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Development of Mobile Manipulator System Providing Grounded Haptic Information During Gait Training for the Stroke

Journal of Institute of control, robotics and Systems 2025年第4期31卷 265-268页

作者： Lee, Junyeong H., Lee M., Kim J., Han J., Yoon Department of AI Convergence Gwangju Institute of Science and Technology Korea Republic of Department of Control and Robot Engineering & School of Aerospace Engineering Gyeongsang National University Korea Republic of Department of Physical Medicine and Rehabilitation Chonnam National University Hospital Korea Republic of

Effective gait training of the stroke can be performed by leveraging grounded haptic information obtain by touching an external immobile object. robotic systems with various preset curvatures allow users to undergo gait training without spatial restrictions, as well as straight-path gait training. We developed a mobile manipulator platform in which the handle is positioned along the preset gait-training path while the robot guides the user by providing grounded haptic information along the designated path as a rail. To verify the system’s functionality, experiments were conducted for various curvatures. Based on the results, the robot can guide the user along the defined track while providing grounded haptic information. Further investigation is required to verify the system’s effectiveness across a broad patient population. © ICROS 2025.

关键词： Electric grounding

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Bacterial Aerosol Inactivation by DBD Plasma: Mechanisms, Kinetic Model, and Disinfection Dose

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IEEE Transactions on Plasma Science 2025年第4期53卷 539-552页

作者： Zhang, Liyang Zhang, Xu Zhou, Qun Kaiyue, Wu Luo, Haiyun Guo, Yuntao Tie, Jinfeng Fu, Yangyang Tsinghua University Department of Electrical Engineering Beijing100084 China Tsinghua Shenzhen International Graduate School Guangdong Shenzhen518055 China Tsinghua University Department of Chemistry Beijing100084 China Beijing Institute of Technology School of Aerospace Engineering Beijing100081 China Disinfection and Infection Control Chinese PLA Center for Disease Prevention and Control Beijing100071 China

Atmospheric pressure dielectric barrier discharge (DBD) plasma has emerged as a promising method for efficient air disinfection in building environments. However, the mechanisms, kinetics, and disinfection doses required for bioaerosol inactivation by plasmas remain inadequately understood. In this study, we first applied optical photothermal infrared (O-PTIR) spectroscopy to examine the biological responses of bacterial aerosols to plasmas at the single-cell level. Significant carbonylation was observed in plasma-treated cells, indicating that oxidation is a crucial mechanism in bacterial aerosol inactivation. A preliminary multispecies kinetic model was proposed for bioaerosol disinfection by plasmas. By differentiating the roles of charged particles and neutral chemical species, the equivalent total chemical dose (ETCD) and equivalent total ionic dose (ETID) were introduced. ETCD primarily considers the oxidation potential of the reactive species in the plasma. The proposed model was applied to a DBD plasma-based air disinfection system and preliminarily validated. The relationship between the macroscopic dose (specific energy density, SED) and microscopic dose (ETCD) was uncovered. This study provides valuable insights into the modeling of plasma-bioaerosol interactions and the underlying mechanisms, offering a theoretical foundation for practical applications. © 1973-2012 IEEE.

关键词： Aerosols

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Collaborative Improvement of Structure Shape and Surface Integrity in Titanium Alloy Hole Burnishing

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Chinese Journal of Mechanical engineering 2025年第1期38卷 186-205页

作者： Jiahui Liu Pingfa Feng Zibiao Wang Jianfu Zhang Feng Feng Xiangyu Zhang Beijing Key Laboratory of Precision/Ultra-precision Manufacturing Equipments and Control Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Tribology in Advanced Equipment Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China Division of Advanced Manufacturing Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

In the aerospace field,hole burnishing enhancement plays an essential role in improving the service performance of load-bearing *** satisfy the assembly accuracy and strength requirements,the structure shape and surface integrity must be considered simultaneously during the enhancement *** current manufacturing process of hole burnishing has a relatively weak balance between the structure shape and surface integrity;therefore,it is necessary to analyze the mechanism and optimize the parameters to improve the strengthening effect of the *** this study,a two-dimensional longitudinal simplified model for the hole burnishing process was established,and the reasons for the surface roughness improvement of the hole wall and material accumulation on the upper surface were *** were conducted to determine the influence of the burnishing parameters on the structure shape(material accumulation,shape contour,and roundness)and surface integrity(surface roughness,residual stress,and surface hardness),based on the opposite requirements of improving the structure shape and surface integrity for the burnishing depth(BD).The results showed that with an increase in the BD,the structure shape deteriorated,whereas the surface integrity *** behavior verification experiments were conducted,and parameter selection schemes for the collaborative improvement of the structure shape and surface integrity were *** the holes of titanium alloy TB6(Ti-10V-2Fe-3Al),the fatigue life can be increased by 162%when the BD,spindle speed,and feed rate were 0.20 mm,200 r/min,and 0.2 mm/r,respectively.

关键词： Hole burnishing Surface roughness Material accumulation Structure shape Titanium alloy Fatigue life

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Predictive Vehicle control at Intersections with Look-Ahead Driving Approach

Predictive Vehicle Control at Intersections with Look-Ahead ...

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2025 SICE International Symposium on control Systems, SICE ISCS 2025

作者： Magzhan, Atykhan Hossain, Md Delowar Bakibillah, A.S.M. Kamal, Md Abdus Samad Yamada, Kou Graduate School of Science and Technology Gunma University Kiryu Japan Institute of Science Tokyo Department of Systems and Control Engineering Tokyo Japan

ISBN: (纸本)9784907764852

This paper presents a look-ahead vehicle control scheme for efficient traffic management at signalized intersections. The proposed scheme predicts future driving states with a look-ahead approach as an extended adaptive cruise control (ACC) model. It maintains car-following behavior similar to human driving while predicting future states of traffic flow to minimize intersection clearing time and aggressive braking independently, similar to model predictive control (MPC) approaches. In contrast to MPC models that are computationally demanding and have limited practical applications due to real-time constraints, the proposed scheme uses a modified Intelligent Driver Model (IDM) with predictive adjustments. The efficiency of the control scheme is evaluated through simulations with multiple vehicles in intersection traffic scenarios, focusing on maintaining safe time headway within a traffic flow and ensuring efficient flows with higher intersection throughput. Comparative results indicate improvements in intersection clearance, human-like vehicle headway management, and enhanced traffic flow performance. © 2025 The Society of Instrument and control Engineers.

关键词： Adaptive cruise control

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Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move

Self-Supervised Learning of Grasping Arbitrary Objects On-th...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Kiyokawa, Takuya Nagata, Eiki Tsurumine, Yoshihisa Kwon, Yuhwan Matsubara, Takamitsu Division of Information Science Robot Learning Laboratory Nara Ikoma Japan Osaka University Graduate School of Engineering Science Department of Systems Innovation Osaka Toyonaka Japan

ISBN: (纸本)9798331531614

Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise timing and pose adjustments. Self-supervised learning can develop a generalizable policy to adjust the robot's velocity and determine grasp position and orientation based on the target object's shape and pose. Due to mobile grasping's complexity, action primitivization and step-by-step learning are crucial to avoid data sparsity in learning from trial and error. This study simplifies mobile grasping into two grasp action primitives and a moving action primitive, which can be operated with limited degrees of freedom for the manipulator. This study introduces three fully convolutional neural network (FCN) models to predict static grasp primitive, dynamic grasp primitive, and residual moving velocity error from visual inputs. A two-stage grasp learning approach facilitates seamless FCN model learning. The ablation study demonstrated that the proposed method achieved the highest grasping accuracy and pick-and-place efficiency. Furthermore, randomizing object shapes and environments in the simulation effectively achieved generalizable mobile grasping. © 2025 IEEE.

关键词： Self-supervised learning

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Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation

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IEEE Access 2025年 13卷 87112-87121页

作者： La, Thai Tao, Linh Watanabe, Dai Shibaura Institute of Technology Graduate School of Engineering and Science Tokyo 135-8548 Japan Hanoi University of Science and Technology School of Mechanical Engineering Hanoi 100000 Viet Nam Shibaura Institute of Technology Department of Machinery and Control Systems Tokyo 135-8548 Japan

Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment on resource-constrained platforms like edge devices. In this paper, we present a novel approach that completely avoids the use of 3D convolutions, with the goal of achieving faster inference speeds while maintaining comparable accuracy to existing state-of-the-art methods. Our proposed solution revolves around a 2D cost aggregation technique, which serves as a viable alternative to traditional 3D convolutions, delivering similar results in terms of accuracy. This innovative method significantly reduces computational overhead, facilitating more efficient resource utilization and paving the way for real-time applications. Complementing our 2D cost aggregation module, we introduce a multi-stage feature extractor, designed to enhance feature representation while remaining straightforward and lightweight. The integration of the 2D cost aggregation and multi-stage feature extraction results in an efficient architecture for cost aggregation, simplifying the model and ensuring computational efficiency without sacrificing accuracy. This framework delivers high-performance stereo matching suitable for devices with limited computational capabilities. Through evaluation on benchmark datasets, we demonstrate the effectiveness of our approach, highlighting its potential for real-time 3D perception in applications. By addressing the constraints imposed by 3D convolutions and offering a more pragmatic alternative, this work bridges the gap between high-performance stereo matching algorithms and the realities of edge computing environments. © 2013 IEEE.

关键词： 2D cost aggregation Autonomous driving depth estimation edge device stereo matching

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Robust Health Monitoring for Lithium-Ion Batteries under Guidance of Proxy Labels: A Deep Multi-Task Learning Approach

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IEEE Transactions on Power Electronics 2025年第8期40卷 10272-10285页

作者： Guo, Ruohan Zhang, Kui He, Shangyang Tao, Shengyu Zhang, Xuan Liu, Kailong Li, Xiangjun Tian, Jinpeng Shen, Weixiang Chung, Chi Yung The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Research Centre for Grid Modernisation Kowloon999077 Hong Kong Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Shandong University School of Control Science and Engineering Jinan250100 China China Electric Power Research Institute Beijing100192 China Swinburne University of Technology School of Engineering MelbourneVIC3122 Australia

State of health (SOH) estimation is foundational for effective health prognosis and management of lithium-ion batteries. However, conventional data-driven approaches struggle to ensure robust generalisability when training data lacks sufficient SOH labels. In this work, we explore the potential utility of ageing information, gathered from routine operational data beyond the standard input collection region, as proxy labels (PLs) to establish nonlinear mappings between a short charging segment and SOH. To achieve this, we develop a multi-task learning (MTL) framework for battery health monitoring, which comprises two critical components: (1) a deep MTL model, and (2) a multivariate Gaussian process regression (GPR) model. The deep MTL model is designed with four distinct tasks, each with a specific goal to exploit data under different labelling conditions, facilitating the extraction of domain-invariant and target-specific features for PL estimation. Using the estimated PLs, the GPR model is employed to provide precise mappings to SOH. Validations are conducted on batteries with diverse chemistries and cycling temperatures, particularly under severe label scarcity below 5%. The results confirm the effectiveness of the proposed method, outperforming baseline methods and achieving a mean absolute error of less than 1.3% in all case studies. © 1986-2012 IEEE.

关键词： Electronic health record

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Energy-Free Sensing and Context Recognition Using Photovoltaic Cells

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IEEE Sensors Journal 2025年第9期25卷 15598-15611页

作者： Shintani, Kaede Rizk, Hamada Yamaguchi, Hirozumi Osaka University Graduate School of Information Science and Technology Department of Information Networking Suita565-0871 Japan Tanta University Tanta31733 Egypt RIKEN Center for Computational Science Kobe650-0047 Japan RIKEN Center for Computational Science Department of Computer and Control Engineering Kobe650-0047 Japan

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this article, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user’s location. We propose a computationally efficient dynamic time warping (DTW) to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43 cm, respectively. This is achieved while the proposed system is nonintrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition. © 2001-2012 IEEE.

关键词： Photovoltaics

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Visualization of Stress-Strain Distribution on Fatigue Crack With Grain-Level Spatial Resolution via Self-Magnetic Flux Density

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Dewa Made Oka Dharmawan, I. Lee, Jinyi Graduate School of Chosun University Department of Control Instrumentation and Robot Engineering Gwangju61452 Korea Republic of Chosun University Department of Electronic Engineering Gwangju61452 Korea Republic of

Fatigue crack - known for its unpredictability and rapid propagation - is influenced by various physical and chemical factors. A promising approach to understanding crack nucleation and growth involves visualizing local stresses and phase changes around defects. To achieve high-resolution mapping, ideally at the grain level, we propose a method to detect stress-strain maps through self-magnetic flux density (MFD) distribution using a linear three-axis tunnel magnetoresistive (TMR) sensor that measures in all orthogonal directions. Theoretical and experimental bases for the reciprocal relationship between stress components and MFD distribution are discussed. Experiments are conducted on three specimens with artificially induced fatigue cracks generated using a compact test (CT). The defect depths vary from 3 to 7 mm, at depth angles of 0°, 30°, and 60°. These experiments successfully map grain-level stress concentrations near the crack tip, identifying the stress concentration zone (SCZ). This analysis should enhance our understanding of stress-strain behavior associated with fatigue cracks, enabling early detection. © 1963-2012 IEEE.

关键词： Fatigue crack

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