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2022 International Conference on Neural Networks, Information, and Communication engineering, NNICE 2022

作者： Xia, Shanhong Li, Minghang Heilongjiang Water Traffic Rescue Center Heilongjiang Province China The Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781510655171

The classical wavelet transform in image processing is easy to lose the details, and the edge of the image is often easily disturbed by the noise which leads to poor extraction effect. In order to overcome the shortcomings of the classical image edge detection methods, an edge detection method based on multi-scale hybrid wavelet transform is proposed. A series of operations such as B-spline filter, Gaussian filter, multi-scale detection and adaptive threshold removal with pseudo-edge contour are carried out to remove the complex noise of image and to obtain the precise edge. Due to the importance of underwater image edge extraction in underwater target recognition, this method is applied to the edge detection of underwater images. The experimental results show that the method based on multi-scale hybrid wavelet transform can suppress the image noise and extract the accurate edge contour of the underwater image, which proves the effectiveness. © 2022 SPIE.

关键词： Wavelet transforms

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Preparation and 3D printing of high-thermal-conductivity continuous mesophase-pitch-based carbon fiber/epoxy composites

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2023年第2期24卷 162-172页

作者： Haiguang ZHANG Kunlong ZHAO Qingxi HU Jinhe WANG Rapid Manufacturing Engineering Center School of Mechatronical Engineering and AutomationShanghai UniversityShanghai 200444China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai UniversityShanghai 200072China National Demonstration Center for Experimental Engineering Training Education Shanghai UniversityShanghai 200444China Nano-Science&Technology Research Center School of ScienceShanghai UniversityShanghai 200444China

To meet the requirements of spacecraft for the thermal conductivity of resins and solve the problem of low thermal conduction efficiency when 3D printing complex parts,we propose a new type of continuous mesophase-pitch-based carbon fiber/thermoplastic polyurethane/epoxy(CMPCF/TPU/epoxy)composite filament and its preparation process in this *** composite filament is based on the high thermal conductivity of CMPCF,the high elasticity of TPU,and the high-temperature resistance of *** tensile strength and thermal conductivity of the CMPCF/TPU/epoxy composite filament were *** CMPCF/TPU/epoxy composites are formed by 3D printing technology,and the composite filament is laid according to the direction of heat conduction so that the printed part can meet the needs of directional heat *** experimental results show that the thermal conductivity of the printed sample is 40.549 W/(m·K),which is 160 times that of pure epoxy resin(0.254 W/(m·K)).It is also approximately 13 times better than that of polyacrylonitrile carbon fiber/epoxy(PAN-CF/epoxy)*** study breaks through the technical bottleneck of poor printability of *** provides a new method for achieving directional thermal conductivity printing,which is important for the development of complex high-performance thermal conductivity products.

关键词： Thermal conductivity 3D printing Continuous mesophase-pitch-based carbon fiber(CMPCF) Thermoplastic polyurethane(TPU) Epoxy composite filament

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A Fully Test-Time Adaptation Method for Motor Imagery

SSRN

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SSRN 2024年

作者： Peng, Yiheng Luo, Jingjing Wang, Hongbo Chen, Li Guo, Shijie Guo, Yuzhu Li, Yang The Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Engineering Research Center of AI and Robotics Ministry of Education Shanghai200043 China Department of Automation Sciences and Electrical Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering China

Transfer learning offers a promising solution to the annotation and calibration challenges associated with motor imagery (MI) electroencephalogram (EEG) decoding during neural-feedback rehabilitation. In this study, we address the challenge of online unsupervised cross-subject adaptation in MI-EEG classification, where target samples arrive sequentially, and we should adapt the model online with limited target data. We focus on the testing stage and propose a MI Fully Test-Time Adaptation (MI-FTTA) method that essentially enables online training using limited and unlabeled target data. MI-FTTA effectively alleviates the influence of noisy pseudo labels with teacher-student mutual learning and time-constrained sample selection. Through recalculating normalization statistics with limited accessible source and target information and prototype-based cross-domain contrastive learning, MI-FTTA adaptively enhances the representational capability of models. It thoroughly harnesses the limited online target samples to enhance adaptation performance. Experimental evaluations are conducted using three classification datasets , including two-class (BCI-IV2b), three-class (High-Gamma), and four-class (BCI-IV2a) scenarios. MI-FTTA demonstrates its superiority by surpassing direct source model deployment with an average performance gain of 4.11\% across different scenarios. Notably, our proposed approach also achieves a significant improvement over 12 different online test-time adaptation algorithms at three different backbones, thus affirming its *** work represents early endeavor in source-free fully test-time adaptation for cross-subject MI-EEG decoding. © 2024, The Authors. All rights reserved.

关键词： Transfer learning

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Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation

arXiv

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arXiv 2025年

作者： Fan, Shichao Yang, Quantao Liu, Yajie Wu, Kun Che, Zhengping Liu, Qingjie Wan, Min School of Mechanical Engineering and Automation BeiHang University China School of Computer Science and Engineering BeiHang University China Beijing Innovation Center of Humanoid Robotics China KTH Royal Institute of Technology Sweden

Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current imitation learning methods struggle in out-of-distribution scenarios, especially for long-horizon tasks. A key challenge is how to mitigate compounding errors in imitation learning, which lead to cascading failures over extended trajectories. To address these challenges, we propose the Diffusion Trajectory-guided Policy (DTP) framework, which generates 2D trajectories through a diffusion model to guide policy learning for long-horizon tasks. By leveraging task-relevant trajectories, DTP provides trajectory-level guidance to reduce error accumulation. Our two-stage approach first trains a generative vision-language model to create diffusion-based trajectories, then refines the imitation policy using them. Experiments on the CALVIN benchmark show that DTP outperforms state-of-the-art baselines by 25% in success rate, starting from scratch without external pretraining. Moreover, DTP significantly improves real-world robot performance. All source code and experiment videos will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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无人飞行器在不确定扰动下的动态目标跟踪

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engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching

arXiv

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arXiv 2023年

作者： Yang, Lu Kong, Zhenglun Li, Ting Bai, Xinyi Lin, Zhiye Cheng, Hong Center for Robotics University of Electronic Science and Technology of China Chengdu China Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Traditional image stitching focuses on a single panorama frame without considering the spatial-temporal consistency in videos. The straightforward image stitching approach will cause temporal flcking and color inconstancy when it is applied to the video stitching task. Besides, inaccurate camera parameters will cause artifacts in the image warping. In this paper, we propose a real-time system to stitch multiple video sequences into a panoramic video, which is based on GPU accelerated color correction and frame warping without accurate camera parameters. We extend the traditional 2D-Matrix (2D-M) color correction approach and a present spatio-temporal 3D-Matrix (3D-M) color correction method for the overlap local regions with online color balancing using a piecewise function on global frames. Furthermore, we use pairwise homography matrices given by coarse camera calibration for global warping followed by accurate local warping based on the optical flow. Experimental results show that our system can generate high-quality panorama videos in real time. Copyright © 2023, The Authors. All rights reserved.

关键词： Real time systems

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TrunkFlex: Enhancing Engagement in Post-Stroke Trunk Rehabilitation through a Follow-and-Stimulate Cable-Driven Robotic System

TrunkFlex: Enhancing Engagement in Post-Stroke Trunk Rehabil...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Lufeng Chen Yingming Han Chenyang Liu Bin Li Jiacheng Huang Jing Qiu Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Center for Robotics School of Mechanical and Electric Engineering University of Electronic Science and Technology of China Chengdu China Yingcai Honors College of UESTC University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Trunk rehabilitation plays a pivotal role in enhancing locomotive capacities and balance for patients with hemiplegia. In this paper, we present TrunkFlex, a cable-driven robotic system designed specifically to promote self-engagement in post-stroke trunk rehabilitation. Unlike conventional rehabilitation robots, which often interact with patients in a rigid manner, TrunkFlex incorporates a cable-driven mechanism powered by eight servo motors, allowing for versatile rehabilitation exercises. Our introduced follow-and-stimulate module, along with posture correction and speed limit modules, is integrated into the control scheme and operates in parallel to induce patient-initiated movement, thereby encouraging self-engagement and promoting a more flexible and seamless rehabilitation process. Muscle activation experiments demonstrate a significant enhancement in active engagement, as evidenced by a marked increase in muscle activity intensity compared to passive rehabilitation methods.

关键词： Mechatronics Process control Muscles Clinical trials Stroke (medical condition) Assistive robots Safety Servomotors Robots

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UAV Forest Fire Detection based on RepVGG-YOLOv5

UAV Forest Fire Detection based on RepVGG-YOLOv5

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Song, Kang Zhang, Yueyuan Lu, Bo Chi, Wenzheng Sun, Lining Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Ningbo Institute of Materi-als Technology & Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9781665481090

The safety of forest resources is of great im-portance to natural and public safety, and the efficient and accurate detection of forest fires is an issue of close concern. Considering the limitations of traditional forest fire detection methods, such as the limited range of view from watchtowers and insufficient resolution of satellite images, a deep learning-based Unmanned Aerial Vehicle (UAV) fire detection system has been proposed. With its high mobility, images of forest fires can be captured by camera-equipped UAV and transmitted to the ground station in real time. The ground station uses a deep learning object detection algorithm to achieve the recognition of fire. In this paper, we choose the state-of-the-art object detection algorithm YOLOv5 for the UAV forest fire detection scenario, and to further improve the detection performance of YOLOv5, we propose RepVGG-YOLOv5 by drawing on the RepVGG network, a training-time multi-branch topology with an inference-time plain architecture. Then we test the models on a self-built dataset, and the experimental results show that the improved RepVGG-YOLOv5 model outperforms the YOLOv5 model in terms of detection performance and detection speed, which proves the feasibility of this paper's model in the UAV forest fire detection systems and the effectiveness of the model improvement. Furthermore, the great potential of this paper's object detection model for real-time applications of accurate forest fire detection has also been demonstrated. © 2022 IEEE.

关键词： Object detection

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A Prospective Study of Haptic Feedback Method on a Lower-Extremity Exoskeleton 7th

A Prospective Study of Haptic Feedback Method on a Lower-Ext...

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7th International Conference on Human Aspects of IT for the Aged Population, ITAP 2021, held as part of the 23rd International Conference, HCI International 2021

作者： Wang, Yilin Qiu, Jing Cheng, Hong Wang, Lu Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Center for Robotics School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783030781071

Medical lower-limb exoskeleton (LLE) has been developed for rehabilitation and walking assistance. However, it lacked feedback on current status to the wearer. The patients with perception lost in lower-extremity (for example, elderly people and paraplegia patients) cannot perceive their lower extremities. Therefore, haptic feedback might be necessary for them if they wear a LLE, which might inform them of current gait state. A prospective study was conducted in this paper with a purpose of designing a vibration device for a lower-extremity exoskeleton system to feed back the current walking state of the exoskeleton to wearers. Firstly, the basic tests were conducted to collect the accuracy and reaction time of subjects based on two vibration modes (strength change and position change vibration mode). Then, the subjects wore an exoskeleton with the two vibration devices to complete walking tasks and the task completion time was recorded. The accuracy of the two vibration devices can both reach about 90%, and there was no significant difference in the reaction time. It indicated that the feedback of the two devices can be well perceived by most subjects. The results of the subjective questionnaires showed that subjects prefer the strength change mode. Although there was no significant of completion time in no-feedback and vibration-feedback modes, the questionnaires showed that exoskeleton with haptic feedback can inform the wearers current state of exoskeleton and made the subjects feel safer. In the future design, LLE with vibration devices can be considered for rehabilitation and walking assistance for the elder people and the patients with motor dysfunction in lower limbs. © 2021, Springer Nature Switzerland AG.

关键词： Surveys

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UAV Wheat Rust Detection based on FasterNet-YOLOv8

UAV Wheat Rust Detection based on FasterNet-YOLOv8

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IEEE International Conference on robotics and Biomimetics

作者： Huaiyu Liu Yueyuan Zhang Shuguang Liu Mengyang Zhao Lining Sun Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou China Robotics and Microsystems Center School of Mechanical and Electrical Engineering Soochow University Suzhou China

This paper introduces a UAV-based wheat rust detection system employing deep learning techniques. To address the limitations of traditional wheat rust disease detection methods, such as time and labor-intensive processes, lack of real-time monitoring, and excessive pesticide usage, In this paper, the YOLOv8 algorithm, representing the forefront of object detection methods is chosen for UAV wheat rust detection. We train the model on a prepared dataset. Subsequently, we evaluated 5 models, including YOLOv5, and YOLOv8, at two different scales (n, s) based on average precision (mAP@.5), precision, recall, and FPS. The experimental outcomes establish the superiority of the enhanced FasterNet-YOLOv8 model over YOLOv8 and YOLOv5 across all metrics, encompassing detection accuracy and processing speed. These findings affirm the viability of our suggested model within the UAV wheat rust monitoring system and underscore the efficacy of the model enhancements.

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