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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 768-781页

作者： Long Lei Yingbai Hu Zixing Jiang Juzheng Miao Xiao Luo Yu Zhang Qiong Wang Shujun Wang Zheng Li Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China Department of Surgery The Chinese University of Hong Kong Hong Kong China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong China Department of Surgery Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Sciences and Multiscale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

关键词： Ultrasonic imaging Lungs Probes Robots Robot kinematics Medical robotics Ribs Medical services Surface morphology Computed tomography

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Collaborative Trolley Transportation System with Autonomous ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Bingyi Xia Hao Luan Ziqi Zhao Xuheng Gao Peijia Xie Anxing Xiao Jiankun Wang Max Q.-H. Meng Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China School of Computing National University of Singapore Singapore Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering Department of Electrical and Computer Engineering Chinese University of Hong Kong in Hong Kong University of Alberta Canada

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video 1 1 Video demonstration: https://***/efnPERm0Rco.)

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Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy

arXiv

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arXiv 2023年

作者： Sun, Hui Luo, Hao Wang, Feifei Chen, Qingjiu Chen, Meng Wang, Xiaoduo Yu, Haibo Zhang, Guanglie Liu, Lianqing Wang, Jianping Wu, Dapeng Li, Wen Jung Department of Mechanical Engineering City University of Hong Kong Hong Kong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Electronics Engineering The University of Hong Kong Hong Kong Shenzhen518000 China Department of Computer Science City University of Hong Kong Hong Kong

Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields. Copyright © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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EFFICIENT UNIVERSAL SHUFFLE ATTACK FOR VISUAL OBJECT TRACKING

arXiv

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arXiv 2022年

作者： Liu, Siao Chen, Zhaoyu Li, Wei Zhu, Jiwei Wang, Jiafeng Zhang, Wenqiang Gan, Zhongxue Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai China The Department of Engineering Research Center for Intelligent Robotics Ji Hua Laboratory Foshan China

Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames. However, previous work only generates the video-specific perturbations, which restricts its application scenarios. In addition, existing attacks are difficult to implement in reality due to the real-time of tracking and the re-initialization mechanism. To address these issues, we propose an offline universal adversarial attack called Efficient Universal Shuffle Attack. It takes only one perturbation to cause the tracker malfunction on all videos. To improve the computational efficiency and attack performance, we propose a greedy gradient strategy and a triple loss to efficiently capture and attack model-specific feature representations through the gradients. Experimental results show that EUSA can significantly reduce the performance of state-of-the-art trackers on OTB2015 and VOT2018. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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SparseGTN: Human Trajectory Forecasting with Sparsely Represented Scene and Incomplete Trajectories

SparseGTN: Human Trajectory Forecasting with Sparsely Repres...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jianbang Liu Guangyang Li Xinyu Mao Fei Meng Jie Mei Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Professor Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Professor in the Department of Electrical and Computer Engineering University of Alberta in Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, great progress has been made in forecasting human motion in crowded scenes. However, current methods are far from practical applications due to the unbearable high computation costs, especially for encoding scene context. In addition, neglecting the partially detected trajectories makes the predicted outcome deviate from the real trajectory distribution. To handle the aforementioned concerns, we propose to represent the scene context and partially observed trajectories with sparse graphs. Customized for this special data structure, we design a hierarchical Graph Transformer Network model SparseGTN to predict multiple possible future trajectories of the target pedestrian by digesting the sparsely represented inputs. Our approach exhibits superiority over the state-of-the-art (SOTA) methods, utilizing a mere 3.42% of the number of floating point operations (FLOPs) and 0.53% of the number of model parameters. The code will be available online ⋆ .

关键词： Pedestrians Switches Predictive models Transformers Encoding Trajectory Mobile robots Forecasting Collision avoidance Intelligent robots

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Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

arXiv

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arXiv 2023年

作者： Xia, Bingyi Luan, Hao Zhao, Ziqi Gao, Xuheng Xie, Peijia Xiao, Anxing Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada School of Computing National University of Singapore Singapore

— Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments. (Video1) Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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A Systematic Evaluation of Different Indoor Localization Methods in Robotic Autonomous Luggage Trolley Collection at Airports

arXiv

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arXiv 2023年

作者： Sun, Zhirui Chen, Weinan Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Guangdong University of Technology Guangzhou China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position. In this article, we survey four popular and representative localization methods to achieve object localization in the luggage collection process, including radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. To test their performance, we construct a qualitative evaluation framework with Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Besides, we conduct a series of quantitative experiments regarding Localization Accuracy and Success Rate on a real-world robotic autonomous luggage trolley collection system. We further analyze the performance of different localization methods based on experiment results, revealing that the Keypoints method is most suitable for indoor environments to achieve the luggage trolley collection. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Face preference detection task based on EEG energy analysis in the source domain

Face preference detection task based on EEG energy analysis ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Pengchao Wang Junkongshuai Wang Lu Wang Wei Mu Gege Zhan Xueze Zhang Lan Niu Jianxiong Bin Zhiyan Dong Lihua Zhang Jie Jia Zhongxue Gan Xiaoyang Kang MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Yiwu City China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Facial stimulation can produce specific event-related potential (ERP) component N170 in the fusiform gyrus region. However, the role of the fusiform gyrus region in facial preference tasks is not clear at present, and the current research of facial preference analysis based on EEG signals is mostly carried out in the scalp domain. This paper explores whether the region of the fusiform gyrus is involved in processing face preference emotions in terms of the distribution of energy over the source domain, and finds that the pars orbitalis cortex is most energetically active in the face preference task and that there are significant differences between the left and right *** Relevance— The role of pars orbitalis in facial preference may help doctors determine whether the pars orbitalis cortex is lost in clinical practice.

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A Shared Pose Regression Network for Pose Estimation of Objects from RGB Images

A Shared Pose Regression Network for Pose Estimation of Obje...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Stefan Hein Bengtson Hampus Åström Thomas B. Moeslund Elin A. Topp Volker Krueger Department of Architecture Design and Media Technology Visual Analysis and Perception (VAP) Laboratory Pioneer Centre for AI Denmark Aalborg University Aalborg Denmark Department of Computer Science Lund University Robotics and Semantic Systems Lund Sweden

In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance.

关键词： Training Pose estimation Memory management Pipelines Feature extraction Rendering (computer graphics) Real-time systems

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Provably Convergent Federated Trilevel Learning

arXiv

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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