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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 IEEE.

关键词： Motion planning

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A New Approach to the Position of Acupoints on the Back of Humans

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Recent Patents on Mechanical Engineering 2023年第2期16卷 150-162页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Song, Fengfeng Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Hangzhou Qiandaohu Ruichun Robot Research Institute Co. Ltd Zhejiang Hangzhou310018 China Zhejiang Shusi Robot Co. Ltd Zhejiang Huzhou313000 China

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effec-tiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high. © 2023 Bentham Science Publishers.

关键词： Computer vision

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PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection 24

PS-TTL: Prototype-based Soft-labels and Test-Time Learning f...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Gao, Yingjie Zhang, Yanan Huang, Ziyue Liu, Nanqing Huang, Di SKLCCSE Beihang University Beijing China SIST Southwest Jiaotong University Chengdu China Zhejiang Industrial Big Data and Robot Intelligent System Key Lab. Hangzhou China

ISBN: (纸本)9798400706868

In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated data. The fine-tuning based paradigm is currently dominating this field, where detectors are initially pre-trained on base classes with sufficient samples and then fine-tuned on novel ones with few samples, but the scarcity of labeled samples of novel classes greatly interferes precisely fitting their data distribution, thus hampering the performance. To address this issue, we propose a new framework for FSOD, namely Prototype-based Soft-labels and Test-Time Learning (PS-TTL). Specifically, we design a Test-Time Learning (TTL) module that employs a mean-teacher network for self-training to discover novel instances from test data, allowing detectors to learn better representations and classifiers for novel classes. Furthermore, we notice that even though relatively low-confidence pseudo-labels exhibit classification confusion, they still tend to recall foreground. We thus develop a Prototype-based Soft-labels (PS) strategy through assessing similarities between low-confidence pseudo-labels and category prototypes as soft-labels to unleash their potential, which substantially mitigates the constraints posed by few-shot samples. Extensive experiments on both the VOC and COCO benchmarks show that PS-TTL achieves the state-of-the-art, highlighting its effectiveness. The code and model are available at https://***/gaoyingjay/PS-TTL. © 2024 ACM.

关键词： Object recognition

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A Digital Twin-based Framework of Motion Control and State Monitoring for Pneumatic Muscle

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Recent Patents on Mechanical Engineering 2023年第3期16卷 203-213页

作者： Xie, Shenglong Liu, Wenyuan Duan, Huiru Chen, Dijian Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Ji-liang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma'anshan243032 China

Introduction: The current digital twin systems usually have the drawback of high cost and complex technology, and it is necessary to develop a simple solution to reduce the cost and cycle for the development of digital twin systems, especially for small projects or systems with simple structures. Objective: A low-cost patent technology of digital twin system was proposed by taking the motion control and state monitoring system (MCSMS) of pneumatic muscle as an example. Methods: The MCSMS is developed based on the browser/server architecture. The software of 3ds Max and SolidWorks are used to make the virtual model, *** and JavaScript are applied to build the browser side. data of the physical world is collected and processed on the server side firstly, and then is sent to the browser side through HTTP communication protocol to realize data interchange between the browser and server. Results: In the roaming experiment and the experiment of motion control and state monitoring of pneumatic muscle, the MCSMS can work smoothly without obvious delay and has good real-time performance, which can realize the 3D visual monitoring of the pneumatic muscle very well. Conclusion: The experimental results indicate that the proposed method possesses the ability of good feasibility and effectiveness. © 2023 Bentham Science Publishers.

关键词： Muscle

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Hu, Chuanqi Wang, Jin Chen, Yikai Chen, Yonghang Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China National Elite Institute of Engineering Chongqing University Chongqing401135 China School of Mechanical Engineering Zhejiang University Hangzhou310058 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments. © 2025 IEEE.

关键词： Flight paths

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-systems and robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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Design and Force Control Analysis of a Novel Waist Massage Instrument

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Recent Patents on Mechanical Engineering 2023年第4期16卷 296-307页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China School of Mechanical Engineering Anhui University of Technology Anhui Ma’anshan243000 China

Background: There is a shortage of rehabilitation doctors and treatment equipment for patients with lumbar spondylosis. Therefore, a waist massage instrument (WMI) is designed to help patients alle-viate lumbar pain. Objective: The objective of this study is to design a new WMI with the advantages of simple structure, lightweight, and function of effective force control, and to verify its function of palmar push, palmar press, and palmar vibration based on the on-off control, PID control, and fuzzy-PID control. Methods: Firstly, the movement characteristics of the Chinese massage manipulation are analyzed to clarify the design principle of the massage instrument. Then, a new WMI composed of massage head, vibration exciter, and actuator is designed to simulate the three massage manipulations. Next, the electric control system and control methods of on-off, PID, and fuzzy-PID controllers are introduced to realize the force control of WMI. Finally, the force control experiments of WMI are carried out to verify the function of the palmar press, palmar push, and palmar vibration based on the on-off control, PID control, and fuzzy-PID control, respectively. Results: The experimental results indicate that the proposed WMI can implement the massage manipulations of palmar push, palmar press, and palmar vibration well, and has good force control ability after using fuzzy-PID control. Conclusion: The newly designed WMI is effective in providing palmar push, palmar press, and palmar vibration massage manipulations for patients with lumbar spondylosis, and the fuzzy-PID controller im-proves the force control ability of the instrument. The WMI has the potential to be used as an effective treatment for lumbar pain. © 2023 Bentham Science Publishers.

关键词： Three term control systems

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Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

arXiv

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arXiv 2025年

作者： Wu, Ruixin Li, Zihan Wang, Jin Xu, Xiangyu Yu, Huan Zheng, Zhi Huang, Kaixiang Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird’s eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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Bridging VLM and KMP: Enabling Fine-grained robotic manipulation via Semantic keypoints Representation

arXiv

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arXiv 2025年

作者： Zhu, Junjie Liu, Huayu Wang, Jin Wen, Bangrong Huang, Kaixiang Li, Xiaofei Zhang, Haiyun Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China School of Robotics Ningbo University of Technology Ningbo315211 China Robotics Research Center of Yuyao City Ningbo315400 China

From early Movement Primitive (MP) techniques to modern Vision-Language Models (VLMs), autonomous manipulation has remained a pivotal topic in robotics. As two extremes, VLM-based methods emphasize zero-shot and adaptive manipulation but struggle with fine-grained planning. In contrast, MP-based approaches excel in precise trajectory generalization but lack decision-making ability. To leverage the strengths of the two frameworks, we propose VL-MP, which integrates VLM with Kernelized Movement Primitives (KMP) via a low-distortion decision information transfer bridge, enabling fine-grained robotic manipulation under ambiguous situations. One key of VL-MP is the accurate representation of task decision parameters through semantic keypoints constraints, leading to more precise task parameter generation. Additionally, we introduce a local trajectory feature-enhanced KMP to support VL-MP, thereby achieving shape preservation for complex trajectories. Extensive experiments conducted in complex real-world environments validate the effectiveness of VL-MP for adaptive and fine-grained manipulation. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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