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Robotic Arm Automatic Placement Workstation with AI Pneumatic Tube Fault Diagnosis

Robotic Arm Automatic Placement Workstation with AI Pneumati...

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international conference on Control and Robots (ICCR)

作者： Jeng-Dao Lee Hsu-Lun Lin Yu-Cheng Ye Ying-Hsiu Hung Department of Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Yunlin County Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Traditional equipment failures are often identified only when they manifest during production, leading to costly downtime, increased defect rates, and safety hazards. this study focuses on the progressive stages and severity of AI pneumatic tube fault diagnosis, a critical component in industrial operations. By leveraging a sensor and a microphone to collect vibration and audio data, we developed a deep learning-based system to accurately classify and predict the progression of these failures. Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks were trained to identify anomalies in the collected data, effectively distinguishing between different fault stages, from minor issues to major failures. TCN models, with their dilated convolutions, capture long-term dependencies in the data, allowing for early detection of subtle anomalies. LSTM models, using their gated architecture, retain and utilize critical historical information, enabling more precise fault classification. A comparative analysis of various equipment condition monitoring methods demonstrates the superiority of our proposed approach in terms of signal detection and classification accuracy. Additionally, system integration of a Robotic Arm Automatic Placement Workstation and a user-friendly Node-RED-based interface supports real-time monitoring and operator intervention. this integrated system allows seamless communication between hardware components and software, enhancing overall operational efficiency. this study highlights the potential of AI and deep learning to enhance predictive maintenance and operational efficiency in industrial settings, particularly for critical equipment like pneumatic tubes.

关键词： Fault diagnosis Vibrations Service robots System integration Pneumatic systems Manipulators Data models Workstations Artificial intelligence Long short term memory

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Research of Control Systems for Robotic Spatial Planning Platforms

Research of Control Systems for Robotic Spatial Planning Pla...

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international conference on Modern Electrical and Energy Systems (MEES)

作者： Yurii Lebedenko Oksana Polyvoda Anatolii Derkachenko Yevhenii Modlo Svitlana Demishonkova Yurii Pylypenko Information and Computer Technologies Department Kyiv National University of Technologies and Design Kyiv Ukraine Automation Robotics and Mechatronics Department Kherson National Technical University Kherson Ukraine Technological Institute State University of Economics and Technology Kryvyi Rih Ukraine Department of Computer Engineering and Electromechanics Kyiv National University of Technologies and Design Kyiv Ukraine

ISBN: (纸本)9798350346848

the work is devoted to the study of the control system of the moving platform of the manipulative robot. the purpose of the work is the development and research of an instrumental complex of control, analysis and modeling of the operation of positioning platforms based on a spherical mechanism for use in automated industrial systems. the state of use of multi-link positioning platforms in various industries was analyzed and their classification was provided, the main areas of use were determined. A comparative analysis of mathematical methods for describing the kinematic properties of positioning platforms was performed, namely: the closed vector contour method, the coordinate transformation method. A model with an analytical apparatus for controlling the drive links of the spherical mechanism according to the given scenarios of the orientation of the positioning platform was developed, as well as a mathematical model of inverse kinematics was made, the working space was modeled, and the service angles were determined, and a calculation and real-life experiment was conducted. For the first time, the work uses the dependencies of analytical geometry to implement algorithms for controlling a spherical mechanism by inverse kinematics, and offers software and hardware that combines visual modeling tools with real-time data transmission to a controller that controls the drivers of a spherical mechanism by direct kinematics. the result of the field experiment is the testing of the proposed control algorithm on the manufactured manipulator mechanism on the most common movements of the platform of the spherical mechanism and its exit to the extreme positions in order to determine the working space and the values of the service angles. A system of remote control of the spherical mechanism at the hardware level with an interactive interface and tracking of the positioning target has been implemented.

关键词： Analytical models Target tracking Robot kinematics Software algorithms Kinematics Aerospace electronics Control systems

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Application of PID algorithms to control robotic platform using Simulink

Application of PID algorithms to control robotic platform us...

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international conference on Electrical Machines, Drives and Power Systems (ELMA)

作者： Martin Dejanov Dept. of Automation and Mechatronics University of Ruse Ruse Bulgaria

the usage of robots in the industry, research and hobby has increased during the years. In the world of academia robots present a perfect test bench for researchers. Many fields like robotics and automatics use them to solve many specific problems. the research area of robots is toward the implementation of new control techniques, new control and machine learning algorithms. Autonomous robots are the main trend in the modern research and industry area. this project is related to the use of MATLAB Simulink in the process of rapid prototyping control algorithms for a robotic platform. the Alphabot2-Pi is used as a research platform. the control algorithms are implemented as software processes started in real-time in the operating system environment of the robot. In this study, Simulink models are developed using standard and non-standard blocks. the system is implemented as the hardware, software and communication tools are used. Simulink toolbox like Embedded Coder is used for automatic code generation, compilation and deploying to the robotic platform. the standard and nonstandard (Fuzzy) PID algorithms are tested to perform and implement the control of the platform steering angle to travel along the specified path. the improvement of PID algorithms is done by varying the coefficients using some heuristic methods. the implementation, configuration and testing of the proposed system capabilities are investigated in a real environment. the study shows that the system, with varying success, is capable of performing the proposed tasks using different control parameters. the results obtained with standard and nonstandard (fuzzy) PID algorithms are satisfactory. Both algorithms solved the proposed task, but the standard PID control algorithm turned out to be better for high-speeds than the fuzzy PID.

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Meta-Learning 3D Shape Segmentation Functions

Meta-Learning 3D Shape Segmentation Functions

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international conference on Autonomous Robots and Agents, ICARA

作者： Yu Hao Hao Huang Shuaihang Yuan Yi Fang NYU Abu Dhabi Abu Dhabi UAE

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

Previous deep learning methods for 3D shape part segmentation often require extensive labeled training data, which can limit their effectiveness on unfamiliar classes with limited data. To tackle this, we introduce a novel meta-learning strategy that regards the 3D shape segmentation function as a task. By training over a number of 3D part segmentation tasks, our method is capable to learn the prior over the respective 3D segmentation function space which leads to an optimal model that is rapidly adapting to new part segmentation tasks. To implement our meta-learning strategy, we propose two novel modules: meta part segmentation learner and part segmentation learner. During the training process, the part segmentation learner is trained to complete a specific part segmentation task in the few-shot scenario. In the meantime, the meta part segmentation learner is trained to capture the prior from multiple similar part segmentation tasks. Based on the learned information of task distribution, our meta part segmentation learner is able to dynamically update the part segmentation learner with optimal parameters which enable our part segmentation learner to rapidly adapt and have great generalization ability on new part segmentation tasks. We demonstrate that our model achieves superior part segmentation performance with the few-shot setting on the widely used dataset: ShapeNet.

关键词： Metalearning Training Deep learning Adaptation models Solid modeling three-dimensional displays Shape

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Confident-anchor-induced multi-source-free domain adaptation 21

Confident-anchor-induced multi-source-free domain adaptation

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Proceedings of the 35th international conference on Neural Information Processing Systems

作者： Jiahua Dong Zhen Fang Anjin Liu Gan Sun Tongliang Liu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences DeSI Lab AAII University of Technology Sydney State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences TML Lab University of Sydney

ISBN: (纸本)9781713845393

Unsupervised domain adaptation has attracted appealing academic attentions by transferring knowledge from labeled source domain to unlabeled target domain. However, most existing methods assume the source data are drawn from a single domain, which cannot be successfully applied to explore complementarily transferable knowledge from multiple source domains with large distribution discrepancies. Moreover, they require access to source data during training, which are inefficient and unpractical due to privacy preservation and memory storage. To address these challenges, we develop a novel Confident-Anchor-induced multisource-free Domain Adaptation (CAiDA) model, which is a pioneer exploration of knowledge adaptation from multiple source domains to the unlabeled target domain without any source data, but with only pre-trained source models. Specifically, a source-specific transferable perception module is proposed to automatically quantify the contributions of the complementary knowledge transferred from multi-source domains to the target domain. To generate pseudo labels for the target domain without access to the source data, we develop a confident-anchor-induced pseudo label generator by constructing a confident anchor group and assigning each unconfident target sample with a semantic-nearest confident anchor. Furthermore, a class-relationship-aware consistency loss is proposed to preserve consistent inter-class relationships by aligning soft confusion matrices across domains. theoretical analysis answers why multi-source domains are better than a single source domain, and establishes a novel learning bound to show the effectiveness of exploiting multi-source domains. Experiments on several representative datasets illustrate the superiority of our proposed CAiDA model.

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Reactive Rhythm Activities and Offline Classification of Imagined Speeds of Finger Movements

Reactive Rhythm Activities and Offline Classification of Ima...

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international conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Yunfa Fu Baolei Xu Lili Pei Hongyi Li State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy and Sciences Shenyang China Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

Reactive rhythm bands to imagined speeds of index finger movement and offline classification of imagined speeds were explored in the paper. EEG was recorded when 4 subjects executed motor imagery of two tasks at first-person perspective that involved left index fingers at two speeds (4Hz and 1Hz, trained and paced by metronome). Relatively prominent rhythmic activities around 10-13 Hz and 24~26 Hz over C3, 12~13 Hz over Cz, and 11~13 Hz, 8~10Hz, and 25~26 Hz over C4 were found by spectrum analysis. the most significant band 9-13Hz was used to construct the feature space. the Fisher discriminant analysis method (FDA), multi-layer perceptron neural network (MLP), and distinction sensitive learning vector quantization (DSLVQ) were applied in offline identification of imagined speeds respectively. the best average misclassification rates between fast and slow movement imagination by only three derivations C3, Cz, and C4 with FDA, MLP, and DSLVQ were 27.5±1%, 31.2±3.7%, and 34.6% at 3260 ms respectively. the results showed that FDA in this study was better than MLP and DSLVQ. this study demonstrated that discrimination for imagined speeds of finger movement was possible and feasible. Classification of imagined movement speeds with high accuracy based on EEG is also possible through improving methods in the paper. the study may provide a strategy to realize fine control of robots by brain-controlled robot interface.

关键词： Fingers Electroencephalography Indexes Electrodes Robot sensing systems Mobile robots

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Design and Fabrication of a Microinjector to Conduct Columnar Injection of Stem Cells for Treating Parkinson's Patients

Design and Fabrication of a Microinjector to Conduct Columna...

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RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Erfun Bayat Sarmadi Sayed Mahdi Same Mahdi Abolghasemi-Dehaqani Arash Bahrami Mohammad-Reza Abolghasemi-Dehaqani Aghil Yousefi-Koma School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran College of Engineering University of Tehran Tehran Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Considering the rising prevalence of neurological diseases like Parkinson's, novel treatment methods have advanced significantly in recent years. One promising approach is transplanting stem cells into targeted brain regions, for which developing precise equipment is essential. A critical method for implanting these cells is columnar injection into brain tissue. the present research focuses on designing and fabricating a microinjector device for the controlled columnar injection of stem cells into the brains of Parkinson's patients. Inspired by existing microdrive models, the newly developed device stands out for using two motors as actuators, two encoders, and a PID controller. this configuration compensates for operational errors and environmental noise, ensuring precise injections at the required rate. the PID controller is designed using system identification, where equations governing the power transmission systems are derived and implemented. To verify the device's performance, the injection rate for a standard procedure (25 microliters at 1 microliter per minute) is measured using encoders and validated with a digital caliper. the instantaneous injection rate is calculated using the encoder and caliper data. While the average of these data is the same, the standard deviation of the caliper data is significantly lower than that of the encoder data. this indicates that the device is functioning as expected and that much of the noise in the encoder data originates from the sensor itself.

关键词： Performance evaluation Working environment noise Noise Power transmission Stem cells Robot sensing systems Mathematical models System identification Object recognition Standards

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Conditional Diffusion Model for Anti-Interference in Train Satellite Navigation Receiver Signals

Conditional Diffusion Model for Anti-Interference in Train S...

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IEEE international conference on Communication and Information Systems (ICCIS)

作者： Zhiwei Zhang Yinghong Wen Jinbao Zhang Dan Zhang Jie Ren School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331531171

ISBN: (纸本)9798331531188

In this paper, a Conditional Diffusion Model (CDM) based signal anti-jamming algorithm is proposed to solve the problem that traditional algorithms such as Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) are not good at dealing with interference signals in train satellite navigation receivers. We use one-dimensional Quadrature Phase Shift Keying (QPSK) time domain signals as input conditions, and then use CDM to predict the noise distribution from the fully noisy signal and reconstruct the QPSK signal. Experimental analysis shows that CDM has significant advantages over traditional methods in denoising QPSK signals, and shows higher accuracy under high-intensity noise interference, and the Signal-to-Noise Ratio (SNR) can be increased from -25dB to 30dB. this provides precise navigation and positioning support for train operation.

关键词： Satellites Receivers Interference Satellite navigation systems Diffusion models Prediction algorithms Discrete wavelet transforms Time-domain analysis Signal to noise ratio Quadrature phase shift keying

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Circuit as set of points 23

Circuit as set of points

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Jialv Zou Xinggang Wang Jiahao Guo Wenyu Liu Qian Zhang Chang Huang School of EIC Huazhong University of Science and Technology Horizon Robotics

As the size of circuit designs continues to grow rapidly, artificial intelligence technologies are being extensively used in Electronic Design automation (EDA) to assist with circuit design. Placement and routing are the most time-consuming parts of the physical design process, and how to quickly evaluate the placement has become a hot research topic. Prior works either transformed circuit designs into images using hand-crafted methods and then used Convolutional Neural Networks (CNN) to extract features, which are limited by the quality of the handcrafted methods and could not achieve end-to-end training, or treated the circuit design as a graph structure and used Graph Neural Networks (GNN) to extract features, which require time-consuming preprocessing. In our work, we propose a novel perspective for circuit design by treating circuit components as point clouds and using Transformer-based point cloud perception methods to extract features from the circuit. this approach enables direct feature extraction from raw data without any preprocessing, allows for end-to-end training, and results in high performance. Experimental results show that our method achieves state-of-the-art performance in congestion prediction tasks on both the CircuitNet and ISPD2015 datasets, as well as in design rule check (DRC) violation prediction tasks on the CircuitNet dataset. Our method establishes a bridge between the relatively mature point cloud perception methods and the fast-developing EDA algorithms, enabling us to leverage more collective intelligence to solve this task. To facilitate the research of open EDA design, source codes and pre-trained models are released at https://***/hustvl/circuitformer.

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Research on the PID Control Method for Indoor Heating Temperature Based on the Improved Coati Optimization Algorithm

Research on the PID Control Method for Indoor Heating Temper...

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IEEE international conference on Control and robotics Engineering (ICCRE)

作者： Weixin Wang Liqing Geng Genghuang Yang Yang Huo School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin China Oumingzhuang Biotechnology (Tianjin) Co. Ltd. Tianjin China

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

the proposed solution addresses the issues of high overshoot and long response time in traditional PID control methods for complex nonlinear heating systems with large inertia and pure lag. the Improved Coati Optimization Algorithm (ICOA-PID) introduces an enhanced controller. Firstly, the heating system's primary and secondary piping transfer function models are established. Secondly, the Coati Optimization Algorithm (COA) has been improved in four aspects: the introduction of the chaotic mapping mechanism, nonlinear inertial weighting factors, t-distribution variation, and the coati alarmist mechanism, and the test is carried out using the CEC2022 function set, which performs well in solving most of them in dimension 20; meanwhile, the data are plotted in boxplots. It can be observed that ICOA has both the smallest mean height and the smallest interquartile range (IQR). these verify that the ICOA is able to converge more rapidly, with a greater degree of accuracy and stability than other algorithms. Finally, the simulation experiments were conducted, the step response plot demonstrated that the rise time of the ICOA-PID controller was reduced by 5 seconds in comparison to the pre-improvement period, and by approximately 10 seconds in comparison to the DBO-PID controller. Additionally, the amount of overshoot was reduced by 2% in comparison to the pre-improvement period; the fitness value curves indicate that the fitness value of the ICOA-PID controller is consistently minimized, indicating that it identifies the most optimal set of PID parameters. the error curve exhibits a rapid and pronounced decline, indicating the optimization of parameters to achieve both dynamic responsiveness and static accuracy within the system. the aforementioned results substantiate that the IOCA-PID control methodology is more effective in meeting the indoor thermal demand demands of winter users compared to other algorithmic controllers, and thus possesses certain practical relevance

关键词： PI control Accuracy Heat engines Water heating Transfer functions Stability analysis PD control

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