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Enhancing Ultrasound Scanning Skills in a Leader-Follower Robotic System through Expert Hand Impedance Regulation

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Niu, Baoshan Yang, Dapeng Zhang, Le Ji, Yiming Jiang, Li Liu, Hong Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150080 China

Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader-follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader-follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57.9% and 73.6%, the contact force variance decreased by 55.2% and 42.5%, and the US image confidence increased by 22.0% and 23.8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32.0% and 25.3%, respectively, and increases confidence by 7.3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader-follower US scanning and rapidly improve surgical skills. © 2013 IEEE.

关键词： Robotic surgery

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Improved Detection of Transmission Tower Equipment Using YOLOv7 with Saliency Data Augmentation 5

Improved Detection of Transmission Tower Equipment Using YOL...

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5th International Conference on Clean Energy and Electric Power Engineering, ICCEPE 2024

作者： Xu, Yingying Song, Chunhe Sun, Yong Yu, Shimao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350390315

Transmission towers are critical infrastructure for power transmission, and the reliable operation of their equipment is essential to ensure electricity supply. However, the detection of transmission tower equipment faces challenges such as target overlap, large variations in size, small targets, and class imbalance due to the characteristics of the equipment. To tackle the identified issues, this study introduces a method based on YOLOv7 for the detection of transmission tower components, utilizing saliency-based data augmentation. Initially, an approach focusing on importance is employed to augment the sample size in categories with limited numbers, thereby enhancing the effectiveness of detection. Secondly, an adaptive adjustment module is introduced into the multi-head self-attention mechanism to dynamically adjust attention allocation based on the actual size of the targets and integrate contextual information, thus better handling targets with large size variations and overlaps. Finally, incorporating dynamic convolution within the backbone network significantly improves the ability to extract features from small objects. The outcomes of the experiments confirm the efficiency of the suggested approach in the smart surveillance of devices within intricate transmission settings, offering a reliable solution for maintaining power supply dependability. © 2024 IEEE.

关键词： Critical infrastructures

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Industrial Wireless Control Networks:From WIA to the Future

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Engineering 2022年第1期8卷 18-24页

作者： Haibin Yu Peng Zeng Chi Xu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China

*** Industrial automation is undergoing a significant innovation as information,communication,and operation technologies are deeply integrating with each *** this trend,industrial wireless control networks(IWCNs)are becoming increasingly attractive to industrial automation since they can help speed up production efficiency,reduce cost,enhance safety,and finally realize intelligent manufacturing[1].

关键词： attractive integrating becoming

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Terrain Classification Based on Wheel-terrain Interaction Measurements using Aside-Wheel Camera

Terrain Classification Based on Wheel-terrain Interaction Me...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xue, Feng Hu, Longteng Yao, Chen Wei, Jin Li, Yunzhou Wang, Peichen Zhu, Zheng Jia, Zhenzhong Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China Harbin150090 China

ISBN: (纸本)9798350325706

The scientific applications over a decade have demonstrated the robust practicality of wheeled rovers, which are capable of undertaking exploration tasks in various unstructured terrains. However, sinkage and slippage are extremely common on sandy terrains. Fortunately, these risks can be mitigated in a timely manner through the perception of terrain types. Thus, terrain classification technology has become a key solution to addressing these issues. In this paper, we propose a new terrain classification pipeline for a rover over sandy terrains. It exploits the aside-wheel sensor suite to measure sinkage, contact angle, slip ratio, and normal force. Due to the strong correlation between contact characteristics and terrain types form our experiments, we can infer terrain types based on wheel-terrain interaction parameters. We train various machine learning models and obtained classification results, which is evaluated using the record dataset form a singlewheel testbed. Through Experimental comparison, most models achieve high accuracy (over 90%), we analyze the reasons behind performance discrepancies among different models and recommend LGBM as the preferred approach. In the end, we transfer the models to use data collected from rover field tests and achieve a higher level of accuracy, which proves the effectiveness of our approach. © 2023 IEEE.

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Modeling of the Constant-Current Stimuli Response of a Bio-Robot for Long-Term Motion Control

Modeling of the Constant-Current Stimuli Response of a Bio-R...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhou, Jie Fang, Yuan Chen, Yang Li, Yao Li, Bing Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

ISBN: (纸本)9781665481090

Bio-robots continue to receive attention because of their small size and low power consumption. Unfortunately, it currently faces two major obstacles before practical application. The first is the habituation issue, insects are difficult to be effectively controlled for a long time by electrical stimuli signals. The other one is the lack of model for motion control. In this research, we established a stimuli-response model for cockroach bio-robot based on long time current. A more convenient optic lobe implantation method has been proposed, which has lower attenuation and better stimuli effect. Constant electrical stimuli also significantly increased the number of effective controls for crawling. A cockroach bio-robot stimuli distance response model was established and validated with experiment data. Five parameters were including in this model, namely sex, length, weight, current amplitude and equivalent stiumated-number. This model can predict the movement distance of biological robots well. © 2024 IEEE.

关键词： Microrobots

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Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic systems, Measurement and Control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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Optimized Design and Experimental Study of Shank Structure of Biped Robot

Optimized Design and Experimental Study of Shank Structure o...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Qin, Meijuan Kong, Lingyu Nie, Daming Jiang, Hongjian Zhang, Yu Fu, Yili Intelligent Robot Research Center Zhejiang Laboratory Hangzhou311100 China The National Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9798350325706

Lightweight design can improve the energy efficiency of biped robots, which is significant in the field of biped robot. Topology optimization has been widely used in the field of robot. However, the existing optimization is limited to the structural space based on traditional empirical design, and the potential of topology optimization is not fully utilized. Moreover, the finite element model is too simplified, which leads to a large optimization deviation. In this paper, a method of structural innovation design for the shank structure of biped robot is proposed. In the initial stage of conceptual design, the design space of optimization object is effectively maximized. Then, the load transmission characteristics of bearings are simulated by link10 element, and the topology optimization method of multiple load cases is carried out to obtain the optimized shank structure, which reduces the weight by 14.09 % compared with the existing shank. Finally, finite element extreme strength check and experiment test are both used to verify the feasibility and availability of the structure. © 2023 IEEE.

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution 38

MSPE: Multi-Scale Patch Embedding Prompts Vision Transformer...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Liu, Wenzhuo Zhu, Fei Ma, Shijie Liu, Cheng-Lin School of Artificial Intelligence UCAS China State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA China Centre for Artificial Intelligence and Robotics HKISI-CAS China

Although Vision Transformers (ViTs) have recently advanced computer vision tasks significantly, an important real-world problem was overlooked: adapting to variable input resolutions. Typically, images are resized to a fixed resolution, such as 224x224, for efficiency during training and inference. However, uniform input size conflicts with real-world scenarios where images naturally vary in resolution. Modifying the preset resolution of a model may severely degrade the performance. In this work, we propose to enhance the model adaptability to resolution variation by optimizing the patch embedding. The proposed method, called Multi-Scale Patch Embedding (MSPE), substitutes the standard patch embedding with multiple variable-sized patch kernels and selects the best parameters for different resolutions, eliminating the need to resize the original image. Our method does not require high-cost training or modifications to other parts, making it easy to apply to most ViT models. Experiments in image classification, segmentation, and detection tasks demonstrate the effectiveness of MSPE, yielding superior performance on low-resolution inputs and performing comparably on high-resolution inputs with existing methods. Code is available at https://***/SmallPigPeppa/MSPE. © 2024 Neural information processing systems foundation. All rights reserved.

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Adaptive Planar Foot with Compliant Ankle Joint and Multi-modal Sensing for Quadruped Robots

Adaptive Planar Foot with Compliant Ankle Joint and Multi-mo...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Shi, Guowei Yao, Chen Wang, Wenhui Zhu, Zheng Jia, Zhenzhong Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems China Shenzhen518055 China

ISBN: (纸本)9781665481090

An adaptive sensorized robot foot that can adapt to uneven and rough terrains and have contact state sensing capability is vital for legged locomotion in exploration missions. Inspired by quadruped animals, we present an adaptive planar foot with large contact area to reduce sinkage, two degrees of freedom (2-DOF) compliant ankle joint for better terrain adaptation, in-foot multi-modal sensing capability. We propose customized ankle design with encoders instead of IMU for ankle joint angle measurement, in order to mitigate the noise injected by large impact forces. The foot also provides an accurate estimated force according to foot-shank relative orientation and the 6-axis force/torque sensor data. We develop a control algorithm for a quadruped robot equipped with our proposed robot feet and test its performance through simulation. Real-world experiments including foot orientation estimation, swing sensitivity, and force perception verify that our robot foot is suitable for dynamic quadruped locomotion. © 2022 IEEE.

关键词： Degrees of freedom (mechanics)

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