Industrial Robot最新文献

{"title":"A method for predicting relative position errors in dual-robot systems via knowledge transfer from geometric and nongeometric calibration","authors":"Siming Cao, Hongfeng Wang, Yingjie Guo, Weidong Zhu, Yinglin Ke","doi":"10.1108/ir-11-2023-0267","DOIUrl":"https://doi.org/10.1108/ir-11-2023-0267","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139555873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Narrow gap welding seam deflection correction study based on passive vision","authors":"Wang Zhang, Lizhe Fan, Yanbin Guo, Weihua Liu, Chao Ding","doi":"10.1108/ir-10-2023-0252","DOIUrl":"https://doi.org/10.1108/ir-10-2023-0252","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139517235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UDS-SLAM: real-time robust visual SLAM based on semantic segmentation in dynamic scenes","authors":"Jun Liu, Junyuan Dong, Mingming Hu, Xu Lu","doi":"10.1108/ir-08-2023-0190","DOIUrl":"https://doi.org/10.1108/ir-08-2023-0190","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on system integration and control methods of an apple-picking robot in unstructured environment","authors":"Qiaojun Zhou, Ruilong Gao, Zenghong Ma, Gonghao Cao, Jianneng Chen","doi":"10.1108/ir-11-2023-0282","DOIUrl":"https://doi.org/10.1108/ir-11-2023-0282","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The purpose of this article is to solve the issue that apple-picking robots are easily interfered by branches or other apples near the target apple in an unstructured environment, leading to grasping failure and apple damage.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>This study introduces the system units of the apple-picking robot prototype, proposes a method to determine the apple-picking direction via 3D point cloud data and optimizes the path planning method according to the calculated picking direction.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>After the field experiments, the average deviation of the calculated picking direction from the desired angle was 11.81°, the apple picking success rate was 82% and the picking cycle was 11.1 s.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This paper describes a picking control method for an apple-picking robot that can improve the success and reliability of picking in an unstructured environment and provides a basis for automated and mechanized picking in the future.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A KMP-based interactive learning approach for robot trajectory adaptation with obstacle avoidance","authors":"Sa Xiao, Xuyang Chen, Yuankai Lu, Jinhua Ye, Haibin Wu","doi":"10.1108/ir-11-2023-0284","DOIUrl":"https://doi.org/10.1108/ir-11-2023-0284","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic parameter identification method for the 5-DOF hybrid robot based on sensitivity analysis","authors":"Zaihua Luo, Juliang Xiao, Sijiang Liu, Mingli Wang, Wei Zhao, Haitao Liu","doi":"10.1108/ir-08-2023-0178","DOIUrl":"https://doi.org/10.1108/ir-08-2023-0178","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint torque prediction of industrial robots based on PSO-LSTM deep learning","authors":"Wei Xiao, Zhongtao Fu, Shixian Wang, Xubing Chen","doi":"10.1108/ir-08-2023-0191","DOIUrl":"https://doi.org/10.1108/ir-08-2023-0191","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139420982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments","authors":"Xin Cai, Xiaozhou Zhu, Wen Yao","doi":"10.1108/ir-09-2023-0196","DOIUrl":"https://doi.org/10.1108/ir-09-2023-0196","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Quadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>First, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139398514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles","authors":"Zhuoyu Zhang, Lijia Zhong, Mingwei Lin, Ri Lin, Dejun Li","doi":"10.1108/ir-10-2023-0233","DOIUrl":"https://doi.org/10.1108/ir-10-2023-0233","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139372945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive decentralized fuzzy compensation control for large optical mirror processing systems","authors":"Zujin Jin, Zixin Yin, Siyang Peng, Yan Liu","doi":"10.1108/ir-09-2023-0207","DOIUrl":"https://doi.org/10.1108/ir-09-2023-0207","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Large optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Experimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.</p><!--/ Abstract__block -->","PeriodicalId":501389,"journal":{"name":"Industrial Robot","volume":"131 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139103165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}