Pub Date : 2024-10-28DOI: 10.1016/j.rcim.2024.102877
Sam Pratt , Tadeusz Kosmal , Christopher Williams
Digital twin tools for additive manufacturing (AM) are constrained by the underlying representations of component geometry that are currently in wide use. Mesh, voxel, and parametric surface representations require numerous conversions to intermediate representations at multiple points throughout the processing chain. Each conversion introduces additional error in the geometric representation and complicates comparison of in-situ process sensor data to the as-designed component. Additionally, the limited interoperability of the various representations produced throughout the process chain limit the insights available from current digital twin tools. We introduce a novel framework based on a unifying geometric representation that serves the complete AM digital thread. The presented GPU-accelerated, adaptively sampled distance function (ASDF) framework serves as a foundation for component design and path planning tools, especially for real-time path planning in AM, as well as provides a baseline representation of geometry from control systems, and enables rapid comparison of in-situ sensor data to the as-designed model without intermediate conversion, greatly reducing the burden of reducing such data to usable process insights.
用于增材制造（AM）的数字孪生工具受到目前广泛使用的组件几何图形底层表示法的限制。网格、体素和参数化曲面表示法需要在整个加工链的多个环节进行大量的中间表示法转换。每次转换都会在几何表示法中引入额外的误差，并使原位工艺传感器数据与设计组件的比较变得复杂。此外，在整个加工链中产生的各种表征的互操作性有限，限制了当前数字孪生工具的洞察力。我们引入了一个基于统一几何表示法的新型框架，该表示法可用于整个 AM 数字线程。所介绍的 GPU 加速自适应采样距离函数（ASDF）框架可作为组件设计和路径规划工具的基础，尤其适用于 AM 中的实时路径规划，还可提供来自控制系统的几何基准表示法，并可将现场传感器数据与设计模型进行快速比较，而无需进行中间转换，从而大大减轻了将此类数据还原为可用工艺见解的负担。
{"title":"Adaptively sampled distance functions: A unifying digital twin representation for advanced manufacturing","authors":"Sam Pratt , Tadeusz Kosmal , Christopher Williams","doi":"10.1016/j.rcim.2024.102877","DOIUrl":"10.1016/j.rcim.2024.102877","url":null,"abstract":"<div><div>Digital twin tools for additive manufacturing (AM) are constrained by the underlying representations of component geometry that are currently in wide use. Mesh, voxel, and parametric surface representations require numerous conversions to intermediate representations at multiple points throughout the processing chain. Each conversion introduces additional error in the geometric representation and complicates comparison of <em>in-situ</em> process sensor data to the as-designed component. Additionally, the limited interoperability of the various representations produced throughout the process chain limit the insights available from current digital twin tools. We introduce a novel framework based on a unifying geometric representation that serves the complete AM digital thread. The presented GPU-accelerated, adaptively sampled distance function (ASDF) framework serves as a foundation for component design and path planning tools, especially for real-time path planning in AM, as well as provides a baseline representation of geometry from control systems, and enables rapid comparison of <em>in-situ</em> sensor data to the as-designed model without intermediate conversion, greatly reducing the burden of reducing such data to usable process insights.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102877"},"PeriodicalIF":9.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534212","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}
Trajectory accuracy, a crucial metric in assessing the dynamic performance of grinding robots, is influenced by the uncertain movement of the tool center point, directly impacting the surface quality of processed workpieces. This article introduces an innovative method for compensating trajectory errors. Initially, a strategy for error compensation is derived using differential kinematics theory. Subsequently, a robot kinematic calibration method utilizing ring particle swarm optimization (RPSO) is proposed to address static errors in the grinding robot. Simultaneously, a method for predicting robot joint variables based on a dual-channel feedforward neural network (DCFNN) is designed to mitigate dynamic errors. Finally, a simulation platform is developed to validate the proposed method. Simulation analysis using extensive data demonstrates an 89.3% improvement in absolute position accuracy and a 74.2% reduction in error fluctuation range, outperforming sparrow search algorithm (SSA), improved mayfly algorithm (IMA), multi-representation integrated predictive neural network (MRIPNN), etc. Algorithmic comparison reveals that kinematic calibration significantly reduces the average trajectory error, while joint variable prediction notably minimizes error fluctuation. Validation through trajectory straightness testing and a 3D printing propeller grinding experiment achieves a trajectory straightness of 0.2425 mm. Implementing this method enables achieving 86.1% surface machining allowance within tolerance, making it an optimal solution for grinding robots.
轨迹精度是评估打磨机器人动态性能的关键指标，它受到刀具中心点不确定运动的影响，直接影响加工工件的表面质量。本文介绍了一种创新的轨迹误差补偿方法。首先，利用微分运动学理论推导出一种误差补偿策略。随后，提出了一种利用环形粒子群优化（RPSO）的机器人运动学校准方法，以解决打磨机器人的静态误差问题。同时，设计了一种基于双通道前馈神经网络（DCFNN）的机器人关节变量预测方法，以减少动态误差。最后，开发了一个仿真平台来验证所提出的方法。利用大量数据进行的仿真分析表明，绝对位置精度提高了 89.3%，误差波动范围缩小了 74.2%，优于麻雀搜索算法（SSA）、改进的蜉蝣算法（IMA）、多表征集成预测神经网络（MRIPNN）等。通过算法比较发现，运动校准能显著降低平均轨迹误差，而联合变量预测则能显著减少误差波动。通过轨迹直线度测试和 3D 打印螺旋桨研磨实验验证，轨迹直线度达到 0.2425 毫米。采用这种方法后，表面加工余量在公差范围内达到了 86.1%，成为打磨机器人的最佳解决方案。
{"title":"Trajectory error compensation method for grinding robots based on kinematic calibration and joint variable prediction","authors":"Kaiwei Ma , Fengyu Xu , Qingyu Xu , Shuang Gao , Guo-Ping Jiang","doi":"10.1016/j.rcim.2024.102889","DOIUrl":"10.1016/j.rcim.2024.102889","url":null,"abstract":"<div><div>Trajectory accuracy, a crucial metric in assessing the dynamic performance of grinding robots, is influenced by the uncertain movement of the tool center point, directly impacting the surface quality of processed workpieces. This article introduces an innovative method for compensating trajectory errors. Initially, a strategy for error compensation is derived using differential kinematics theory. Subsequently, a robot kinematic calibration method utilizing ring particle swarm optimization (RPSO) is proposed to address static errors in the grinding robot. Simultaneously, a method for predicting robot joint variables based on a dual-channel feedforward neural network (DCFNN) is designed to mitigate dynamic errors. Finally, a simulation platform is developed to validate the proposed method. Simulation analysis using extensive data demonstrates an 89.3% improvement in absolute position accuracy and a 74.2% reduction in error fluctuation range, outperforming sparrow search algorithm (SSA), improved mayfly algorithm (IMA), multi-representation integrated predictive neural network (MRIPNN), etc. Algorithmic comparison reveals that kinematic calibration significantly reduces the average trajectory error, while joint variable prediction notably minimizes error fluctuation. Validation through trajectory straightness testing and a 3D printing propeller grinding experiment achieves a trajectory straightness of 0.2425 mm. Implementing this method enables achieving 86.1% surface machining allowance within tolerance, making it an optimal solution for grinding robots.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102889"},"PeriodicalIF":9.1,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534211","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 : 2024-10-24DOI: 10.1016/j.rcim.2024.102887
Ali Khishtan , Seong Hyeon Kim , Jihyun Lee
The joint deflection of robots in machining degrades product accuracy. Compliance error compensation has been investigated to reduce the static deflection of robotic machining. The challenge in compliance error compensation is accurately measuring the deflection or cutting force. External sensors have been used to measure them in robotic machining, but it is not practical. The authors proposed a nonlinear disturbance observer to indirectly measure the cutting force online in robotic machining in the previous study. The observer, however, needs to utilize the robot model that includes characteristics of high nonlinearity, uncertainty, and high dynamic variation for different robot postures. After investigating these challenges of modeling, this paper proposes a hybrid modeling approach combining a physics-based model with a new empirical friction model, and a data-driven model to accurately estimate the cutting force while minimizing the error of the robot's mathematical model. The joint torque calculated from the hybrid model can cover the effect of joints' postures and speeds on the varying dynamic in its workspace. Real-time optimization just before cutting is also proposed to adapt to the real-time joint's motion conditions. The experimental results from aluminum multi-axis cutting show that the estimated cutting force via the nonlinear disturbance observer based on the proposed hybrid modeling approach can improve its accuracy up to 45% and 74% in the x and y directions respectively, compared to the physics-based modeling approach. The deflection of the tool center point can be compensated by using a compliance error compensation method up to 79.1% and 75.4% in the x and y directions, respectively, at 0.5 mm/s feed rate, and up to 77.2% and 78.9% at 3 mm/s feed rate. Consequently, the approaches developed in this paper can solve the problems of conventional robot modeling and improve the accuracy of robot machining.
机器人在加工过程中的关节挠度会降低产品精度。为了减少机器人加工中的静态挠度，人们对顺应误差补偿进行了研究。顺应性误差补偿的难点在于精确测量挠度或切削力。在机器人加工中，外部传感器被用来测量它们，但这并不实用。作者在之前的研究中提出了一种非线性干扰观测器，用于间接在线测量机器人加工中的切削力。然而，该观测器需要利用机器人模型，而机器人模型包括高非线性、不确定性和不同机器人姿态下的高动态变化等特点。在研究了建模所面临的这些挑战后，本文提出了一种混合建模方法，将基于物理的模型与新的经验摩擦模型和数据驱动模型相结合，在精确估算切削力的同时，最大限度地减小机器人数学模型的误差。混合模型计算出的关节扭矩可以涵盖关节姿态和速度对其工作空间内动态变化的影响。此外，还提出了切割前的实时优化，以适应关节的实时运动条件。铝材多轴切削的实验结果表明，与基于物理的建模方法相比，通过基于混合建模方法的非线性扰动观测器估算的切削力在 x 和 y 方向的精度分别提高了 45% 和 74%。在进给速度为 0.5 mm/s 的情况下，使用顺应性误差补偿方法，刀具中心点的偏移在 x 和 y 方向的补偿率分别可达 79.1% 和 75.4%；在进给速度为 3 mm/s 的情况下，补偿率分别可达 77.2% 和 78.9%。因此，本文开发的方法可以解决传统机器人建模的问题，提高机器人加工的精度。
{"title":"A hybrid model in a nonlinear disturbance observer for improving compliance error compensation of robotic machining","authors":"Ali Khishtan , Seong Hyeon Kim , Jihyun Lee","doi":"10.1016/j.rcim.2024.102887","DOIUrl":"10.1016/j.rcim.2024.102887","url":null,"abstract":"<div><div>The joint deflection of robots in machining degrades product accuracy. Compliance error compensation has been investigated to reduce the static deflection of robotic machining. The challenge in compliance error compensation is accurately measuring the deflection or cutting force. External sensors have been used to measure them in robotic machining, but it is not practical. The authors proposed a nonlinear disturbance observer to indirectly measure the cutting force online in robotic machining in the previous study. The observer, however, needs to utilize the robot model that includes characteristics of high nonlinearity, uncertainty, and high dynamic variation for different robot postures. After investigating these challenges of modeling, this paper proposes a hybrid modeling approach combining a physics-based model with a new empirical friction model, and a data-driven model to accurately estimate the cutting force while minimizing the error of the robot's mathematical model. The joint torque calculated from the hybrid model can cover the effect of joints' postures and speeds on the varying dynamic in its workspace. Real-time optimization just before cutting is also proposed to adapt to the real-time joint's motion conditions. The experimental results from aluminum multi-axis cutting show that the estimated cutting force via the nonlinear disturbance observer based on the proposed hybrid modeling approach can improve its accuracy up to 45% and 74% in the <em>x</em> and <em>y</em> directions respectively, compared to the physics-based modeling approach. The deflection of the tool center point can be compensated by using a compliance error compensation method up to 79.1% and 75.4% in the <em>x</em> and <em>y</em> directions, respectively, at 0.5 <em>mm/s</em> feed rate, and up to 77.2% and 78.9% at 3 <em>mm/s</em> feed rate. Consequently, the approaches developed in this paper can solve the problems of conventional robot modeling and improve the accuracy of robot machining.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102887"},"PeriodicalIF":9.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534213","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 : 2024-10-15DOI: 10.1016/j.rcim.2024.102888
Yinghao Cheng , Yingguang Li , Guangxu Li , Xu Liu , Jinyu Xia , Changqing Liu , Xiaozhong Hao
Monitoring tool breakage during computer numerical control machining is essential to ensure machining quality and equipment safety. In consideration of the low cost in long-term use and the non-invasiveness to workspace, using servo signals of machine tools to monitor tool breakage has been viewed as the solution that has great potential to be applied in real industry. However, because machine tool servo signals can only partially and indirectly reflect tool conditions, the accuracy and reliability of existing methods still need to be improved. To overcome this challenge, a novel two-step data-driven tool breakage monitoring method using spindle servo signals is proposed. Since spindle cutting torque is acknowledged as one of the most effective and reliable physical signals for detecting tool breakage, it is introduced as the key intermediate variable from spindle servo signals to tool conditions. The monitored spindle servo signals are used to predict the spindle cutting torque in real time based on a long short-term memory neural network, and then the predicted spindle cutting torque is used to detect tool breakage based on a one-dimensional convolutional neural network. The experimental results show that the proposed method can accurately predict the spindle cutting torque for normal tools and broken tools. Compared with the tool breakage monitoring methods that directly use spindle servo signals, the proposed method has higher detection accuracy and more reliable detection results, and the performance is more stable when increasing the detection frequency and decreasing training data.
{"title":"Tool breakage monitoring driven by the real-time predicted spindle cutting torque using spindle servo signals","authors":"Yinghao Cheng , Yingguang Li , Guangxu Li , Xu Liu , Jinyu Xia , Changqing Liu , Xiaozhong Hao","doi":"10.1016/j.rcim.2024.102888","DOIUrl":"10.1016/j.rcim.2024.102888","url":null,"abstract":"<div><div>Monitoring tool breakage during computer numerical control machining is essential to ensure machining quality and equipment safety. In consideration of the low cost in long-term use and the non-invasiveness to workspace, using servo signals of machine tools to monitor tool breakage has been viewed as the solution that has great potential to be applied in real industry. However, because machine tool servo signals can only partially and indirectly reflect tool conditions, the accuracy and reliability of existing methods still need to be improved. To overcome this challenge, a novel two-step data-driven tool breakage monitoring method using spindle servo signals is proposed. Since spindle cutting torque is acknowledged as one of the most effective and reliable physical signals for detecting tool breakage, it is introduced as the key intermediate variable from spindle servo signals to tool conditions. The monitored spindle servo signals are used to predict the spindle cutting torque in real time based on a long short-term memory neural network, and then the predicted spindle cutting torque is used to detect tool breakage based on a one-dimensional convolutional neural network. The experimental results show that the proposed method can accurately predict the spindle cutting torque for normal tools and broken tools. Compared with the tool breakage monitoring methods that directly use spindle servo signals, the proposed method has higher detection accuracy and more reliable detection results, and the performance is more stable when increasing the detection frequency and decreasing training data.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102888"},"PeriodicalIF":9.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433852","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 : 2024-10-03DOI: 10.1016/j.rcim.2024.102886
Loizos Psarakis, Dimitris Nathanael, Nicolas Marmaras
The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.
{"title":"Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration","authors":"Loizos Psarakis, Dimitris Nathanael, Nicolas Marmaras","doi":"10.1016/j.rcim.2024.102886","DOIUrl":"10.1016/j.rcim.2024.102886","url":null,"abstract":"<div><div>The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102886"},"PeriodicalIF":9.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422723","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 : 2024-10-01DOI: 10.1016/j.rcim.2024.102885
Jixiang Yang, Jinxian Zhang, Tianshu Song, Han Ding
Adaptive allocation of the machining allowance is the crucial factor in ensuring the machining accuracy of complex parts. In this work, we present a multi-objective constraint registration method. First, an improved point cloud segmentation method is developed by combining point search and region data expansion algorithms. Afterward, the machining allowance is accurately calculated by using statistical analysis and multi-point sampling strategies to enhance the calculation accuracy of the point-to-triangular patch distance. Finally, a registration objective function is established by considering the allowance constraints of various geometric regions of the workpiece, and the particle swarm optimization algorithm is used to solve the optimum solution. The proposed multi-constraint registration method realizes optimal allocation of the allowance in different regions, which offers a reference coordinate system for the robotic milling of complex free-formed parts. Simulation and experimental results reveal that the developed method satisfies the minimum registration error while ensuring the allocation of allowance in the robotic milling of the casing cavity compared with other methods.
{"title":"A point cloud registration algorithm considering multi-allowance constraints for robotic milling of complex parts","authors":"Jixiang Yang, Jinxian Zhang, Tianshu Song, Han Ding","doi":"10.1016/j.rcim.2024.102885","DOIUrl":"10.1016/j.rcim.2024.102885","url":null,"abstract":"<div><div>Adaptive allocation of the machining allowance is the crucial factor in ensuring the machining accuracy of complex parts. In this work, we present a multi-objective constraint registration method. First, an improved point cloud segmentation method is developed by combining point search and region data expansion algorithms. Afterward, the machining allowance is accurately calculated by using statistical analysis and multi-point sampling strategies to enhance the calculation accuracy of the point-to-triangular patch distance. Finally, a registration objective function is established by considering the allowance constraints of various geometric regions of the workpiece, and the particle swarm optimization algorithm is used to solve the optimum solution. The proposed multi-constraint registration method realizes optimal allocation of the allowance in different regions, which offers a reference coordinate system for the robotic milling of complex free-formed parts. Simulation and experimental results reveal that the developed method satisfies the minimum registration error while ensuring the allocation of allowance in the robotic milling of the casing cavity compared with other methods.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102885"},"PeriodicalIF":9.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359647","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}
Trajectory planning is crucial in the motion planning of robots, where finding the time-optimal path parameterization (TOPP) of a given path subject to kinodynamic constraints is an important component of trajectory planning. The tangential discontinuity at the intersection of continuous line segments limits the speed of trajectory planning and can easily cause jitter and over-constraint phenomena. Smooth transitions at corners can be achieved by inserting parameter spline curves. However, due to the insensitivity of parameter spline curves to arc length, their performance in the application of the TOPP algorithm, which relies on the higher-order robot kinematics smoothness (i.e., the function of the configuration space to the Cartesian space), fails to meet expectations.
A smoothing method suitable for the TOPP algorithm is proposed: Sigmoid Angle-Arc Curve (SAAC). This curve exhibits excellent performance in smooth corner transitions of the TOPP algorithm and is parameterized using arc length. The curvature and geometry of its curves can be expressed analytically in terms of arc lengths. Compared with the traditional B-spline method and the symmetric Euler spiral blending (SE-spiral), SAAC can provide smoother robot kinematics characteristics. Using the TOPP algorithm based on SAAC can significantly enhance the robustness of the TOPP algorithm, significantly reduce jerks, and reduce the time required for movement.
{"title":"Sigmoid angle-arc curves: Enhancing robot time-optimal path parameterization for high-order smooth motion","authors":"Shize Zhao, Tianjiao Zheng, Chengzhi Wang, Ziyuan Yang, Tian Xu, Yanhe Zhu, Jie Zhao","doi":"10.1016/j.rcim.2024.102884","DOIUrl":"10.1016/j.rcim.2024.102884","url":null,"abstract":"<div><div>Trajectory planning is crucial in the motion planning of robots, where finding the time-optimal path parameterization (TOPP) of a given path subject to kinodynamic constraints is an important component of trajectory planning. The tangential discontinuity at the intersection of continuous line segments limits the speed of trajectory planning and can easily cause jitter and over-constraint phenomena. Smooth transitions at corners can be achieved by inserting parameter spline curves. However, due to the insensitivity of parameter spline curves to arc length, their performance in the application of the TOPP algorithm, which relies on the higher-order robot kinematics smoothness (i.e., the function <span><math><mrow><mi>q</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow></mrow></math></span> of the configuration space to the Cartesian space), fails to meet expectations.</div><div>A smoothing method suitable for the TOPP algorithm is proposed: Sigmoid Angle-Arc Curve (SAAC). This curve exhibits excellent performance in smooth corner transitions of the TOPP algorithm and is parameterized using arc length. The curvature and geometry of its curves can be expressed analytically in terms of arc lengths. Compared with the traditional B-spline method and the symmetric Euler spiral blending (SE-spiral), SAAC can provide smoother <span><math><msup><mrow><mi>C</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> robot kinematics characteristics. Using the TOPP algorithm based on SAAC can significantly enhance the robustness of the TOPP algorithm, significantly reduce jerks, and reduce the time required for movement.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102884"},"PeriodicalIF":9.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326346","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}
Nowadays, Industry 5.0 starts to gain attention, which advocates that intelligent manufacturing should adequately consider the roles and needs of humans. In this context, how to enhance human capabilities or even liberate humans from the processes of perception, learning, decision-making, and execution has been one of the key issues to be addressed in intelligent manufacturing. Large language models (LLMs), as the breakthrough in new-generation artificial intelligence, could provide human-like interaction, reasoning, and replies suitable for various application scenarios, thus demonstrating significant potential to address the above issues by providing aid or becoming partners for humans in perception, learning, decision-making, and execution in intelligent manufacturing. The combination of LLMs and intelligent manufacturing has inherent advantages and is expected to become the next research hotspot. Hence, this paper primarily conducts a systematic literature review on the application of LLMs in intelligent manufacturing to identify the promising research topics with high potential for further investigations. Firstly, this paper reveals the concept, connotation, and foundational architecture of LLMs. Then, several typical and trending interdisciplinary LLM applications, such as healthcare, drug discovery, social & economic, education, and software development, are summarized, on which an LLM-enabled intelligent manufacturing architecture is designed to provide a reference for applying LLMs in intelligent manufacturing. Thirdly, the specific pathways for applying LLMs in intelligent manufacturing are explored from the perspectives of design, production, and service. Finally, this paper identifies the limitations, barriers, and challenges that will be encountered during the research and application of LLMs in intelligent manufacturing, while providing potential research directions to address these limitations, barriers, and challenges.
{"title":"A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing","authors":"Chao Zhang , Qingfeng Xu , Yongrui Yu , Guanghui Zhou , Keyan Zeng , Fengtian Chang , Kai Ding","doi":"10.1016/j.rcim.2024.102883","DOIUrl":"10.1016/j.rcim.2024.102883","url":null,"abstract":"<div><div>Nowadays, Industry 5.0 starts to gain attention, which advocates that intelligent manufacturing should adequately consider the roles and needs of humans. In this context, how to enhance human capabilities or even liberate humans from the processes of perception, learning, decision-making, and execution has been one of the key issues to be addressed in intelligent manufacturing. Large language models (LLMs), as the breakthrough in new-generation artificial intelligence, could provide human-like interaction, reasoning, and replies suitable for various application scenarios, thus demonstrating significant potential to address the above issues by providing aid or becoming partners for humans in perception, learning, decision-making, and execution in intelligent manufacturing. The combination of LLMs and intelligent manufacturing has inherent advantages and is expected to become the next research hotspot. Hence, this paper primarily conducts a systematic literature review on the application of LLMs in intelligent manufacturing to identify the promising research topics with high potential for further investigations. Firstly, this paper reveals the concept, connotation, and foundational architecture of LLMs. Then, several typical and trending interdisciplinary LLM applications, such as healthcare, drug discovery, social & economic, education, and software development, are summarized, on which an LLM-enabled intelligent manufacturing architecture is designed to provide a reference for applying LLMs in intelligent manufacturing. Thirdly, the specific pathways for applying LLMs in intelligent manufacturing are explored from the perspectives of design, production, and service. Finally, this paper identifies the limitations, barriers, and challenges that will be encountered during the research and application of LLMs in intelligent manufacturing, while providing potential research directions to address these limitations, barriers, and challenges.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102883"},"PeriodicalIF":9.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322543","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 : 2024-09-24DOI: 10.1016/j.rcim.2024.102882
Yi Gong , Xiangli Li , Rui Zhou , Miao Li , Sheng Liu
Carbon fiber reinforced polymers (CFRP) have significant applications in aerospace and automotive manufacturing. However, due to the complexity of CFRP structures, manufacturing defects are challenging to avoid and even affect the mechanical properties. Timely detection and repair are essential to ensure product quality. In this study, we propose a robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement. First, the influence of three typical defects (delamination, wrinkle and impurity) on mechanical properties is analyzed through numerical analysis. Then, a defect detection model is improved using the channel attention mechanism and decoupling head module, which enhances detection accuracy and the ability to identify small and deep defects. Based on the identification result, a corresponding repair strategy is generated, which considers the effects of force, path, heating and repair modes. The experimental results demonstrate that the tensile stiffness and bending strength of the repaired material are improved by 12.34% and 230.92%, respectively.
{"title":"A robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement","authors":"Yi Gong , Xiangli Li , Rui Zhou , Miao Li , Sheng Liu","doi":"10.1016/j.rcim.2024.102882","DOIUrl":"10.1016/j.rcim.2024.102882","url":null,"abstract":"<div><div>Carbon fiber reinforced polymers (CFRP) have significant applications in aerospace and automotive manufacturing. However, due to the complexity of CFRP structures, manufacturing defects are challenging to avoid and even affect the mechanical properties. Timely detection and repair are essential to ensure product quality. In this study, we propose a robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement. First, the influence of three typical defects (delamination, wrinkle and impurity) on mechanical properties is analyzed through numerical analysis. Then, a defect detection model is improved using the channel attention mechanism and decoupling head module, which enhances detection accuracy and the ability to identify small and deep defects. Based on the identification result, a corresponding repair strategy is generated, which considers the effects of force, path, heating and repair modes. The experimental results demonstrate that the tensile stiffness and bending strength of the repaired material are improved by 12.34% and 230.92%, respectively.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102882"},"PeriodicalIF":9.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315697","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 : 2024-09-23DOI: 10.1016/j.rcim.2024.102881
Yaohua Zhou , Chin-Yin Chen , Guilin Yang , Chi Zhang
In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.
{"title":"Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations","authors":"Yaohua Zhou , Chin-Yin Chen , Guilin Yang , Chi Zhang","doi":"10.1016/j.rcim.2024.102881","DOIUrl":"10.1016/j.rcim.2024.102881","url":null,"abstract":"<div><div>In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102881"},"PeriodicalIF":9.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310867","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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