International Journal of Intelligent Robotics and Applications最新文献

{"title":"Investigation on robotic cells design improvement in the welding process of body in white","authors":"Qi Xia, Bangcheng Zhang, Xiyu Zhang, Lei Li, Chen Wu","doi":"10.1007/s41315-023-00317-8","DOIUrl":"https://doi.org/10.1007/s41315-023-00317-8","url":null,"abstract":"<p>Issues about cycle time optimization is of great importance in the field of automotive production, the industrial robots are widely used in the welding process of automobiles, but there is little research on the optimization of intra station rhythm during the design phase. By conducting research on workstation with industrial robot processing as key process, this paper carries out analysis from the selection of equipment layout within the workstation, planning production rhythm, and the facility performance analysis within the workstation. The finding shows the cycle time within the workstation has been reduced by 12 s. This article aims at improving the rhythm of robotic cells in complex production environment, and raising production efficiency of workstation. The robot path is optimized by using intelligent algorithms, the human machine collaborative work has been validated in virtual scenes, some digital design is adopted for modelling and simulating, the designed workstation has been verified from multiple perspectives, and finally achieve the workstation design of applying industrial robots in the production scenario.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201321","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 state-of-the-art review on topology and differential geometry-based robotic path planning—part I: planning under static constraints","authors":"Sindhu Radhakrishnan, Wail Gueaieb","doi":"10.1007/s41315-024-00330-5","DOIUrl":"https://doi.org/10.1007/s41315-024-00330-5","url":null,"abstract":"<p>Autonomous robotics has permeated several industrial, research and consumer robotic applications, of which path planning is an important component. The path planning algorithm of choice is influenced by the application at hand and the history of algorithms used for such applications. The latter is dependent on an extensive conglomeration and classification of path planning literature, which is what this work focuses on. Specifically, we accomplish the following: typical classifications of path planning algorithms are provided. Such classifications rely on differences in knowledge of the environment (known/unknown), robot (model-specific/generic), and constraints (static/dynamic). This classification however, is not comprehensive. Thus, as a resolution, we propose a detailed taxonomy based on a fundamental parameter of the space, i.e. its ability to be characterized as a set of disjoint or connected points. We show that this taxonomy encompasses important attributes of path planning problems, such as connectivity and partitioning of spaces. Consequently, path planning spaces in robotics may be viewed as simply a set of points, or as manifolds. The former can further be divided into unpartitioned and partitioned spaces, of which the former uses variants of sampling algorithms, optimization algorithms, model predictive controls, and evolutionary algorithms, while the latter uses cell decomposition and graph traversal, and sampling-based optimization techniques.This article achieves the following two goals: The first is the introduction of an all-encompassing taxonomy of robotic path planning. The second is to streamline the migration of path planning work from disciplines such as mathematics and computer vision to robotics, into one comprehensive survey. Thus, the main contribution of this work is the review of works for static constraints that fall under the proposed taxonomy, i.e., specifically under topology and manifold-based methods. Additionally, further taxonomy is introduced for manifold-based path planning, based on incremental construction or one-step explicit parametrization of the space.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205661","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":"An algorithm for abnormal behavior recognition based on sharing human target tracking features","authors":"","doi":"10.1007/s41315-024-00329-y","DOIUrl":"https://doi.org/10.1007/s41315-024-00329-y","url":null,"abstract":"<h3>Abstract</h3> <p>Human behavior recognition is a hot research topic in the field of computer vision, and a complete behavior recognition usually includes human detection, human tracking and behavior recognition. At present, the two tasks of human tracking and abnormal behavior recognition based on deep learning are mostly executed separately, and the related feature information in the two tasks cannot be fully utilized, resulting in high time cost and resource consumption of the final abnormal behavior recognition algorithm. The problem greatly limits the widespread application of abnormal behavior recognition. In order to improve the performance of the algorithm a novel model for abnormal behaviors recognition based on human target tracking is proposed, which implements the process of recognizing abnormal behaviors after human target tracking through feature sharing. First, the real-time multi-domain convolutional neural network is improved by introducing a spatial attention mechanism to improve its tracking of a particular human body in a video series. Then the output of the convolutional layer in MDnet is used as the input of the abnormal behavior recognition network, and these features are combined with CNN and LSTM to realize human abnormal behavior recognition. During the network training process, a multi-task learning approach was used to train a model for human tracking and behaviour recognition. Six types of abnormal behaviors selected on the CASIA Behavioural Analytics dataset and 12 types of behaviours selected on the NTU database are used to train and test the network model. According to test results, the proposed model is capable of tracking human targets precisely and in real time (26 frames per second). The proposed model can also distinguish abnormal behaviors of tracking targets with a recognition rate of 92.1%. The human features obtained in the tracking model is used as the input of the abnormal behavior recognition network, so the feature sharing of tracking and recognition is achieved, and a complete abnormal behavior recognition framework including target tracking, feature extraction, and behavior recognition is established. There is great practical significance to the proposed method.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166599","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":"Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning","authors":"","doi":"10.1007/s41315-024-00325-2","DOIUrl":"https://doi.org/10.1007/s41315-024-00325-2","url":null,"abstract":"<h3>Abstract</h3> <p>Robot-based painting industries optimize operations and enhance product quality by leveraging insights from real and virtual studies, encompassing trajectory patterns, paint film qualities, and machine learning for fault identification. Automation of fault identification procedures is the novel aspect of the study that helps to reduce human error and maintain consistent quality standards in manufacturing. This in-depth investigation examines the analysis of paint paths for robot painting with a focus on three distinctive movement patterns: linear, circular, and zigzag. The investigation includes assessments of smoothness for each route, along with morphological evaluations using Scanning Electron Microscope (SEM) pictures. The surface quality is assessed methodically using Taguchi L9 orthogonal testing, while Analysis of Variance (ANOVA) is utilised to identify the key factors that contribute to variations in paint qualities. In order to enhance quality control, machine learning is included to automate the classification and identification of flaws, utilising sophisticated picture analysis techniques. It is essential to incorporate virtual-environment experiments to ensure the accuracy and applicability of the results in real-world situations. This technique reveals crucial observations on the temporal difference between virtual and real surroundings, providing significant information for enhancing the painting process to better match the actual operational parameters. In addition, the analysis determines that the best combination of roughness is A3B3C2 using the Taguchi method, which results in an outstanding finish with a roughness value of 0.0347 µm. Verifying the efficacy of cutting-edge technology in honing painting techniques and improving end product quality, the machine learning model demonstrates a remarkable 94% accuracy in real-time flaw detection.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124911","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 robot sewing method based on process modeling","authors":"Fengming Li, Dang Hou, Tianyu Fu, Jiexin Song, Wenbin He, Rui Song","doi":"10.1007/s41315-024-00326-1","DOIUrl":"https://doi.org/10.1007/s41315-024-00326-1","url":null,"abstract":"<p>At present, most clothing sewing relies on manual labor, and robot sewing has become a trend. However, different clothing styles have various sewing requirements. This poses a challenge for robot sewing, and the key to solving this challenge lies in the planning of robot operation trajectories. Although the shapes of sewing components are diverse, we can decompose them into the most basic straight lines and curved edges. In order to solve the trajectory planning problem in robot sewing process, this paper divides the sewing task into two parts: straight line and curve, and proposes a new robot sewing method based on task process decomposition. Firstly, The robot complex sewing task is divided into two parts: straight line and curve. Based on the extensibility, the sewing tension is predicted, and the robot linear sewing based on impedance control is realized. At the same time, the trajectory planning is carried out on the basis of the line identification of the curved edge to realize the curve sewing. Finally, the robot complex stitch sewing under different curvatures is realized on the built physical experiment platform. It is verified that the effectiveness of the robot sewing method based on process modeling.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125088","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":"Intelligent optimization algorithms for control error compensation and task scheduling for a robotic arm","authors":"Ping-Huan Kuo, Min-Jhih Syu, Shuo-Yi Yin, Han-Hao Liu, Chao-Yi Zeng, Wei-Chih Lin, Her-Terng Yau","doi":"10.1007/s41315-024-00328-z","DOIUrl":"https://doi.org/10.1007/s41315-024-00328-z","url":null,"abstract":"<p>A task scheduling and error control optimization method for robotic arms was developed. The arm’s accuracy after optimization with particle swarm optimization, artificial bee colony, grey wolf optimizer, the genetic algorithm, differential evolution algorithm, and the bat algorithm was compared to identify the best optimization method. Task scheduling was optimized by identifying the optimal paths to each target object. The method can control positioning error, enabling the robotic arm to reach its target coordinates with the smallest error despite being affected by interference during navigation. The proposed method was verified in virtual environments with varying target objects at different locations. The estimation results and convergence speed of each algorithm were compared to identify the most accurate algorithm. The proposed method could be used to improve the task scheduling and error control of robotic arms. The method could also be used in combination with algorithms in accordance with the requirements of practical scenarios.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036832","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":"Mechanism analysis and suppression control strategy of frictional impact for humanoid robots","authors":"","doi":"10.1007/s41315-024-00319-0","DOIUrl":"https://doi.org/10.1007/s41315-024-00319-0","url":null,"abstract":"<h3>Abstract</h3> <p>Stability and robustness are the important expressions of intelligent walking ability of biped robots. The Zeno behavior caused by the frictional impact of knee joints affects the stability during the dynamic walking, which has greatly limited robot’s application and efficiency. Based on the analysis of the intrinsic mechanism of Zeno behavior, this paper aims to explore biped walking control methods to provide theoretical basis and key technologies for suppressing Zeno behavior. The internal relationship between Zeno behavior and robot knee joint collision is built by studying the cause of Zeno behavior. An event-based feedback controller is proposed to deal with the problem of stabilization of Zeno periodic orbit. It is achieved adaptive periodic stable walking in complex environment based on event-based and hybrid zero dynamic control strategy, which proposes the stability analysis method based on Poincare return map. Meanwhile, the identify parameters of dynamic equations with Zeno behavior is utilized with genetic algorithm and particle swarm optimization. Finally, the effectiveness of the proposed method is verified by simulations.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140011003","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":"Modeling and control for a long-stroke 4-PPR compliant parallel mechanism","authors":"Jianze Ren, Chi Zhang, Miao Yang, Liming Yuan, Na Sang, Jianhua Yao","doi":"10.1007/s41315-023-00313-y","DOIUrl":"https://doi.org/10.1007/s41315-023-00313-y","url":null,"abstract":"<p>Long-stroke compliant parallel mechanisms (CPMs) are widely used in precision applications. However, stress stiffening and sensitivity to external disturbances in CPMs present challenges in the design of controller. In this paper, the nonlinear stiffness model of the stage is established which is incorporated into the dynamic model. In particular, the method of adaptive nonsingular fast terminal sliding mode control (ANFTSMC) is developed based on the dynamic model. This method addresses the problems of the system parameter uncertainty and the slow convergence of traditional sliding mode control (SMC) at the equilibrium point. The stability of the presented ANFTSMC strategy has been proved based on the Lyapunov analysis. Finally, the proposed control architecture is implemented on the designed 4-prismatic-prismatic-revolute (4-PPR) CPM. The results demonstrate that the developed method exhibits excellent tracking accuracy and robustness compared to the traditional linear sliding mode control (LSMC) and proportional-integral-derivative (PID) control.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003453","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 stiffness adaptation control of medical assistive robots based on stiffness prediction","authors":"Chong Yao, Changle Li, Yihan Shan, Xuehe Zhang, Leifeng Zhang, Jie Zhao","doi":"10.1007/s41315-024-00321-6","DOIUrl":"https://doi.org/10.1007/s41315-024-00321-6","url":null,"abstract":"<p>Predicting human stiffness, especially at the distal end of the human arm, holds significant potential for various applications. It facilitates the realization of humanoid stiffness regulation in robots, improves the adaptability and human-likeness of interactive robots, and addresses critical issues in human control of medical assistive robots. Recognizing that surface electromyographic (EMG) signals not only contain rich information but are also easy to collect and process, they serve as an optimal choice for predicting human stiffness. To establish a mapping relationship between surface EMG signals and stiffness information, we constructed a stiffness acquisition system to collect signals such as EMG, angular, force, and displacement signals. Additionally, considering the influence of different angles (configurations) of the human arm on the stiffness at the distal end, we researched a stiffness prediction model for the distal end of the human arm using a multilayer perceptron. Experimental results demonstrate that our proposed stiffness prediction model, utilizing EMG information provided by the EMG armband along with angular information, can predict the stiffness at the distal end of the human arm in various scenarios. This provides ample reference for achieving humanoid stiffness regulation in medical assistive robots.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003181","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":"Model-free based adaptive BackStepping-Super Twisting-RBF neural network control with α-variable for 10 DOF lower limb exoskeleton","authors":"Farid Kenas, Nadia Saadia, Amina Ababou, Noureddine Ababou","doi":"10.1007/s41315-024-00322-5","DOIUrl":"https://doi.org/10.1007/s41315-024-00322-5","url":null,"abstract":"<p>Lower limb exoskeletons play a pivotal role in augmenting human mobility and improving the quality of life for individuals with mobility impairments. In light of these pressing needs, this paper presents an improved control strategy for a 10-degree-of-freedom lower limb exoskeleton, with a particular focus on enhancing stability, precision, and robustness. To simplify the intricate dynamic model of the exoskeleton, our approach leverages a more manageable 2nd order ultra-local model. We employ two radial basis function (RBF) neural networks to accurately estimate both lumped disturbances and non-physical parameters associated with this ultra-local model. In addition, our control strategy integrates the backstepping technique and the super twisting algorithm to minimize tracking errors. The stability of the designed controller is rigorously established using Lyapunov theory. In the implementation phase, a virtual prototype of the exoskeleton is meticulously designed using SolidWorks and then exported to Matlab/Simscape Multibody for co-simulation. Furthermore, the desired trajectories are derived from surface electromyography (sEMG) measured data, aligning our control strategy with the practical needs of the user. Comprehensive experimentation and analysis have yielded compelling numerical findings that underscore the superiority of our proposed method. Across all 10 degrees of freedom, our controller demonstrates a significant advantage over alternative controllers. On average, it exhibits an approximately 45% improvement compared to the Adaptive Backstepping-Based -RBF Controller, a 74% improvement compared to the Model-Free Based Back-Stepping Sliding Mode Controller, and an outstanding 74% improvement compared to the Adaptive Finite Time Control Based on Ultra-local Model and Radial Basis Function Neural Network. Furthermore, when compared to the PID controller, our approach showcases an exceptional improvement of over 80%. These significant findings underscore the effectiveness of our proposed control strategy in enhancing lower limb exoskeleton performance, paving the way for advancements in the field of wearable robotics.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139969200","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}