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Automated Nanomanipulation for Repairing Defects on Nanoimprint Lithography Masters

IEEE Transactions on automation Science and Engineering 2025年 22卷 14967-14975页

作者： Meng, Siyuan Jiang, Jiankang Wang, Fubo Zhu, Junhui Zhang, Qianjun Wang, Yong Dong, Wei Ru, Changhai Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150000 China JITRI Micro-Nano Automation System and Equipment Technology Research Institute Jiangsu21500 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215000 China

The fabrication of Nanoimprint Lithography (NIL) masters serves as the initial process in the manufacturing of devices such as silicon photonic chips using NIL. Repairing defects and modifying structures on an NIL master accurately is critical for NIL manufacturing. This study proposes an innovative scanning electron microscope (SEM)-based in-situ nanomanipulation technique to address the macro-micro-nano cross-scale nanopositioning issues necessary for repair functions. A macro/micro closed-loop control system was designed, which includes a frequency/voltage (f/u) proportional controller, a real-time direct inverse hysteresis compensation feedforward controller, a grating displacement sensor, and a cross-scale nanopositioning platform. The performance testing of the nano-manipulation approach resulted in a repair range of 22.18×20.92×10.06 mm3, meeting the repair requirements for most silicon photo chip NIL masters in terms of size. The system achieved a repair accuracy at 4.946 nm (X-axis), 4.663 nm (Y-axis), and 4.679 nm (Z-axis). As a demonstrate, the repair functionality testing confirmed the ability of the system to remove target structures such as cantilever beams and detach adhered particles from a master surface. © 2004-2012 IEEE.

关键词： Nanoimprint lithography

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Person Re-Identification Based on Spatial Feature Learning and Multi-Granularity Feature Fusion

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Journal of Shanghai Jiaotong University (Science) 2025年第2期30卷 363-374页

作者： Diao, Zijian Cao, Shuai Li, Wenwei Liang, Jianan Wen, Guilin Huang, Weixi Zhang, Shouming Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Robotic Laboratory South China Robotics Innovation Research Institute Foshan528300 Guangdong China Foshan Zhiyouren Technology Co. Ltd. Foshan528300 Guangdong China Robotic Laboratory Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou510070 China

In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks, a person re-identification method combining spatial feature learning and multi-granularity feature fusion was proposed. First, an attention spatial transformation network (A-STN) is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial features. Then the network was divided into a global branch, a local coarse-grained fusion branch, and a local fine-grained fusion branch to extract pedestrian global features, coarse-grained fusion features, and fine-grained fusion features, respectively. Among them, the global branch enriches the global features by fusing different pooling features. The local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity features. The local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global features. Finally, the proposed method was compared on three public datasets: Market1501, DukeMTMC-ReID and CUHK03. The experimental results were better than those of the comparative methods, which verifies the effectiveness of the proposed method. © Shanghai Jiao Tong University 2023.

关键词： Convolutional neural networks

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A Spiral Coverage Path Planning Algorithm for Nonomnidirectional Robots

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Journal of Field robotics 2025年第0期

作者： Hou, Taogang Li, Jiaxin Pei, Xuan Wang, Hao Liu, Tianhui Institute of Advanced Control Systems School of Automation and Intelligence Beijing Jiaotong University Beijing Beijing China Institute of Robotics School of Mechanical Engineering and Automation Beihang University Beijing Beijing China

The limited steering capabilities of nonomnidirectional robots introduce significant complexity into complete coverage tasks, often leading to increased path overlap or incomplete coverage of certain areas. Although recent research has made progress in optimizing coverage path planning, redundant coverage or omissions are still prone to occur in the target area to be covered. To address these persistent challenges, we propose a novel spiral coverage method. This approach not only conforms to the kinematic constraints of nonomnidirectional robots but also enhances coverage efficiency by dividing the target area into center and boundary regions and devising tailored coverage strategies for each. This method effectively reduces path redundancy and improves overall area coverage. Furthermore, we introduce a comprehensive metric that combines total path length and area coverage ratio to evaluate coverage efficiency, overcoming the limitations and computational complexity associated with existing metrics. For scenarios where maximizing the area coverage ratio is critical, we have developed a high-coverage-rate turning strategy that ensures 100% coverage. Through simulation tests in six representative areas and actual experiments on airport runways, our method shows an improvement of 0.238%–14.538% in coverage efficiency compared with parallel coverage method and 60.548%–76.339% compared with deep reinforcement learning-based method. Additionally, implementing high-coverage-rate turning strategies improves the area coverage ratio by 2.021%–6.732%. In field experiments, our method reduces execution time by 1.61% compared with parallel coverage method. These results show that our method has a significant effect in improving coverage efficiency and achieving complete coverage goals. © 2025 Wiley Periodicals LLC.

关键词： Robot programming

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Intelligent interaction system based on multimodal conformal triboelectric nanogenerator patch for disabled sports and life

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Science China(Technological Sciences) 2025年第2期68卷 290-305页

作者： Yuzhang WEN Mengqi ZHANG Zhenning XIE Zida AN Bing LIU Fengxin SUN Tianming ZHAO Zuojun YU Fei WANG Yupeng MAO Physical Education Department Northeastern UniversityShenyang 110819China Faculty of Robot Science and Engineering Northeastern UniversityShenyang 110819China China Ice Sports College Beijing Sport UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Criminal Investigation Police University of China Shenyang 110035China

Intelligent interaction technology based on advanced sensing units has become a research *** this paper,an intelligent interaction system(IIS)based on a multimodal conformal triboelectric nanogenerator patch(MCTP)is reported for sports monitoring and life promotion of the *** IIS consists of a cloud-based smart sports recognition program and a smart life *** porous micro-reinforced structure of polyurethane(PU)Sponge&Ecoflex,and the surface modification of PTFE by coating it with MXene endows the MCTP with excellent electrical *** shows good angle and force sensitivities of 1.1 V/°and 1.61 V/N,respectively,and has an ultra-fast response time of 15 ms,meeting the needs of human motion *** MCTP can also capture the mechanical energy,producing a maximum output power of 139μ*** with the commercial inertial measuring unit(IMU),the MCTP shows better consistency in human joint angle sensing.A precise analysis of wheelchair curling sports monitoring points is achieved by combining the MCTP and the human anatomy *** cloud-based smart sports recognition program utilizes machine learning to achieve visual feedback on the three throwing technologies of draw,takeout and *** addition,the developed smart life application uses a highsensitivity MCTP-sensor array design to achieve a variety of smart home interactions through simple finger ***,the IIS integrates the three major functions of smart sports sensing,energy capture,and smart home control,providing important application prospects in the fields of smart sports and human-computer interaction for the disabled.

关键词： triboelectric nanogenerator machine learning smart sports smart home human-computer interaction

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Enhancing Robot Learning Through Cognitive Reasoning Trajectory Optimization Under Unknown Dynamics

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IEEE robotics and automation Letters 2025年第6期10卷 5401-5408页

作者： Dong, Qingwei Wu, Tingting Zeng, Peng Zang, Chuanzhi Wan, Guangxi Cui, Shijie 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 Beijing100049 China China Mobile Research Institute Beijing100053 China Shenyang University of Technology School of Artifcial Intelligence Shenyang110870 China

In the domain of robot learning, equipping robots with the capability to swiftly acquire operational skills poses a significant challenge. Currently, reinforcement learning techniques are adept at addressing dynamic, unstructured problems involving rich contact scenarios. However, the convergence rate of these algorithms is often slow due to the high dimensionality of the robot state-action mapping space and the extensive initial policy search space. Meanwhile, advancements in large language models (LLMs) have endowed these models with a degree of logical reasoning ability, enabling them to take goal-oriented actions proactively during the initial phase of a robotic task. These models can implicitly generate features of states and uncover underlying patterns in trajectory generation. Yet, in complex manipulative tasks involving rich contact scenarios, LLMs still fall short. Thus, integrating the robust interactive capabilities of reinforcement learning with the strong logical reasoning of LLMs, and enhancing policy search with LLMs, could potentially accelerate the speed of policy searches significantly. In this letter, we introduce a Cognitive Reasoning Trajectory Optimization method. This approach utilizes Low-level Cognitive Control Tuning to enable LLMs with robust logical reasoning to make effective single-step decisions in Markov Decision Process (MDP) tasks. By fitting dynamic models with high-quality cognitive reasoning data and optimizing control strategies, this method constrains the policy search space and enhances the efficiency of trajectory optimization. Experimental results on various manipulative tasks using the Sawyer robot in the Mujoco simulator validate the effectiveness of the proposed algorithm. © 2016 IEEE.

关键词： Markov processes

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60 GHz Compact Microstrip Antenna Design with Inset-Fed Technique for Millimetre-Wave Applications 1

60 GHz Compact Microstrip Antenna Design with Inset-Fed Tech...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Badrinathan, B. Anitha, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Automation and Robotics Engineering Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Millimeter-wave (mmWave) frequencies play a critical role in next-generation communication systems by supporting high-speed data transfer and improved bandwidth utilization. This study focuses on developing a compact 60 GHz microstrip patch antenna using an inset-fed technique with a gallium arsenide (GaAs) substrate. The methodology integrates precision micromachining and electromagnetic simulations conducted with Ansys HFSS 2021. Key results include a return loss of -50.18 dB, a gain of 4.8 dBi, and a VSWR of 1.585, demonstrating efficient signal transmission and excellent impedance matching. The antenna's small footprint of 1.8 mm × 1.2 mm ensures its suitability for telecommunications, radar systems, and other mmWave technologies applications. By effectively addressing bandwidth and signal attenuation challenges, the optimized design highlights significant advancements in performance and scalability. The study concludes with insights into the potential for cost-effective manufacturing, marking a step forward in mmWave antenna technology. © 2025 IEEE.

关键词： Gallium arsenide

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Bioinspired fabrication of graphene/PDMS composite materials for high-performance flexible pressure sensor

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Science China Materials 2025年第4期68卷 1184-1195页

作者： Dandan Xu Peilin Zhou Haibo Yu Jianhang Chen Ya Zhong Hongji Guo Xiuli Zhang Yueqing Xia Guangrui Xiang Lianqing Liu College of Mechanical and Electrical Engineering Henan Agricultural UniversityZhengzhou 450002China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110o16China

Flexible pressure sensors show significant potential for applications in the fields of intelligent wearable devices,electronic skins,and health ***,the fast response saturation and high viscoelasticity of the flexible sensing materials often result in reduced sensitivity and increased response hysteresis,limiting the practical application of these ***,achieving flexible pressure sensors with both high sensitivity and wide detection range still remains great *** this study,bioinspired by the forcesensitive sensing mechanism and physiological structure of human skin,we propose a low-cost flexible fabrication method for high-performance piezoresistive flexible pressure sensor based on graphene/polydimethylsiloxane(PDMS)composite *** results show that the sensor has an ultra-high sensitivity(321 kPa^(-1)),wide detection range(0.01-1000 kPa),fast response time(29 ms),and exhibits stability over 5000 *** addition,the successful detections and applications indicate the wide application prospect of the developed sensor in fields of health monitoring,human-machine interactions and intelligent robotic perception.

关键词： bioinspired flexible pressure sensor graphene ultrahigh sensitivity wide detection range

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Towards Autonomous Cardiac Ultrasound Scanning: Combining Physician Expertise and Machine Intelligence

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 782-792页

作者： Hao, Mingrui Zhang, Pengcheng Hou, Xilong Gu, Xiaolin Zhou, Xiao-Hu Hou, Zeng-Guang Chen, Chen Wang, Shuangyi Chinese Academy of Sciences National Key Laboratory for Multi-modal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100043 China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Hong Kong Hong Kong Lingshu Medical Company Suzhou215123 China

Echocardiography serves as a prevalent modality for both heart disease diagnosis and procedural guidance in medical applications. Nevertheless, the conventional echocardiography examination heavily relies on the manual dexterity of the sonographer, leading to the suboptimal repeatability. Despite the extensive exploration of robot-assisted ultrasound systems, achieving a heightened level of automation in examinations and enhancing the practicality of these robotic platforms for primary utilization remain formidable challenges within the field. In this study, we introduce an innovative automatic acquisition method for cardiac views using a novel ultrasound robot. The method is designed to autonomously traverse and scan target positions and angular ranges to search and identify the target cardiac views. First, the target positions and angular ranges were derived from a professional sonographer’s practice on 14 cases. Then, an automatic traversal scanning method is designed integrating visual guidance, human-machine collaboration, and path planning within the framework of a novel parallel mechanism-based ultrasound robot. Finally, we explore deep metric learning to search for target ultrasound images in the traversed ultrasound video. Experiments on the test set to evaluate the target ultrasound view searching algorithm achieved a mAP of 98.8% and a Rank-1 accuracy of 98.23%. Our method has been successfully validated by data from five subjects, achieving the acquisition of standard parasternal long-axis and short-axis cardiac views essential for diagnosis, demonstrating the effectiveness of the proposed method. © 2018 IEEE.

关键词： Motion planning

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Rope-net: deep convolutional neural network via robust principal component analysis

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Machine Learning 2025年第7期114卷 1-26页

作者： Liu, Baichen Han, Zhi Chen, Xi’ai Wang, Yanmei Tang, Yandong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

Low-rank decomposition methods can compress parameters and accelerate training of deep convolutional neural networks (DCNNs) by mining the low-rankness in weights. However, for an image classification task, the test accuracy of DCNNs compressed by low-rank decomposition usually decreases. Our experiments show that the error term of low-rank decomposition is not sparse and not suitable for reutilizing to maintain accuracy. To overcome this problem, in this paper, we propose an effective compression approach of DCNNs by robust principal component analysis (RPCA) on the weights of convolutional and fully connected layers. Low-rank term of RPCA keeps advantages of low-rank decomposition. Sparse term of RPCA can be retrained via sparse learning to improve test accuracy because of its sparsity. We name this kind of DCNN as Rope-Net (DCNN via Robust Principal Component Analysis). Our Rope-ResNet110 (ResNet110 via RPCA) experiment on CIFAR10 demonstrates that Rope-ResNet110 can achieve: a) 3.3 $$\times$$ parameter compression ratio and 0.33% accuracy improvement, b) 5 $$\times$$ parameter compression ratio without accuracy drop. More remarkably, Rope-VGG16 achieves 8.5 $$\times$$ parameter compression ratio and 5.48% accuracy improvement on randomly cutout CIFAR10 test set compared with VGG16, which validates strong robustness of Rope-Net.

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Application of artificial neural network for prediction of fracture energy of concrete

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Asian Journal of Civil Engineering 2025年第6期26卷 2629-2644页

作者： Pathak, Sudhanshu Mane, Sachin Pataskar, Smita Vemawala, Gaurang Shiyekar, Sandeep Sarnobat, Sandeep Department of Civil Engineering D Y Patil College of Engineering Akurdi Pune India Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Robotics and Automation D Y Patil College of Engineering Akurdi Pune India

The analysis of fracture parameters of concrete drawing the researchers attention and getting popular day by day. Every concrete structure undergoes crack formation, initiation and propagation phase, to understand the kind and severity of crack study the fracture mechanics is very much needed. Fracture energy (G_f) is one the main characteristic amongst numerous fracture parameters. The different parameters such as water to cement (w/c) ratio, compressive strength (fc), diameter of aggregates, testing age of specimens etc. play essential role in understanding the G_f. In present work, G_f of concrete is measured by replacing cement with nano TiO₂ (NT) at 1, 2, 3, and 4% of the concrete mix, as well as fly ash (FA) and ground granulated blast furnace slag (GGBS) at 10, 20, 30, and 40%. The G_f was investigated using the size effect technique (SEM), and the notched beams were subjected to a three-point bend test. According to the experimental results, the NT4FA40 mix had the maximum G_f, while mixtures including FA out performed than GGBS mixes. Furthermore, an attempt was made to anticipate G_f using the soft computing method in light of the current necessity. The G_f is predicted using an ANN. The literature database, which includes 193 fracture tests, was gathered from earlier research in addition to the data from the current experimental investigation. Furthermore, the formula proposed by Bažant and Becq-Giraudon was utilized to make predictions based on a number of characteristics, including compressive strength, maximum aggregate size, and w/c ratio. compressive strength, maximum aggregate size, and water to cement ratio are among the characteristics that are trained, verified, and tested for ANN. The ANN model developed using literature-based data, Bažant and Becq-Giraudon equation derived data and experimental data gives promising results with R values 0.999, 0.981, 0.984 respecti

关键词： Artificial neural network Fly ash Fracture energy Ground granulated blast furnace slag Nano TiO2

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