automated visual inspection in industrial settings, effectively balancing high precision with operational efficiency.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103188"},"PeriodicalIF":11.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614038","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":"Dual-service combination optimization of manufacturing and logistics: models for self-managed and third-party logistics in cloud manufacturing","authors":"Chunhua Tang ,&nbsp;Shuangyao Zhao ,&nbsp;Ting Huang ,&nbsp;Mark Goh","doi":"10.1016/j.rcim.2025.103178","DOIUrl":"10.1016/j.rcim.2025.103178","url":null,"abstract":"<div><div>Service combination (SC) is a critical technique in cloud manufacturing, enabling the integration of multiple services to deliver value-added solutions. Logistics plays a pivotal role in SC by ensuring seamless coordination across various manufacturing stages, thereby maximizing the efficiency of production flows. This implies that the SC process must integrate both manufacturing services (MSs) and logistics services (LSs) to determine the optimal combination strategy. Prior research has focused mainly on MS performance, often overlooking the critical impact of logistics on SC outcomes. Although some studies have incorporated logistics considerations, they have largely treated logistics attributes as secondary components of MS evaluations or adopted linear aggregation methods to jointly configure MSs and LSs. These approaches fail to capture the dynamic nature of logistics performance and the interdependencies between MSs and LSs. To address these gaps, this study develops two optimization models for SC that integrate both MSs and LSs, tailored for self-managed and third-party logistics modes. In particular, an innovative bi-level optimization model is introduced to capture the sequential dependencies and dynamic interactions between MSs and LSs in logistics outsourcing, ensuring seamless integration. The upper level focuses on optimizing the MS selection, while the lower level identifies the optimal LSs based on the determined MSs. Improved genetic algorithms incorporating adaptive and parallel mechanisms are developed to address the models, dynamically adjusting parameters to improve solution accuracy and efficiency. Case studies and numerical experiments validate the effectiveness of the proposed models and algorithms, offering actionable managerial insights grounded in the results.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103178"},"PeriodicalIF":11.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614037","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 drawings to decisions: A hybrid vision-language framework for parsing 2D engineering drawings into structured manufacturing knowledge","authors":"Muhammad Tayyab Khan ,&nbsp;Lequn Chen ,&nbsp;Zane Yong ,&nbsp;Jun Ming Tan ,&nbsp;Wenhe Feng ,&nbsp;Seung Ki Moon","doi":"10.1016/j.rcim.2025.103186","DOIUrl":"10.1016/j.rcim.2025.103186","url":null,"abstract":"<div><div>Efficient and accurate extraction of key information from 2D engineering drawings is essential for advancing digital manufacturing workflows. This information includes elements such as geometric dimensioning and tolerancing (GD&amp;T), measures, material specifications, and textual annotations. Manual extraction remains slow and labor-intensive, while generic optical character recognition (OCR) models often fail to interpret 2D drawings accurately due to complex layouts, engineering symbols, and rotated annotations. These limitations result in incomplete and unreliable outputs. To address these challenges, this paper proposes a hybrid vision-language framework that integrates a rotation-aware object detection model (YOLOv11-obb) with a transformer-based vision-language parser. We introduce a structured parsing pipeline that first applies YOLOv11-obb to localize annotations and extract oriented bounding box (OBB) image patches, which are subsequently parsed into structured outputs using a fine-tuned, lightweight vision-language model (VLM). To develop and evaluate this pipeline, we curate a dataset of 1367 2D mechanical drawings manually annotated across nine key categories: GD&amp;Ts, General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. YOLOv11-obb is trained on this dataset to detect OBBs and extract annotation patches. These image patches are then parsed using two fine-tuned open-source VLMs. The first is Donut, a transformer-based model that combines a Swin-B visual encoder with a BART text decoder, enabling end-to-end parsing directly from images without relying on OCR. The second is Florence-2, a prompt-driven encoder–decoder model that integrates a DaViT vision backbone and supports structured output generation through multimodal token alignment. Both models are lightweight and well-suited for specialized industrial tasks under limited computational overhead. Following fine-tuning of both models on the curated dataset of image patches paired with structured annotation labels, a comparative experiment is conducted to evaluate parsing performance across four key metrics. Donut outperforms Florence-2, achieving 89.2 % precision, 99.2 % recall, and a 94 % F1-score, with a hallucination rate of 10.8 %. Finally, a case study demonstrates how the extracted structured information supports downstream manufacturing tasks such as process and tool selection, showcasing the practical utility of the proposed framework in modernizing 2D drawing interpretation.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103186"},"PeriodicalIF":11.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614041","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 Novel Pivot-Move Strategy for Dual-Robot Manipulator Additive Manufacturing: Enabling Collision Avoidance without Halting Deposition","authors":"C.L. Li ,&nbsp;Y.C. Jiao ,&nbsp;K. Ren ,&nbsp;N. Liu ,&nbsp;Y.F. Zhang","doi":"10.1016/j.rcim.2025.103177","DOIUrl":"10.1016/j.rcim.2025.103177","url":null,"abstract":"<div><div>Robot-assisted additive manufacturing (AM) has been gaining increasing popularity due to its great flexibility and reachability. Moreover, an AM system with dual deposition-heads held by robot manipulators would significantly shorten the building time, especially for large-scale parts. However, motion planning (MP) for the dual robot manipulators AM is highly challengeable due to various constraints imposed by the setup and the AM process aiming to improve the qualities of the component, e.g., maintaining travelling speed and posture of the deposition head and avoiding collision. In this paper, a novel pivot-move strategy is proposed for MP in AM with dual robot manipulators. Given the sequenced deposition toolpath segments to each deposition head, an initial MP solution including robot configuration at each time sample waypoint is firstly generated for each robot manipulator, respectively. This is followed by conducting a check-and-correct process at each waypoint, where the collision among the links of two robot manipulators is identified and corrected. Specially, the robot manipulator is designed to simultaneously pivot and move to avoid the collision while maintaining the traveling speed unchanged. Numerical simulation, physical implementation, and benchmarking were conducted to exhibit a 78.295% deposition time reduction and high-quality deposition with the developed strategy. To the best of the authors' knowledge, this study represents the pioneering effort in addressing the collision issue in dual robot manipulators depositing on the same heated bed, achieving collision avoidance without interrupting the ongoing deposition process. It can be a valuable supplement to the state of the art in this area.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103177"},"PeriodicalIF":11.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611798","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 robotic framework for high-throughput and multi-view 3D digital image correlation (3D-DIC): Increasing measurement volume and versatility for deformation analysis","authors":"Özgüç Bertuğ Çapunaman ,&nbsp;Alale Mohseni ,&nbsp;Dennis Dombrovskij ,&nbsp;Kaiyang Yin ,&nbsp;Benay Gürsoy ,&nbsp;Max David Mylo","doi":"10.1016/j.rcim.2025.103187","DOIUrl":"10.1016/j.rcim.2025.103187","url":null,"abstract":"<div><div>Three-dimensional digital image correlation (3D-DIC) is a widely applicable, non-contact optical imaging technique for accurately quantifying full-field surface displacements and strains in materials and structures. However, conventional 3D-DIC implementations relying on fixed stereo camera positions face trade-offs between the field-of-view and spatial resolution and lack high-throughput for long-duration measurements. Here we present an integrated robotic 3D-DIC framework that employs an industrial robotic arm to autonomously and repeatedly reposition stereo cameras. This enables automated calibration, monitoring of multiple samples over extended periods, and expansion of the effective spatial coverage and data throughput, all while maintaining calibration stability and measurement fidelity. We validate this approach on rigid and deforming reference samples and demonstrate its ability to quantify material deformation of bio-composite samples simultaneously during the drying process. Under robotic repositioning, rigid samples exhibit stable displacement and strain measurements while benefiting from significantly increased volumetric coverage and reduced manual oversight. Thus, the proposed system improves experimental efficiency and allows for the incorporation of advanced techniques, such as multi-view stitching, to characterize complex geometries with higher effective resolution. When applied to slowly deforming bio-composites, the system can capture time-lapse images from multiple viewpoints, providing a more comprehensive assessment of complex, evolving material behaviors. These enhancements in 3D-DIC further improve geometric accuracy, increase data density, and expand applicability to a broader range of materials and experimental conditions. Ultimately, the proposed robot-assisted 3D-DIC system creates a robust, high-throughput monitoring framework for bio-fabrication, additive manufacturing, and advanced composite processing, paving the way for targeted programming of shape changes, among other applications.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"99 ","pages":"Article 103187"},"PeriodicalIF":11.4,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595008","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 lightweight object detection approach for precision gripping in multiple peg-in-hole assembly tasks","authors":"Jianjun Jiao ,&nbsp;Zonggang Li ,&nbsp;Guangqing Xia ,&nbsp;Guoping Wang ,&nbsp;Yinjuan Chen ,&nbsp;Ruibing Gao","doi":"10.1016/j.rcim.2025.103185","DOIUrl":"10.1016/j.rcim.2025.103185","url":null,"abstract":"<div><div>Automating product assembly using manipulators in manufacturing remains challenging. This is mainly because detection and gripping prior to component assembly still depend heavily on manual operations and traditional teaching methods, resulting in a low overall level of automation. The primary difficulty in detection and gripping arises from the precise recognition of rotation angles and the complex demands for accuracy, real-time performance, and stability. This paper presents an improved lightweight model, IDPC-YOLOv8, for multiple peg-in-hole workpiece detection and gripping to address these challenges. The proposed approach integrates adaptive image preprocessing to enhance visual clarity under varying lighting conditions and employs an efficient network architecture that jointly exploits global and local features to improve detection precision and computational efficiency. In addition, a rotation-aware detection strategy is introduced to enable accurate prediction of object orientation. Moreover, a network optimization scheme further reduces model parameters, making the system suitable for real-time deployment. Experimental results reveal that the IDPC-YOLOv8 model achieves an accuracy of 97.8% and a detection speed of 126.59 FPS, representing improvements of 4% and 8.3%, respectively, over the original YOLOv8-OBB model. Compared to several state-of-the-art rotation detection models, IDPC-YOLOv8 demonstrates superior integration and generalization capabilities. The effectiveness of the proposed method is further validated through excellent gripping success rates achieved in real-world experiments using the AUBO-i5 manipulator.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"98 ","pages":"Article 103185"},"PeriodicalIF":11.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567450","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}