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International Conference on Ubiquitous Robots (UR)

作者： Shota Chikushi Kenji Kimura Jonghyun Ahn Kazuo Ishii Department of Robotics Kindai University Higashi-Hiroshima-shi Hiroshima Japan Department of Control Engineering National Institute of Technology Matsue College Matsue-shi Shimane Japan Department of Intelligent Mechanical Engineering Hiroshima Institute of Technology Hiroshima-shi Hiroshima Japan Department of Human Intelligence Systems Kyushu Institute of Technology Kitakyushu-shi Fukuoka Japan

To conserve human resources and labor, there is a demand for robots to transport objects. Compared to previous object transport methods, sphere transport by a mobile robot is efficient for transporting objects without loading them and while on the ground. However, previous research on sphere transport has not clarified the methodology for designing and controlling sphere transport based on kinematic and dynamic properties. Therefore, this study attempts to develop a rollersphere motion measurement method for focusing on the roller angular velocity and to evaluate the effectiveness of the roller arrangement requirement. In particular, this study evaluates the “roller arrangement requirement,” which is necessary for roller arrangement (position/orientation) considering kinematic characteristics in sphere transport. Experiments have shown the effectiveness of the roller arrangement requirement with a novel roller-sphere motion measurement method.

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

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Iranian Conference on Electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： Computer aided software engineering Automation Optimization methods Human-robot interaction Collaboration Programming

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LS-SVM Combined with ZNN for Predicting the Continuous Motion Joint Angle of Lower Limb 10

LS-SVM Combined with ZNN for Predicting the Continuous Motio...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Zhang, Xin Liu, Keping Li, Chunxu Yi, Jiang Duan, Xiaoqin Sun, Zhongbo Changchun University of Technology Department of Control Engineering Changchun130000 China University of Plymouth Centre for Robotics and Neural Systems PlymouthPL48AA United Kingdom Medicine of the Second Hospital of Jilin University Department of Rehabilitation Changchun130000 China

ISBN: (纸本)9781665424233

In this paper, a method fusing least squares support vector machine (LS-SVM) with Gaussian kernel function and zeroing neural network (ZNN) is proposed to forecast the continuous motion of lower limb. The surface electromyography (sEMG) signal contains human behavior information and directly reflects the movement intention. In the experiment, the sEMG signal of the lower limbs when the tester is doing leg extension exercise is collected and the real knee joint angle is recorded at the same time. Then the raw sEMG is subjected to a series of preprocessing, and the corresponding muscle activation is gained by calculation. The muscle activation is used as input to the LS-SVM model, and the output to the model is the knee joint angle. LS-SVM transforms the original problem into solving linear equations. When the amount of data is relatively large, the traditional solution method is very time-consuming, and the proposed ZNN method is able to solve the problem, thus speeding up the convergence speed and greatly reducing the learning time. Finally, the back propagation neural network (BPNN) model is utilized to form a comparative experiment. The numerical results indicate that a more stable and better performance is reflected in the raised method, which provides a valuable reference for the research of joint continuous estimation. © 2021 IEEE.

关键词： Numerical methods

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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Efficient Distributed Learning in Stochastic Non-cooperative Games without Information Exchange

arXiv

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arXiv 2022年

作者： Li, Haidong Sheng, Anzhi Peng, Yijie Wang, Long Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China Center for Systems and Control College of Engineering Peking University Beijing China Guanghua School of Management Peking University Beijing China

In this work, we study stochastic non-cooperative games, where only noisy black-box function evaluations are available to estimate the cost function for each player. Since each player's cost function depends on both its own decision variables and its rivals' decision variables, local information needs to be exchanged through a center/network in most existing work for seeking the Nash equilibrium. We propose a new stochastic distributed learning algorithm that does not require communications among players. The proposed algorithm uses simultaneous perturbation method to estimate the gradient of each cost function, and uses mirror descent method to search for the Nash equilibrium. We provide asymptotic analysis for the bias and variance of gradient estimates, and show the proposed algorithm converges to the Nash equilibrium in mean square for the class of strictly monotone games at a rate faster than the existing algorithms. The effectiveness of the proposed method is buttressed in a numerical experiment. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng 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 Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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An Improved Method for Small Target Recognition Based on Faster RCNN 17th

An Improved Method for Small Target Recognition Based on Fas...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Liu, Qun-po Wang, Qi-jing Hanajima, Naohiko Su, Bo Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue Jiaozuo Henan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue Jiaozuo Henan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shi Hokkaido050-8585 Japan

ISBN: (纸本)9789811663239

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Design and Analysis of Individually Demagnetized Power Converter for Switched Reluctance Motors

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8439-8449页

作者： Liu, Hemiao Chen, Hao Wang, Xing Gorbounov, Yassen Wheeler, Patrick Anuchin, Alecksey Demidova, Galina Korovkin, Nikolay Liudmila, Sakhno Shenzhen Research Institute China University of Mining and Technology Shenzhen515100 China China University of Mining and Technology School of Electrical Engineering Xuzhou221116 China Xinjiang Institute of Engineering Ürümqi830000 China International Joint Research Center of Central and Eastern European Countries on New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China International Cooperation Joint Laboratory of New Energy Power Generation and Electric Vehicles of Jiangsu Province Colleges and Universities Xuzhou221008 China Xuzhou Key Laboratory of New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China Mining Production University of Mining and Geology "St. Ivan Rilski " Department of Automation Sofia1700 Bulgaria The University of Nottingham Power Electronics Machines and Control Group NottinghamNG7 2RD United Kingdom Electric Drives Department Moscow111250 Russia Saint-Petersburg National Research University of Information Technologies Mechanics and Optics Faculty of Control Systems and Robotics Engineer Saint Petersburg197101 Russia Peter the Great St. Petersburg Polytechnic University Institute of Energy Saint Petersburg195251 Russia

Switched reluctance motors (SRMs) are capable of operating at high speeds and high loads. The large back EMF results in the excitation winding current being demagnetized more slowly, which, in turn, reduces the efficiency and output of the motor system. This study proposes a novel individually demagnetization power converter (IDPC) specifically designed for SRMs to effectively use the demagnetization energy through an independent demagnetization circuit. Compared with traditional converters, the IDPC can accept direct current (dc) and high-frequency ac inputs while achieving fast demagnetization and energy recovery. The topology and working principle of the IDPC are analyzed in detail in this article using a three-phase 12/8 SRM as the research object. In addition, the influence of the winding demagnetization voltage on the negative torque during the demagnetization phase was studied, as well as the functional implementation and logic control strategy of the IDPC. Simulation and experimental results confirm the validity of the proposed topology and high-frequency ac drive characteristics, as well as their advantages in terms of efficiency, output capacity, and dynamic performance. © 2015 IEEE.

关键词： Reluctance motors

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