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IEEE International Conference on automation Science and Engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators Computer aided software engineering automation Adaptive systems Control systems End effectors Hardware Dynamical systems Manipulator dynamics

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers

Robust-Tube Model Predictive Control for Piezoelectrically A...

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Artificial Intelligence and automation Control (AIAC), International Conference on

作者： Yu Chai Chen Yang Huijuan Dong Jie Zhao Yuri A. W. Shardt Bingfeng Ju ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Department of Automation Engineering Technical University of Ilmenau Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%.

关键词： Vibrations Uncertain systems Uncertainty Simulation Switches High-voltage techniques Predictive models Trajectory Approximation methods Predictive control

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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Computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Transcranial Direct Current Stimulation Associated with Visual and Auditory Cueing during Gait Training: A Case Study with a Parkinson's Disease Patient

Transcranial Direct Current Stimulation Associated with Visu...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Angie Pino Patricio Barria Karim Baleta Rolando Aguilar José M. Azorín Marcela Múnera Carlos A. Cifuentes Club de Leones Cruz del Sur Rehabilitation Center Punta Arenas Chile Department of Electrical Engineering University of Magallanes Punta Arenas Chile Department of Systems Engineering and Automation Miguel Hernández University of Elche Elche Spain Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Parkinson's Disease (PD) is a neurodegenerative disorder that leads to motor dysfunctions and restricts walking independence. Approaches such as transcranial direct current stimulation (tDCS) and cueing gait are potential rehabilitation strategies that have proven efficient in improving motor function in individuals with PD in preliminary and separate studies. This case study with a single PD patient explores the feasibility and potential effects of combining anodal tDCS (a-tDCS) with visual and auditory cues during 15 gait training sessions. The patient was assessed before the first intervention and after completing all training weeks regarding kinematic parameters of gait, neuronal activation, muscle strength, and functional skills. Results showed a beneficial impact in significantly increasing the participant's speed, stride length, step length, and muscle strength. Furthermore, the decrease in beta brain activity and increase in the delta were associated with a reduction in the effects of the disease, improved motor performance, and shorter functional task times. This study suggests the a-tDCS strategy with double cueing gait can lead to therapeutic potential in relieving the disease's motor symptoms.

关键词： Training Visualization Motor drives Parkinson's disease Kinematics Muscles Motors Neurorehabilitation Transcranial direct current stimulation Robots

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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System Reliability and Safety Engineering (SRSE), International Conference on

作者： Ruiqi Li Kai Wang Peng Zeng Jiwei Cao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical Engineering Harbin Institute of Technology China

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed.

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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Trustworthy Bayesian Perceptrons

Trustworthy Bayesian Perceptrons

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International Conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

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AI Enabled Automatic Mobile Robot Intelligent Navigation in Construction with Obstacle Awareness

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IEEE Transactions on automation Science and Engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

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Multi-stage warm started optimal motion planning for over-actuated mobile platforms

arXiv

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arXiv 2022年

作者： Paz-Delgado, Gonzalo Jesús Pérez-Del-Pulgar, C.J. Azkarate, M. Kirchner, F. García-Cerezo, A. The Space Robotics Laboratory Department of Systems Engineering and Automation Universidad de Málaga Andalucia Tech Málaga29070 Spain The Automation and Robotics Section European Space Agency NoordwijkAZ 2201 Netherlands The DFKI Robotics Innovation Center Bremen Robert-Hooke-Str. 1 Bremen28359 Germany

This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers non-linearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. This novel approach has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time, increasing the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of an over-actuated mobile platform. © 2022, CC BY.

关键词： Rovers

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Multiparametric atomic force microscopy imaging of single native exosomes

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生物化学与生物物理学报（英文版） 2021年第3期53卷 385-388页

作者： Mi Li Xinning Xu Ning Xi Wenxue Wang Xiaojing Xing Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Cancer Hospital of China Medical University Shenyang 110042China Liaoning Cancer Hospital&Institute Shenyang 110042China Department of Industrial and Manufacturing Systems Engineering The University of Hong KongHong Kong 999077China

The advent of atomic force microscopy(AFM)provides a novel powerful tool for addressing biological issues in their native *** uses a sharp tip mounted at the end of a micro-fabricated cantilever to probe the specimens immobilized on the *** with other high-resolution imaging and mea-surement techniques(***-resolution optical microscopy,scan-ning electron microscopy,transmission electron microscopy,and magnetic/optical tweezers),the unique merit of AFM is that it is able to simultaneously acquire the structures and properties of living biological samples under aqueous conditions with unprece-dented spatiotemporal resolution(nanometer spatial resolution and millisecond temporal resolution)[1],making AFM an invalu-able platform for revealing the underlying mechanisms guiding life *** wide applications of AFM in molecular and cell biology provide considerable novel insights into the physi-ological/pathological processes[2].Particularly,in recent years,a new AFM imaging mode,which is called peak force tapping(PFT)mode[3],emerges as a promising way for correlating the topography of the specimens with the mechanics of *** PFT mode,the vibrating AFM tip indents the sample at each sam-pling point to record force curves,and the peak force of the force curve is used as the feedback[4].By analyzing the force curves,several types of mechanical properties of the specimens can be real-timely visualized,and thus PFT mode is also called multiparametric imaging AFM[5],offering novel possibilities for resolving the fine structures and properties of fragile biological specimens.

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