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6th International Conference on Life System Modeling and Simulation, LSMS 2020, and 6th International Conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2020

作者： Zhang, Shuxiao Ma, Zhiyuan Lin, Li Feng, Wei Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China NDT & E Laboratory Dalian University of Technology Dalian116000 China Guangdong Ultrasonic Nondestructive Testing Engineering Technology Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9789813363779

According to SEM photos of ZrO2 coatings prepared by plasma spraying, 24 physical models with different porosities were constructed, and ultrasonic testing was simulated by finite-difference time-domain method, and the change of longitudinal wave velocity under porosities was analyzed according to the ultrasonic reflection coefficient amplitude spectroscopy. The simulation shows that the longitudinal wave velocity and porosity of the coating show a linear correlation given that the pore morphology and distribution state are unchanged. The simulation is verified by experiments, and it is found that the experiments and the simulation are in good agreement. This research work has important value for the determination of longitudinal wave velocity of thermal barrier coating in ultrasonic testing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Thermal barrier coatings

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Evaluation of LSTM for predicting grip strength using electromyography: a comparison of setups and methods

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Neural Computing and Applications 2025年

作者： Anam, Khairul Sudrajat, Ahmad Rizal, Naufal Ainur Intyanto, Gramandha Wega Muldayani, Wahyu Negara, Mohamad Agung Prawira Sumardi Bukhori, Saiful Gitakarma, Made Santo Castellini, Claudio Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Jawa Timur Jember68121 Indonesia Intelligent Systems and Robotics Lab CDAST Universitas Jember Jl. Kalimantan 37 Jawa Timur Jember68121 Indonesia Faculty of Computer Science CDAST Universitas Jember Jl. Kalimantan 37 Jawa Timur Jember68121 Indonesia Department of Electronic Systems Engineering Technology Universitas Pendidikan Ganesha Jl. Udayana 11 Bali Singaraja81116 Indonesia Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Nürnberger Str. 74 Bavaria Erlangen91052 Germany

Despite decades of research in prosthetics and myocontrol, using electromyography (EMG) to accurately predict the force a user grasps an object with is still a subject of investigation. Although the problem seems trivial, the optimal EMG setup, able to deliver high prediction accuracy at a minimal economic and computational cost needs to be found. In this work, we compare several EMG setups consisting of one to eight sensors and deep learning methods to find out which combination is most convenient. In particular, we compare long short-term memory (LSTM), together with a stacked autoencoder (LSTM–SAE) and an attention mechanism (LSTMATT). Our experimental results reveal that, while the best performance is attained by LSTM–SAE (coefficient of correlation 0.9867±0.0087, coefficient of determination 0.9676±0.0489, normalized root mean square error 0.048±0.0213), statistically significant differences can only be found when the number of sensors is drastically reduced, namely to 2 sensors, in which case, anyway, the performance is still close to optimal and even surpasses state-of-the-art methods. Further research will focus on testing the optimal approach and setup online on amputated users using prosthetic hardware in daily living activities. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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Adaptive Unscented Kalman Filter-based Disturbance Rejection With Application to High Precision Hydraulic Robotic Control

Adaptive Unscented Kalman Filter-based Disturbance Rejection...

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Peng Lu Timothy Sandy Jonas Buchli Interdisciplinary Division of Aeronautical and Aviation Engineering Hong Kong Polytechnic University HKSAR China Agile & Dexterous Robotics Lab Institute of Robotics and Intelligent Systems ETH Zürich Switzerland

This paper presents a novel nonlinear disturbance rejection approach for high precision model-based control of hydraulic robots. While most disturbance rejection approaches make use of observers, we propose a novel adaptive Unscented Kalman Filter to estimate the disturbances in an unbiased minimum-variance sense. The filter is made adaptive such that there is no need to tune the covariance matrix for the disturbance estimation. Furthermore, whereas most model-based control approaches require the linearization of the system dynamics, our method is nonlinear which means that no linearization is required. Through extensive simulations as well as real hardware experiments, we demonstrate that our proposed approach can achieve high precision tracking and can be readily applied to most robotic systems even in the presence of uncertainties and external disturbances. The proposed approach is also compared to existing approaches which demonstrates its superior tracking performance.

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Using robotics to train students for Industry 4.0

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IFAC-PapersOnLine 2019年第9期52卷 153-158页

作者： Elisa Tosello Nicola Castaman Emanuele Menegatti Intelligent Autonomous Systems Lab (IAS-Lab) Department of Information Engineering University of Padova Via Gradenigo 6/B 35131 Padova Italy IT+Robotics srl Contrà Valmerlara 21 36100 Vicenza Italy

This paper presents the master course on Autonomous robotics that we offer at the School of engineering of the University of Padova (Italy). Its novelty is the assignment of a lab project carefully designed to train students on autonomous and industrial robotics in the framework of Industry 4.0: the “Industry 4.0 robotics Challenge”. Students have to program both a manipulator and a mobile robot, together with a 3D vision system, in order to collaborate in the fulfillment of a pick-place-transport industrial task. We adopt a constructionist approach: project-based learning and team-based learning are applied to robotics and Industry 4.0. The project is organized as a challenge to motivate students to propose innovative ideas. A survey on students’ satisfaction is reported at the end of the paper. We made the description of both the hardware and software setup, together with tutorials and wikis, publicly available to let other robotics instructors replicate our proposal and make it a point of reference for teaching robotics in the frame of Industry 4.0.

关键词： Education robotics ROS Industry 4.0 Robotic manipulation Robot navigation

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Fuzzy Adaptive Control-based Real-time Obstacle Avoidance under Uncertain Perturbations

Fuzzy Adaptive Control-based Real-time Obstacle Avoidance un...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Yingbai Hu Guang Chen Longbin Zhang Hang Su Mengyao Li Yunus Schmirander Hu Cao Alois Knoll Chair of Robotics Artificial Intelligence and Realtime Systems Technical University of Munich Munich Germany College of Automotive Engineering Tongji University Shanghai China KTH MoveAbility Lab KTH Royal Institute of Technology Stockholm Sweden Politecnico di Milano Milano Italy Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Dynamic Movement Primitives (DMPs) framework is a powerful approach to imitate motor skills, which has outstanding characteristics, such as convergence to the goal position and good imitation performance. Considering complex motion scenes of manipulators, such as changing the goal position or adding obstacles, the original DMPs framework is not sufficient for the requirements. In this paper, we propose a learning control-based hierarchical control strategy to adapt to new goal positions and avoid obstacles: the high-level learning scheme is targeted at imitating the motor skill and generating the optimization trajectory for obstacle avoidance; the lower-level control scheme focuses on the safety and stability of the robot's movement with unknown disturbances. Firstly, the enhanced DMPs framework is presented to imitate the trajectory from human demonstrations, where the novel DMPs can adapt to new goal position with the changing goal, and avoid single or multiple obstacles. Then, the fuzzy adaptive control method is employed to control redundant manipulators, where the fuzzy logic system (FLS) is incorporated to approximate an unknown nonlinear function term of the unknown disturbance. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with simulation results. The results show that the developed hierarchical strategy has good performance for new goal adaptation and obstacle avoidance.

关键词： Collision avoidance Trajectory Manipulator dynamics Nonlinear dynamical systems Adaptive control

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Stairs reconstruction with 3D point cloud for gait generation of lower limb exoskeleton robot

Stairs reconstruction with 3D point cloud for gait generatio...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Feng, Yachun Xia, Linqing He, Yong Wang, Can Yan, Zefeng Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

With the aging of the population, a considerable number of elderly persons suffer from walking disability. Lower limb exoskeleton robots have been proved to be effective in rehabilitation training and walking assistance. Though significant advancement has been made in the last decade, lacking the ability of adapting to complex terrain is one of the main challenges that restrict the wide application of exoskeleton robots. Thus we focus on the environmental perception and corresponding gait pattern generation of lower limb assistant exoskeleton. In this paper, we introduce a method to generate gait of exoskeleton walking on a stair, which applies the L-0 minimization-based acquisition and reconstruction of stairs information through 3D Point Cloud. The results of experiments show that our method can precisely measure the physical dimension information of stairs. © 2019 IEEE.

关键词： Stairs

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Infrastructure-free Multi-robot Localization with Ultrawideband Sensors

Infrastructure-free Multi-robot Localization with Ultrawideb...

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American Control Conference

作者： Samet Guler Mohamed Abdelkader Jeff S. Shamma Computer Electrical and Mathematical Science and Engineering Division (CEMSE) KAUST Robotics Intelligent Systems and Control (RISC) Lab Thuwal 23955-6900 Saudi Arabia

ISBN: (纸本)9781538679012;9781538679265

Swarm applications use motion capture system or GPS sensors as localization systems. However, motion capture systems provide local solutions, and GPS sensors are not reliable in occluded environments. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard localization framework for multi-robot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor experiments on a two-drone setup. The proposed framework with the dual MCL algorithm yields accurate estimates for various speed profiles of the tag robot, outperforms the standard particle filter and extended Kalman filter, and suffice for a relative position maintenance application.

关键词： Ultra wideband technologies Robot motion capture Sensors Particle filters multi-robot systems Extended Kalman filters Frameworks

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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