Advanced Engineering Informatics最新文献_第7页

Sliding time–frequency synchronous average based on autocorrelation function for extracting fault feature of bearings 基于自相关函数的滑动时频同步平均法提取轴承故障特征

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102876

Tao Liu , Laixing Li , Yongbo Li , Khandaker Noman

{"title":"Sliding time–frequency synchronous average based on autocorrelation function for extracting fault feature of bearings","authors":"Tao Liu , Laixing Li , Yongbo Li , Khandaker Noman","doi":"10.1016/j.aei.2024.102876","DOIUrl":"10.1016/j.aei.2024.102876","url":null,"abstract":"<div><div>Making weak repetitive pulses clearly appear in time–frequency distribution is essential for detecting early failure of bearings. However, this operation is a challenging issue in fault diagnosis. To resolve this problem, a signal enhancement method called sliding time–frequency synchronous average based on autocorrelation function (STFSA-ACF) is proposed in this paper, based on three ways of signal enhancement. In the method, the autocorrelation function is first utilized to enhance the repetitive components of signals. The time–frequency representation of the autocorrelation function result is obtained by short-time Fourier transform. Furthermore, an improved version of time synchronous average called the sliding time–frequency synchronous average is developed to make the weak repetitive pulses more visible. In this method, a window sliding in the time–frequency plane is introduced to intercept the signal, and the time synchronous average is employed to process the intercepted section. The aforementioned operations construct the STFSA-ACF. Finally, the gamma transform is used to improve the contrast of generated STFSA-ACF. A series of numerically simulated signals are generated to validate the proposed algorithm. Besides, this method is employed to process part signals of two sets of public data. Performance of the proposed STFSA-ACF has been compared with popular methods such as fast Kurtogram, maximum correlated kurtosis deconvolution, and adaptive maximum second-order cyclostationarity blind deconvolution. Comparison results indicate that the STFSA-ACF has the best performance in terms of making weak repetitive pulses more visible.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102876"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatially embedded transformer: A point cloud deep learning model for aero-engine coaxiality prediction based on virtual measurement 空间嵌入式变压器：基于虚拟测量的航空发动机同轴度预测点云深度学习模型

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102900

Tianyi Wu , Ke Shang , Xin Jin , Zhijing Zhang , Chaojiang Li , Steven Wang , Jun Liu

{"title":"Spatially embedded transformer: A point cloud deep learning model for aero-engine coaxiality prediction based on virtual measurement","authors":"Tianyi Wu , Ke Shang , Xin Jin , Zhijing Zhang , Chaojiang Li , Steven Wang , Jun Liu","doi":"10.1016/j.aei.2024.102900","DOIUrl":"10.1016/j.aei.2024.102900","url":null,"abstract":"<div><div>Coaxiality is a critical indicator of assembly accuracy in aero-engines, directly impacting the device’s operational performance and lifespan. Due to the enclosed nature of the aero-engine casing system, measuring the coaxiality of assembled components presents significant challenges. This paper introduces a novel deep learning architecture, the spatially embedded transformer (SETrans), designed to predict coaxiality from unassembled part data by correlating it with the contact surface points of assembled components. Additionally, a virtual measurement model is developed to collect micron-scale point cloud data, facilitating the fine-tuning of the deep learning model. The SETrans utilizes the transformer’s capability for global information aggregation to process point cloud inputs, capturing the comprehensive relationships across assembled surfaces. A newly designed module, the spatial bias, integrates distance and angular information between neighboring point clouds into the transformer block, enhancing the model’s ability to capture fine-grained local details. Experimental validation is conducted using two distinct datasets representing different assembly scenarios: the aero-engine casing, sampled using contact-based coordinate measuring machines, and the rotor, sampled using non-contact optical gaging products. These specific sampling methods test the generalizability of the SETrans across diverse measurement techniques. Comparative analysis with other point cloud deep learning benchmarks shows that the proposed approach achieves top prediction accuracies of 93.65% and 94.31% with a coaxiality precision of 0.01 mm across different data domains. These results confirm the effectiveness of the SETrans and demonstrate its adaptability to real-world assembly conditions involving various components.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102900"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting human-guided progressive learning with machine vision systems for robust surface defect detection 利用机器视觉系统增强人类引导的渐进式学习，实现稳健的表面缺陷检测

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102906

Swarit Anand Singh, Sahil J Choudhari, K.A. Desai

{"title":"Augmenting human-guided progressive learning with machine vision systems for robust surface defect detection","authors":"Swarit Anand Singh, Sahil J Choudhari, K.A. Desai","doi":"10.1016/j.aei.2024.102906","DOIUrl":"10.1016/j.aei.2024.102906","url":null,"abstract":"<div><div>Machine vision systems commonly utilize Convolutional Neural Networks (CNNs) for in-line surface defect detection of manufactured components. The prediction abilities of vision-based inspection systems deteriorate with time as the defect detection model trained on fixed image datasets fails to accommodate deviations. This paper proposes a human-guided progressive learning approach that systematically imparts learning of new features to the CNN-powered vision-based defect detection system. The approach augments the surface defect detection model with human intelligence, using an intuitive user interface to address model drift. The human expert monitors the trained model performance under specific conditions leading to the change of characteristics during implementation, identifies misclassifications, and initiates re-training. The algorithm accumulates misclassified data till a pre-defined threshold level is reached or a human expert terminates inspection. The misclassified results merge with the original datasets for progressive re-training using a strategy similar to the base model development. The present work utilizes pre-trained CNN Efficientnet-b0 to develop the surface defect detection model for tapered roller inspection through transfer learning. It is concluded that the progressive re-training improves defect detection performance and reduces misclassifications. The Matthews Correlation Coefficient (MCC) score, derived from the confusion matrix, showed improvement from 0.6 to 0.82 after four iterations. A cross-model benchmarking study is also performed to show the versatility of the proposed approach. The present work demonstrated that the human-guided progressive learning approach can provide adaptability to vision-based surface defect detection utilizing deep learning algorithms and enhance system performance during real-time implementation.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102906"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From technology opportunities to solutions generation via patent analysis: Application of machine learning-based link prediction 通过专利分析从技术机遇到解决方案的生成：基于机器学习的链接预测的应用

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102944

Ziliang Wang , Wei Guo , Hongyu Shao , Lei Wang , Zhixing Chang , Yuanrong Zhang , Zhenghong Liu

{"title":"From technology opportunities to solutions generation via patent analysis: Application of machine learning-based link prediction","authors":"Ziliang Wang , Wei Guo , Hongyu Shao , Lei Wang , Zhixing Chang , Yuanrong Zhang , Zhenghong Liu","doi":"10.1016/j.aei.2024.102944","DOIUrl":"10.1016/j.aei.2024.102944","url":null,"abstract":"<div><div>Technology convergence represents a significant mode of technological innovation that is widely prevalent across various industries. This innovative approach integrates multiple technologies to develop new integrated solutions, thereby fostering a competitive advantage for enterprises. Anticipating future potential technology convergence is of paramount importance for businesses. However, previous research has predominantly relied on the topological information of convergence networks, overlooking the nodal attributes and inter-nodal relationships that have an impact on the emergence of technology convergence. To enhance existing studies, this paper employs three types of features: node attributes and inter-node relationships based on the drivers of technology convergence, along with link prediction similarity indices. Additionally, we utilize Graph Convolutional Neural Network (GCN) for node embedding to leverage node attributes. Machine learning models are utilized for link prediction based on these features to identify potential technology opportunities. To guide research and development (R&D) efforts, we recommend high-value patents for each node using entropy weighting across five metrics that objectively quantify patent value, and transform patent abstracts into vectors using Doc2Vec. Patents with high similarity in abstract text between nodes are utilized to extract technical solutions and fuse ideas for technology convergence. A case study is conducted within the autonomous driving industry, leveraging comprehensive information including node attributes, inter-node relationships, and topology-based similarities to identify technology opportunities and guide the generation of R&D ideas through the convergence of technical solutions.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102944"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data 利用专家知识和有限数据为复杂工业系统建模的可解释大规模信念规则库

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102852

Zheng Lian, Zhijie Zhou, Changhua Hu, Zhichao Feng, Pengyun Ning, Zhichao Ming

{"title":"Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data","authors":"Zheng Lian, Zhijie Zhou, Changhua Hu, Zhichao Feng, Pengyun Ning, Zhichao Ming","doi":"10.1016/j.aei.2024.102852","DOIUrl":"10.1016/j.aei.2024.102852","url":null,"abstract":"<div><div>Complex system modeling technology is a hot topic. Nowadays, many complex industrial systems present three characteristics: multiple input indicators, limited data and interpretability requirements. With good interpretability, belief rule base (BRB) serves as an efficient tool for modeling complex systems. However, as the number of input indicators of industrial systems increases, BRB suffers from the combinatorial explosion problem, which makes it hard to generate large-scale BRB and optimize it while maintaining its interpretability. For this purpose, an interpretable large-scale BRB is proposed for complex systems with limited data, where expert knowledge can be utilized effectively. First, a framework for generating an initial large-scale BRB using expert knowledge and limited data is developed, including the determination of attribute weight, basic belief degree and rule weight. Afterwards, a new parameter optimization model is designed to reduce the burden of parameter optimization and maintain the interpretability of BRB, where the Adaptive Moment Estimation (Adam) algorithm is adopted to further improve the efficiency of large-scale parameter optimization. Finally, a health assessment case of an inertial navigation system (INS) verifies the proposed method.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102852"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating Smeta-LU：基于标签更新的旋转机械自监督元学习故障诊断方法

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102875

Zhiqian Zhao , Yinghou Jiao , Yeyin Xu , Zhaobo Chen , Runchao Zhao

{"title":"Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating","authors":"Zhiqian Zhao , Yinghou Jiao , Yeyin Xu , Zhaobo Chen , Runchao Zhao","doi":"10.1016/j.aei.2024.102875","DOIUrl":"10.1016/j.aei.2024.102875","url":null,"abstract":"<div><div>During operation of rotating machinery, collecting high-quality labeled fault samples is difficult, and the corresponding data annotation is time consuming and costly. Therefore, developing novel intelligent diagnostic methods which can extract key information from massive fault data without labeling is of great significance. In this regard, a self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating, called Smeta-LU, is proposed. It eliminates the pre-training phase and generates meta-tasks directly without labeling information during training. A two-branch framework in Smeta-LU is developed using a contrastive learning approach, which involves the application of a dynamic dictionary to construct samples for one branch, represented by an online encoder. The other branch utilizes the parameters of the former to obtain a target encoder through exponential moving average. To dynamically construct diverse meta-tasks during the meta-training process, each sample in the current batch is treated as a query set, while the support set is selected from queues to construct few-shot tasks, thereby generating a larger pool of candidates. The fault diagnosis task is completed by assigning the label matrix with an optimal transport algorithm and identifying the shots closest to each of the prototype centers. Additionally, the iterative properties of the momentum network and dynamic dictionary are implemented for label updating. The outcomes of two validation experiments demonstrate the superiority and scalability of our self-supervised meta-learning approach compared with conventional supervised meta-learning techniques. Better performance in identifying new fine-grained fault categories is also exhibited during our research.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102875"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision-based motion prediction for construction workers safety in real-time multi-camera system 基于视觉的运动预测，在实时多摄像头系统中保障建筑工人安全

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102898

Yuntae Jeon , Dai Quoc Tran , Almo Senja Kulinan , Taeheon Kim , Minsoo Park , Seunghee Park

{"title":"Vision-based motion prediction for construction workers safety in real-time multi-camera system","authors":"Yuntae Jeon , Dai Quoc Tran , Almo Senja Kulinan , Taeheon Kim , Minsoo Park , Seunghee Park","doi":"10.1016/j.aei.2024.102898","DOIUrl":"10.1016/j.aei.2024.102898","url":null,"abstract":"<div><div>Ensuring worker safety on dynamic construction sites is a significant challenge, especially as it is crucial to immediately identify potential hazards and warn workers. Existing computer vision-based motion prediction methods often overlook the false negative issue caused by the noisy environments of construction sites, and treat tracking and trajectory prediction as disconnected processes. This study introduces MPSORT, a method that suggests trajectory prediction-based tracking with trajectory interpolation for vision-based automated safety monitoring. The proposed method predicts the future movements of construction workers and vehicles using multiple CCTV cameras, and localizes these predictions onto the construction site’s bird’s eye view (BEV) map. This enables to send the real-time warnings to workers in danger, preventing accidents such as collision, fall, and getting stuck. We evaluated the performance of our method in both object tracking and trajectory prediction tasks on dataset from multiple CCTV cameras on construction sites. The object tracking results show an approximate 10% increase in the number of tracked objects, and the trajectory prediction results indicate an ADE of 7.138 and an FDE of 12.493, reflecting improvements of more than 5% and 2% in ADE and FDE, respectively, compared to previous methods. Overall, these findings are significant for minimizing accidents and enhancing safety and efficiency on construction sites.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102898"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed integer programming and multi-objective enhanced differential evolution algorithm for human–robot responsive collaborative disassembly in remanufacturing system 再制造系统中人机响应协同拆卸的混合整数编程和多目标增强差分进化算法

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102895

Zeqiang Zhang , Wei Liang , Dan Ji , Yanqing Zeng , Yu Zhang , Yan Li , Lixia Zhu

{"title":"Mixed integer programming and multi-objective enhanced differential evolution algorithm for human–robot responsive collaborative disassembly in remanufacturing system","authors":"Zeqiang Zhang , Wei Liang , Dan Ji , Yanqing Zeng , Yu Zhang , Yan Li , Lixia Zhu","doi":"10.1016/j.aei.2024.102895","DOIUrl":"10.1016/j.aei.2024.102895","url":null,"abstract":"<div><div>The recycling of waste products is essential for resource reuse. However, turning operation direction causes significant fatigue to operators handling end-of-life (EoL) products, consequently degrading the recycling efficiency. Accordingly, this study employs responsive collaboration robots to aid operators in turning the operation direction of disassembled products. To solve the human-robot responsive collaboration disassembly line balancing problem (HRRC-DLBP), a mixed integer programming (MIP) model is constructed, and a decoding mechanism is designed in this study. Additionally, a multi-objective enhanced differential evolution algorithm (MEDE) in which the decoding mechanism is incorporated is devised and applied to solve the HRRC-DLBP. The MEDE algorithm is validated by comparing its solution results with those of the MIP model. Finally, the MEDE is used to optimise the EoL printer case for the HRRC-DLBP and the disassembly line balancing problem in which the operation direction is turned by humans (H-DLBP). The optimisation results show that the recycling of EoL products is more efficient using the HRRC-DLBP than employing the H-DLBP.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102895"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D UAV path planning in unknown environment: A transfer reinforcement learning method based on low-rank adaption 未知环境中的 3D 无人机路径规划：基于低阶自适应的转移强化学习方法

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102920

Lan Bo , Tiezhu Zhang , Hongxin Zhang , Jichao Hong , Mingjie Liu , Caihong Zhang , Benyou Liu

{"title":"3D UAV path planning in unknown environment: A transfer reinforcement learning method based on low-rank adaption","authors":"Lan Bo , Tiezhu Zhang , Hongxin Zhang , Jichao Hong , Mingjie Liu , Caihong Zhang , Benyou Liu","doi":"10.1016/j.aei.2024.102920","DOIUrl":"10.1016/j.aei.2024.102920","url":null,"abstract":"<div><div>The increasing number of application scenarios necessitate unmanned aerial vehicles to possess the capability of autonomous obstacle avoidance and navigation in unknown environments, representing a crucial direction for its development. Path planning plays a crucial role in this process. Path planning aims to design efficient and safe navigation paths for UAVs, thereby significantly reducing energy consumption and time spent while improving equipment adaptability to the environment. Firstly, we employ the deep reinforcement learning algorithm to train the agent on randomly changing maps, enabling it to possess both generalization capabilities and active obstacle avoidance skills. Secondly, a novel framework combining transfer reinforcement learning is proposed. It establishes the pre-trained model and utilizes the enhanced low-rank adaptive algorithm to transfer it into formal training, thereby incorporating prior knowledge and improving the efficacy of formal training. Finally, a novel method of sample abundance is proposed to reuse the experience pool stored in the pre-trained model and further increase the generalization capability of the agent, thereby significantly improving its success rate. The proposed algorithm efficiently uses both the pre-trained model and the experience pool. In practical applications, the pre-trained model can be acquired by training on a limited dataset to endow the agent with autonomous obstacle avoidance capabilities. In formal training, numerous random samples are established to simulate unfamiliar environmental terrains. After rapid training, the agent achieves a success rate of 95% in the test set and demonstrates exceptional performance in smoothness and path length.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102920"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimization-based motion planner for dual-arm manipulation of the soft deformable linear objects with nonnegligible gravity 基于优化的运动规划器，用于双臂操纵具有不可忽略重力的软变形线性物体

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102874

Shirui Wu, Jiwen Zhang, Dan Wu

{"title":"An optimization-based motion planner for dual-arm manipulation of the soft deformable linear objects with nonnegligible gravity","authors":"Shirui Wu, Jiwen Zhang, Dan Wu","doi":"10.1016/j.aei.2024.102874","DOIUrl":"10.1016/j.aei.2024.102874","url":null,"abstract":"<div><div>The dual-arm manipulation of deformable linear objects (DLOs) represents a practical and challenging problem in robotics research, offering significant potential for various industrial applications, including cable assembly. To accurately model the mechanical properties of DLOs, a Kirchhoff differential model is employed, which parameterizes the DLO configuration as a 6-dimensional manifold. Traditionally, approaches to solving this planning problem relied solely on sampling-based methods, incurring high computational costs due to the necessity of obtaining the DLO shape for each sample. Additionally, these methods completely ignored gravity, assuming that the DLO was stiff enough. However, in many industrial scenarios, this assumption cannot hold, particularly when dealing with soft DLOs, where the effects of gravity are non-negligible, leading to poorer stability and sensitivity. In this work, a novel optimization-based paradigm is proposed for the manipulation planning of soft DLOs with dual arms, addressing the challenges associated with their soft nature and the influence of gravity. The concept of ’stability distance’ is introduced as an easily measurable indicator of the degree of DLO stability. Furthermore, a thorough investigation into the singularity phenomenon in DLO local leading is conducted to identify its causes and provide effective solutions. Additionally, a strategy is introduced to avoid local traps of the DLO in complex obstacle environments. The comprehensive planner is validated through both simulation and hardware experiments, utilizing two types of soft DLOs with a length of approximately 1 m in various environmental settings. The results demonstrate the promising performance of the algorithm across diverse assembly scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102874"},"PeriodicalIF":8.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0