Pub Date : 2026-10-01Epub Date: 2026-02-28DOI: 10.1016/j.rcim.2026.103280
Kai Ding , Qingyuan Mao , Yaqian Zhang , Yirong Zhang , Pai Zheng , Lihui Wang
Industry 5.0 represents a paradigm shift from efficiency-oriented automation to human-centric, resilient, and sustainable manufacturing, where human–robot collaboration (HRC) plays a crucial role by combining human flexibility with robotic precision. However, current HRC systems remain reactive and fragmented, lacking the alignment across perception, cognition, and execution required for seamless collaboration and robust generalization. While generative large models (GLMs) are emerging as a promising solution to these challenges, their integration into HRC exhibits a notable temporal lag compared to robotic domains, necessitating a systematic cross-domain synergy. This paper presents a review of GLM-enhanced HRC and proposes a prospective blueprint of multimodal perception, mutual cognition, and embodied execution for HRC in Industry 5.0. This blueprint outlines potential pathways toward human-centric smart manufacturing by synergizing generative artificial intelligence and embodied intelligence.
{"title":"Review and perspectives on multimodal perception, mutual cognition, and embodied execution for human–robot collaboration in Industry 5.0","authors":"Kai Ding , Qingyuan Mao , Yaqian Zhang , Yirong Zhang , Pai Zheng , Lihui Wang","doi":"10.1016/j.rcim.2026.103280","DOIUrl":"10.1016/j.rcim.2026.103280","url":null,"abstract":"<div><div>Industry 5.0 represents a paradigm shift from efficiency-oriented automation to human-centric, resilient, and sustainable manufacturing, where human–robot collaboration (HRC) plays a crucial role by combining human flexibility with robotic precision. However, current HRC systems remain reactive and fragmented, lacking the alignment across perception, cognition, and execution required for seamless collaboration and robust generalization. While generative large models (GLMs) are emerging as a promising solution to these challenges, their integration into HRC exhibits a notable temporal lag compared to robotic domains, necessitating a systematic cross-domain synergy. This paper presents a review of GLM-enhanced HRC and proposes a prospective blueprint of multimodal perception, mutual cognition, and embodied execution for HRC in Industry 5.0. This blueprint outlines potential pathways toward human-centric smart manufacturing by synergizing generative artificial intelligence and embodied intelligence.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103280"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330050","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 : 2026-10-01Epub Date: 2026-03-05DOI: 10.1016/j.rcim.2026.103293
Yingchao You , Ze Ji , Changyun Wei
Task planning plays a pivotal role in ensuring the smooth collaboration between humans and robots by efficiently allocating tasks among agents and scheduling available resources. Although some recently proposed task planners incorporate human factors into their frameworks, few explicitly account for human-related uncertainties, which can potentially lead to task failures. To address this gap, this study introduces a physical exertion–aware task planner that explicitly considers uncertainties in both human factors and task execution time. The uncertainties associated with physical exertion and execution time are modelled using the Single-Valued Triangular Neutrosophic (SVTN) Number method. Furthermore, a reinforcement learning-based approach is developed to learn adaptive task allocation policies and scheduling under these uncertainties. The experimental results indicate that the reinforcement learning-based approach effectively reduces performance variability compared with the benchmark methods.
{"title":"Towards human-centric manufacturing: Task planning under uncertainties in human–robot collaborative assembly","authors":"Yingchao You , Ze Ji , Changyun Wei","doi":"10.1016/j.rcim.2026.103293","DOIUrl":"10.1016/j.rcim.2026.103293","url":null,"abstract":"<div><div>Task planning plays a pivotal role in ensuring the smooth collaboration between humans and robots by efficiently allocating tasks among agents and scheduling available resources. Although some recently proposed task planners incorporate human factors into their frameworks, few explicitly account for human-related uncertainties, which can potentially lead to task failures. To address this gap, this study introduces a physical exertion–aware task planner that explicitly considers uncertainties in both human factors and task execution time. The uncertainties associated with physical exertion and execution time are modelled using the Single-Valued Triangular Neutrosophic (SVTN) Number method. Furthermore, a reinforcement learning-based approach is developed to learn adaptive task allocation policies and scheduling under these uncertainties. The experimental results indicate that the reinforcement learning-based approach effectively reduces performance variability compared with the benchmark methods.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103293"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360715","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 : 2026-10-01Epub Date: 2026-03-02DOI: 10.1016/j.rcim.2026.103273
Qiang Cui , Chuan Yu , Daoqian Yang , Jiangshan Li , Chunyang Yu
Wire Arc Additive Manufacturing (WAAM) enables efficient fabrication of large-scale electric vehicle (EV) structures, yet its integration with Discrete Topology Optimization (DTO) is often limited by static and conservative manufacturability constraints. This study presents a dual-loop framework that tightly couples DTO with WAAM through adaptive constraint refinement and in-situ process feedback. An inner loop performs real-time path compensation and process parameter adjustment based on geometric deviation monitoring, while an outer loop updates inclination-based manufacturability constraints using accumulated fabrication knowledge. Printability is characterized by minimum self-supporting and maximum compensable angle thresholds, allowing manufacturability to be modeled as a graded design variable. Both hard and soft constraint strategies are incorporated into the DTO formulation to regulate overhang-sensitive members. A full-scale electric vehicle chassis is used as a running case throughout the paper to demonstrate the proposed framework, spanning constrained DTO, deposition experiments, and robotic WAAM fabrication, and showing improved printability while preserving load-efficient topologies.
{"title":"A dual-loop framework for manufacturability-aware topology optimization of electric vehicle structures via wire arc additive manufacturing","authors":"Qiang Cui , Chuan Yu , Daoqian Yang , Jiangshan Li , Chunyang Yu","doi":"10.1016/j.rcim.2026.103273","DOIUrl":"10.1016/j.rcim.2026.103273","url":null,"abstract":"<div><div>Wire Arc Additive Manufacturing (WAAM) enables efficient fabrication of large-scale electric vehicle (EV) structures, yet its integration with Discrete Topology Optimization (DTO) is often limited by static and conservative manufacturability constraints. This study presents a dual-loop framework that tightly couples DTO with WAAM through adaptive constraint refinement and <em>in-situ</em> process feedback. An inner loop performs real-time path compensation and process parameter adjustment based on geometric deviation monitoring, while an outer loop updates inclination-based manufacturability constraints using accumulated fabrication knowledge. Printability is characterized by minimum self-supporting and maximum compensable angle thresholds, allowing manufacturability to be modeled as a graded design variable. Both hard and soft constraint strategies are incorporated into the DTO formulation to regulate overhang-sensitive members. A full-scale electric vehicle chassis is used as a running case throughout the paper to demonstrate the proposed framework, spanning constrained DTO, deposition experiments, and robotic WAAM fabrication, and showing improved printability while preserving load-efficient topologies.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103273"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360728","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 : 2026-10-01Epub Date: 2026-03-11DOI: 10.1016/j.rcim.2026.103295
Suyog Ghungrad , Reihane Arabpoor , Sean Rescsanski , Farhad Imani , Azadeh Haghighi
Energy‑aware path planning is central to robotic manufacturing, as it demands accurate, low‑latency predictions of both trajectory feasibility and energy consumption. Physics‑based estimators are accurate but slow and platform‑specific, while existing learned surrogates are fast yet often mis‑score infeasible paths and transfer poorly across robot models. We present Kinematics-Guided Multi-Task (KG-MT), a kinematics‑aware architecture that jointly performs prediction of path feasibility and energy consumption during robotic additive manufacturing processes. By injecting inverse kinematics as a shared computational foundation into a shared backbone, KG‑MT internalizes reachability, joint and velocity limits, as well as local Jacobian conditioning, yielding features that benefit both tasks. We tune hyperparameters via Bayesian optimization and study two adaptation regimes, architecture‑only and architecture‑with‑weights transfer, to reduce target‑data needs and training time. Comprehensive evaluations under homogeneous and heterogeneous robot scenarios show that the proposed model not only outperforms traditional two-stage pipelines but also drastically reduces computation time while maintaining high prediction accuracy. In cross-robot transfer tests, KG-MT achieves 97.98 % feasibility accuracy with 2.60 % energy MAE in the homogeneous transfer setting and 96.82 % accuracy with 3.79 % MAE in the heterogeneous setting. Critically, for real-world additive manufacturing applications, KG-MT performs 312 times faster than analytical simulations and 2.2 times faster than cascaded neural network surrogates. KG‑MT provides a practical foundation for cross‑platform, energy‑aware planning in robotic manufacturing, supporting path optimization, robot placement, and sustainable operations, and is readily extensible to additional objectives (e.g., jerk or thermal constraints) without re‑architecting the model.
{"title":"Kinematics-guided multi-task learning for transferable models in robotic manufacturing","authors":"Suyog Ghungrad , Reihane Arabpoor , Sean Rescsanski , Farhad Imani , Azadeh Haghighi","doi":"10.1016/j.rcim.2026.103295","DOIUrl":"10.1016/j.rcim.2026.103295","url":null,"abstract":"<div><div>Energy‑aware path planning is central to robotic manufacturing, as it demands accurate, low‑latency predictions of both trajectory feasibility and energy consumption. Physics‑based estimators are accurate but slow and platform‑specific, while existing learned surrogates are fast yet often mis‑score infeasible paths and transfer poorly across robot models. We present Kinematics-Guided Multi-Task (KG-MT), a kinematics‑aware architecture that jointly performs prediction of path feasibility and energy consumption during robotic additive manufacturing processes. By injecting inverse kinematics as a shared computational foundation into a shared backbone, KG‑MT internalizes reachability, joint and velocity limits, as well as local Jacobian conditioning, yielding features that benefit both tasks. We tune hyperparameters via Bayesian optimization and study two adaptation regimes, architecture‑only and architecture‑with‑weights transfer, to reduce target‑data needs and training time. Comprehensive evaluations under homogeneous and heterogeneous robot scenarios show that the proposed model not only outperforms traditional two-stage pipelines but also drastically reduces computation time while maintaining high prediction accuracy. In cross-robot transfer tests, KG-MT achieves 97.98 % feasibility accuracy with 2.60 % energy MAE in the homogeneous transfer setting and 96.82 % accuracy with 3.79 % MAE in the heterogeneous setting. Critically, for real-world additive manufacturing applications, KG-MT performs 312 times faster than analytical simulations and 2.2 times faster than cascaded neural network surrogates. KG‑MT provides a practical foundation for cross‑platform, energy‑aware planning in robotic manufacturing, supporting path optimization, robot placement, and sustainable operations, and is readily extensible to additional objectives (e.g., jerk or thermal constraints) without re‑architecting the model.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103295"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387692","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 : 2026-10-01Epub Date: 2026-03-06DOI: 10.1016/j.rcim.2026.103290
Ruikai Liu , Ruiqi Li , Qingwei Dong , Guangxi Wan , Maowei Jiang , Yifan Wang , Peng Zeng
Symbolic planning for manufacturing robotics is undermined by brittle domain models. While Large Language Models can generate PDDL (Planning Domain Definition Language) domains from language, they often introduce subtle flaws like weak preconditions or incomplete effects. These flaws create a critical semantic gap where syntactically correct plans fail in physical execution, posing a major challenge to robot reliability. We introduce a framework for trajectory-guided domain repair that systematically aligns symbolic models with physical reality. Its two-stage feedback loop first uses an Iterative Beam Widening search to select a compact, informative set of trajectories, minimizing the interaction cost—i.e., the number of environment interactions (EI). Second, it performs failure attribution for execution errors — distinguishing between flawed preconditions and upstream effects — and generates structured hints to guide the LLM’s repair. Validated across twelve benchmarks, including an industrial simulation and a physical robot, our framework achieves a state-of-the-art execution success rate of 71.2%, outperforming all compared baselines under the same evaluation protocol. Notably, this performance is obtained with substantially lower interaction cost, requiring an average of 231 EI per task, which corresponds to a near order-of-magnitude reduction compared to the 2014 EI required by exploration-based methods. Our results highlight a practical path toward bridging the gap between high-level symbolic reasoning and robust physical execution, enhancing the reliability of LLM-driven automation in complex manufacturing environments.
{"title":"Bridging the semantic gap: Trajectory-guided domain repair for reliable planning","authors":"Ruikai Liu , Ruiqi Li , Qingwei Dong , Guangxi Wan , Maowei Jiang , Yifan Wang , Peng Zeng","doi":"10.1016/j.rcim.2026.103290","DOIUrl":"10.1016/j.rcim.2026.103290","url":null,"abstract":"<div><div>Symbolic planning for manufacturing robotics is undermined by brittle domain models. While Large Language Models can generate PDDL (Planning Domain Definition Language) domains from language, they often introduce subtle flaws like weak preconditions or incomplete effects. These flaws create a critical semantic gap where syntactically correct plans fail in physical execution, posing a major challenge to robot reliability. We introduce a framework for trajectory-guided domain repair that systematically aligns symbolic models with physical reality. Its two-stage feedback loop first uses an Iterative Beam Widening search to select a compact, informative set of trajectories, minimizing the interaction cost—i.e., the number of environment interactions (EI). Second, it performs failure attribution for execution errors — distinguishing between flawed preconditions and upstream effects — and generates structured hints to guide the LLM’s repair. Validated across twelve benchmarks, including an industrial simulation and a physical robot, our framework achieves a state-of-the-art execution success rate of 71.2%, outperforming all compared baselines under the same evaluation protocol. Notably, this performance is obtained with substantially lower interaction cost, requiring an average of 231 EI per task, which corresponds to a near order-of-magnitude reduction compared to the 2014 EI required by exploration-based methods. Our results highlight a practical path toward bridging the gap between high-level symbolic reasoning and robust physical execution, enhancing the reliability of LLM-driven automation in complex manufacturing environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103290"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387694","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 : 2026-10-01Epub Date: 2026-03-09DOI: 10.1016/j.rcim.2026.103291
Siyu Liu , Mengzhen Liu , Zhiyuan Ming , Yilun Huang , Lingfei Ma , Deyu Zhang , Yifan Song , Jian Zhang , Tianyi Yan
This paper proposes an innovative human–robot interaction (HRI) framework called Copilot, which aims to bridge the gap between human intent and robot intelligence. Currently, existing HRI systems struggle to infer human intentions and rely heavily on predefined rules, a limitation that significantly hinders the advancement of the field. To address this issue, the Copilot framework, for the first time, integrates the environmental understanding capabilities of large language models (LLMs) with the intention recognition advantages of brain-machine interface (BMI). It constructs three core modules: (1) a LLM-based visual evoked potential (LLM-VEP) paradigm module utilizing LLM for scene understanding and dynamic marking; (2) a BMI module employing the blink-triggered multivariate variational mode decomposition with canonical correlation analysis (BT-MVMD-CCA) algorithm; and (3) an intelligent agent flexibly adapting to different task requirements. Through online experimental validation with 12 participants, the system performed optimally when using the EEG-based double blink triggering (EEG-DBT) method: 0% false trigger rate, 94.09% blink detection rate, and 84.00% task completion rate. In offline experiments, the proposed BT-MVMD-CCA algorithm achieved 92.3% classification accuracy and a peak information transfer rate (ITR) of 71.1 bits/min at DTW = 1.5 s. This research not only provides theoretical support for the HRI field, but also offers promising solutions for assistive robotics and manufacturing scenarios.
{"title":"Copilot: A framework for integrating LLM and BMI to enhance human–robot interaction","authors":"Siyu Liu , Mengzhen Liu , Zhiyuan Ming , Yilun Huang , Lingfei Ma , Deyu Zhang , Yifan Song , Jian Zhang , Tianyi Yan","doi":"10.1016/j.rcim.2026.103291","DOIUrl":"10.1016/j.rcim.2026.103291","url":null,"abstract":"<div><div>This paper proposes an innovative human–robot interaction (HRI) framework called Copilot, which aims to bridge the gap between human intent and robot intelligence. Currently, existing HRI systems struggle to infer human intentions and rely heavily on predefined rules, a limitation that significantly hinders the advancement of the field. To address this issue, the Copilot framework, for the first time, integrates the environmental understanding capabilities of large language models (LLMs) with the intention recognition advantages of brain-machine interface (BMI). It constructs three core modules: (1) a LLM-based visual evoked potential (LLM-VEP) paradigm module utilizing LLM for scene understanding and dynamic marking; (2) a BMI module employing the blink-triggered multivariate variational mode decomposition with canonical correlation analysis (BT-MVMD-CCA) algorithm; and (3) an intelligent agent flexibly adapting to different task requirements. Through online experimental validation with 12 participants, the system performed optimally when using the EEG-based double blink triggering (EEG-DBT) method: 0% false trigger rate, 94.09% blink detection rate, and 84.00% task completion rate. In offline experiments, the proposed BT-MVMD-CCA algorithm achieved 92.3% classification accuracy and a peak information transfer rate (ITR) of 71.1 bits/min at DTW = 1.5 s. This research not only provides theoretical support for the HRI field, but also offers promising solutions for assistive robotics and manufacturing scenarios.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103291"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387693","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 : 2026-10-01Epub Date: 2026-03-06DOI: 10.1016/j.rcim.2026.103272
Shaban Usman , Tianrun Ye , Haotian Xue , Lei Liu , Weiwei Qin , Ping Zhang , Ailong Yuan , Chueh Ting , Yanli Gong , Chunming Gao
The dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) addresses practical challenges in modern production systems, especially where human and robotic resources are jointly managed. This study proposes a DRCFJSP model with ergonomic consideration (DRCFJSP-ER), aiming to simultaneously enhance productivity and the well-being of workers in both conventional and human-robot systems. Ergonomic load in a job-shop environment is assessed using the rapid upper limb assessment (RULA) score by introducing three novel evaluation metrics: the weighted average RULA score for operations, the cumulative RULA score for operations, and the cumulative RULA score for the entire job-shop cycle. To efficiently solve the DRCFJSP-ER, we propose an enhanced NSGA-II with teaching-learning effect (ENSGA-TL) to simultaneously minimize the makespan and maximum cumulative RULA score. A comprehensive analysis based on standard performance metrics is conducted to evaluate the effectiveness of ENSGA-TL for DRCFJSP-ER using newly generated test instances. Additionally, two real-world case studies in an agricultural production environment, selected for their labor-intensive and robotics-relevant characteristics, demonstrate the model’s effectiveness and adaptability to conventional and smart robotic production systems. The results validate the potential of the DRCFJSP-ER model and the ENSGA-TL algorithm in improving production efficiency and protecting worker well-being.
{"title":"Dual-Resource constrained flexible job-shop scheduling with ergonomic considerations in conventional and human-robot systems using an enhanced NSGA-II with teaching-learning effect","authors":"Shaban Usman , Tianrun Ye , Haotian Xue , Lei Liu , Weiwei Qin , Ping Zhang , Ailong Yuan , Chueh Ting , Yanli Gong , Chunming Gao","doi":"10.1016/j.rcim.2026.103272","DOIUrl":"10.1016/j.rcim.2026.103272","url":null,"abstract":"<div><div>The dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) addresses practical challenges in modern production systems, especially where human and robotic resources are jointly managed. This study proposes a DRCFJSP model with ergonomic consideration (DRCFJSP-ER), aiming to simultaneously enhance productivity and the well-being of workers in both conventional and human-robot systems. Ergonomic load in a job-shop environment is assessed using the rapid upper limb assessment (RULA) score by introducing three novel evaluation metrics: the weighted average RULA score for operations, the cumulative RULA score for operations, and the cumulative RULA score for the entire job-shop cycle. To efficiently solve the DRCFJSP-ER, we propose an enhanced NSGA-II with teaching-learning effect (ENSGA-TL) to simultaneously minimize the makespan and maximum cumulative RULA score. A comprehensive analysis based on standard performance metrics is conducted to evaluate the effectiveness of ENSGA-TL for DRCFJSP-ER using newly generated test instances. Additionally, two real-world case studies in an agricultural production environment, selected for their labor-intensive and robotics-relevant characteristics, demonstrate the model’s effectiveness and adaptability to conventional and smart robotic production systems. The results validate the potential of the DRCFJSP-ER model and the ENSGA-TL algorithm in improving production efficiency and protecting worker well-being.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103272"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387361","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 : 2026-10-01Epub Date: 2026-03-11DOI: 10.1016/j.rcim.2026.103294
Yong Tao , Jiao Xue , Yazui Liu , Lin Yang , Jiewu Leng , Pai Zheng , Baicun Wang , Xiaotong Wang , Hongxing Wei
During the grinding of aeroengine blade edges, complex time-varying nonlinear coupling and uncertain disturbances pose challenges to the adaptive regulation of constant force grinding, reducing process stability and precision. This paper proposed a multi-modal fusion-enhanced fuzzy adaptive variable impedance control with improved deep belief network (DBN) for robotic constant force blade grinding. Specifically, the three-dimensional model and point cloud model of the blade are integrated to extract accurate geometric information and generate reference grinding trajectories. Furtherly, the DBN training hyperparameters are optimized using linear success history-based adaptive differential evolution (LSHADE). This improves the DBN configuration and overcomes the limitations of conventional DBN based force compensation with fixed network structures and single modality inputs. On this basis, a fuzzy adaptive variable impedance control method based on the improved DBN is developed. Geometric, force/pose, and error modalities are fused to dynamically adjust the force compensation term. This design enables the controller to outperform conventional adaptive variable impedance methods under strongly time-varying conditions. It improves the interaction between the robot and the environment and realizes adaptive active compliant constant-force control in robotic grinding. Comparative experiments demonstrate the stability and reliability of the proposed method. Compared with mainstream methods, the proposed method reduces the grinding force error by 66.7% and 28.6%, respectively. The key error metrics MSE, RMSE, MAPE, and MAE are reduced by more than 71% and 20%, and the average surface roughness is reduced by approximately 15.6% and 5.8%, respectively
{"title":"Multi-modal fusion-enhanced fuzzy adaptive variable impedance control with improved DBN for robotic constant force blade grinding","authors":"Yong Tao , Jiao Xue , Yazui Liu , Lin Yang , Jiewu Leng , Pai Zheng , Baicun Wang , Xiaotong Wang , Hongxing Wei","doi":"10.1016/j.rcim.2026.103294","DOIUrl":"10.1016/j.rcim.2026.103294","url":null,"abstract":"<div><div>During the grinding of aeroengine blade edges, complex time-varying nonlinear coupling and uncertain disturbances pose challenges to the adaptive regulation of constant force grinding, reducing process stability and precision. This paper proposed a multi-modal fusion-enhanced fuzzy adaptive variable impedance control with improved deep belief network (DBN) for robotic constant force blade grinding. Specifically, the three-dimensional model and point cloud model of the blade are integrated to extract accurate geometric information and generate reference grinding trajectories. Furtherly, the DBN training hyperparameters are optimized using linear success history-based adaptive differential evolution (LSHADE). This improves the DBN configuration and overcomes the limitations of conventional DBN based force compensation with fixed network structures and single modality inputs. On this basis, a fuzzy adaptive variable impedance control method based on the improved DBN is developed. Geometric, force/pose, and error modalities are fused to dynamically adjust the force compensation term. This design enables the controller to outperform conventional adaptive variable impedance methods under strongly time-varying conditions. It improves the interaction between the robot and the environment and realizes adaptive active compliant constant-force control in robotic grinding. Comparative experiments demonstrate the stability and reliability of the proposed method. Compared with mainstream methods, the proposed method reduces the grinding force error by 66.7% and 28.6%, respectively. The key error metrics MSE, RMSE, MAPE, and MAE are reduced by more than 71% and 20%, and the average surface roughness is reduced by approximately 15.6% and 5.8%, respectively</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103294"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387362","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 : 2026-10-01Epub Date: 2026-03-03DOI: 10.1016/j.rcim.2026.103278
Ci Song , Baicun Wang , Xingyu Li , Huayong Yang , Lihui Wang
With the advent of human-centric manufacturing paradigm in the context of Industry 5.0, human-robot collaboration (HRC) becomes a crucial strategy to achieving enhanced flexibility and adaptability in manufacturing systems. Serving as a foundation for HRC deployment, human action recognition (HAR) infers human operational intent and enables robots to respond accordingly. However, existing HAR methods embedded in HRC systems mainly focus on accurately classifying actions into a known category encountered during training, with limited consideration of unknown sample in real scenarios, which may undermine the stability and safety of HRC systems. To address this issue, this work proposes a novel skeleton-based HAR algorithm with open-set recognition ability. The model features ensembled backbones for feature extraction using three parallel branches, and a corresponding Energy-based Diverse Non-Parametric Outlier Synthesis (EDNPOS) learning framework is designed which is able to generate virtual outliers as supervision signals and optimize the decision boundary between known and unknown data. Comprehensive experiments are conducted on three public datasets NTU RGB+D 60 (NTU 60), NW-UCLA and InHARD. Results verify the outstanding open-set recognition ability of our model while maintaining competitive closed-set accuracy. Finally, quantitative and qualitative evaluations on a compressor assembly case demonstrate the effectiveness and promise of our method in HRC applications. This work is expected to serve as a reference for realizing a more reliable HAR function in HRC systems.
{"title":"EDNPOS: An open-set skeleton-based human action recognition approach for human-robot collaboration enabled by outlier exposure","authors":"Ci Song , Baicun Wang , Xingyu Li , Huayong Yang , Lihui Wang","doi":"10.1016/j.rcim.2026.103278","DOIUrl":"10.1016/j.rcim.2026.103278","url":null,"abstract":"<div><div>With the advent of human-centric manufacturing paradigm in the context of Industry 5.0, human-robot collaboration (HRC) becomes a crucial strategy to achieving enhanced flexibility and adaptability in manufacturing systems. Serving as a foundation for HRC deployment, human action recognition (HAR) infers human operational intent and enables robots to respond accordingly. However, existing HAR methods embedded in HRC systems mainly focus on accurately classifying actions into a known category encountered during training, with limited consideration of unknown sample in real scenarios, which may undermine the stability and safety of HRC systems. To address this issue, this work proposes a novel skeleton-based HAR algorithm with open-set recognition ability. The model features ensembled backbones for feature extraction using three parallel branches, and a corresponding Energy-based Diverse Non-Parametric Outlier Synthesis (EDNPOS) learning framework is designed which is able to generate virtual outliers as supervision signals and optimize the decision boundary between known and unknown data. Comprehensive experiments are conducted on three public datasets NTU RGB+<em>D</em> 60 (NTU 60), NW-UCLA and InHARD. Results verify the outstanding open-set recognition ability of our model while maintaining competitive closed-set accuracy. Finally, quantitative and qualitative evaluations on a compressor assembly case demonstrate the effectiveness and promise of our method in HRC applications. This work is expected to serve as a reference for realizing a more reliable HAR function in HRC systems.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103278"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360721","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 : 2026-10-01Epub Date: 2026-02-27DOI: 10.1016/j.rcim.2026.103275
Chong Lv , Lai Zou , Heng Li , Lei Ren , Feng Jiao , Xinli Wang
In robotic belt grinding of complex curved blades, the elastic contact characteristics and variable curvature distribution of the blade results in non-uniform residual height distributions in both the chordwise and spanwise directions, thereby hindering the attainment of stringent dimensional tolerances. In this work, a novel trajectory planning method for robotic grinding of blades is presented to effectively improve surface residual uniformity. Initially, a 3D residual theoretical model is established through the curved surface geometric properties. Subsequently, the maximum chord height between adjacent cutter contact (CC) points is recalculated by the iterative verification algorithm, and an optimized chord height method is proposed to maximize the step length within the allowable. Furthermore, the isoparametric trajectory and the isoscallop trajectory for 3D residual optimization are proposed respectively to dynamically adjust the row spacing based on the curvature changes of CC points. Simulation and experimental results demonstrate the effectiveness of the proposed methods from the perspectives of machined efficiency and machined quality. The machining efficiency of the optimized isoscallop method is improved by 7.4 % compared with that before optimization, the fluctuation ranges of the surface profile error of these two proposed trajectories decreased by 28.7 % and 38.5 %, respectively. The presented trajectory planning method provides a valuable reference for improving the machined surface quality consistency in robotic grinding of complex curved surfaces.
{"title":"3D residual optimization-based trajectory planning for robotic grinding of complex curved blades","authors":"Chong Lv , Lai Zou , Heng Li , Lei Ren , Feng Jiao , Xinli Wang","doi":"10.1016/j.rcim.2026.103275","DOIUrl":"10.1016/j.rcim.2026.103275","url":null,"abstract":"<div><div>In robotic belt grinding of complex curved blades, the elastic contact characteristics and variable curvature distribution of the blade results in non-uniform residual height distributions in both the chordwise and spanwise directions, thereby hindering the attainment of stringent dimensional tolerances. In this work, a novel trajectory planning method for robotic grinding of blades is presented to effectively improve surface residual uniformity. Initially, a 3D residual theoretical model is established through the curved surface geometric properties. Subsequently, the maximum chord height between adjacent cutter contact (CC) points is recalculated by the iterative verification algorithm, and an optimized chord height method is proposed to maximize the step length within the allowable. Furthermore, the isoparametric trajectory and the isoscallop trajectory for 3D residual optimization are proposed respectively to dynamically adjust the row spacing based on the curvature changes of CC points. Simulation and experimental results demonstrate the effectiveness of the proposed methods from the perspectives of machined efficiency and machined quality. The machining efficiency of the optimized isoscallop method is improved by 7.4 % compared with that before optimization, the fluctuation ranges of the surface profile error of these two proposed trajectories decreased by 28.7 % and 38.5 %, respectively. The presented trajectory planning method provides a valuable reference for improving the machined surface quality consistency in robotic grinding of complex curved surfaces.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"101 ","pages":"Article 103275"},"PeriodicalIF":11.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330051","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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