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TrinityMag:A novel magnetically actuated miniature robot manipulating system with a human-scale workspace

Science China(Technological Sciences) 2024年第11期67卷 3546-3558页

作者： LI Dong CHEN BinHan HUANG ChenYang XU Sheng WU XinYu XU TianTian Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Key Laboratory of Biomedical Imaging Science and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

The development of the magnetic manipulating system is essential for applications of magnetically actuated miniature robots in biomedical practice,such as targeted therapy and precise ***,the workspaces of existing magnetic manipulating systems for miniature robots are mostly insufficient to manipulate miniature robots inside human *** present study proposes an innovative electromagnets-based manipulating system,TrinityMag,which can produce dynamic three-dimensional(3D)magnetic fields in a human-scale spherical workspace with a 2.6 m *** magnetic field of a single electromagnet is simulated,and a new calibration technic is designed based on deep learning ***,the arrangement of three electromagnets is optimized to produce maximal 3D arbitrary magnetic fields with limited ***,a target-tracking algorithm is developed so that the TrinityMag can track the miniature robot in real ***,the TrinityMag is validated in experiments to manipulate a soft millirobot to move in human-scale tortuous tracks with two types of *** maximum speed of the soft millirobot reaches 11.05 body length/*** work contributes to a significant increment in the workspace of the electromagnets-based manipulating system for miniature *** further expect that the TrinityMag could push the applications of miniature robots from laboratory to clinical practice.

关键词： magnetic manipulating system human-scale workspace magnetically actuated miniature robots

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Contact Force Optimization to Enhance Fault-tolerant Motion Stability of a Hexapod Robot

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Journal of Bionic Engineering 2024年第5期21卷 2199-2214页

作者： Bo You Shangdong Shi Chen Chen Jiayu Li Nan Li Liang Ding The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education and the Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin University of Science and TechnologyHarbin150080China The State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150001China

This paper presents a novel method for optimizing the contact force of a hexapod robot to enhance its dynamic motion stability when one of its legs *** proposed approach aims to improve the Force Angle Stability Margin(FASM)and adapt the foot trajectory through contact force optimization to achieve safe and stable motion on various *** foot force optimization approach is designed to optimize the FASM,a factor rarely considered in existing contact force optimization *** formulating the problem of enhancing the motion stability of the hexapod robot as a Quadratic Programming(QP)optimization problem,this approach can effectively address this *** of a hexapod robot using a fault-tolerant gait,along with real field experiments,were conducted to validate the effectiveness and feasibility of the contact force optimization *** results demonstrate that this approach can be used to design a motion controller for a hexapod robot with a significantly improved motion *** summary,the proposed contact force optimization method offers a promising solution for enhancing the motion stability of hexapod robots with single leg failures and has the potential to significantly advance the development of fault-tolerant hexapod robots for various applications.

关键词： Hexapod robot Fault-tolerant Stability margin Contact force optimization

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Multi-modal Bionic Motion Analysis of A Cpg-controlled Pneumatic Soft Robot

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Journal of Bionic Engineering 2024年第5期21卷 2247-2257页

作者： Yu Zhang Peiyu Huang Dongjie Li Jiangyu Zhou Yu Li Bo You Yanhe Zhu Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin University of Science and TechnologyHarbin150080China State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin150001China

This paper designs a soft robot with a multi-chamber,multi-airbag mimicking soft biological structure,where the airbags of the same chamber are interconnected with each *** upper and lower chambers are separated by an intermediate layer(thin plate),which is extended and widened to achieve robot movement and *** applying pressure to the different chambers of the soft robot,it is possible to produce a variety of bionic movements of the inchworm and *** to the strong nonlinearity and infinite number of degrees of freedom properties of the material,it is impossible to obtain the analytical solution of the bending morphology and pressure of the soft robot ***,a method to establish a mathematical model of soft robot deformation based on the classical stacked plate theory is proposed,and a chain composite model of soft robot bending motion is established based on the large-deflection modeling *** paper proposes a method to generate a multi-mode soft robot motion control based on the Central Pattern Generator(CPG)using a single controller,which achieves the switching of sine wave-like patterns,half-wave-like patterns,and dragging patterns by adjust-ing frequency,amplitude and period of ***,a pneumatic control platform is built for the validation of the theoretical model and different experimental models of the motion of the *** comparation of the different motion modes of the soft robot under similar non-load and load conditions.

关键词： Soft robot Bionic multi-modal movement Laminated plate theory CPG control

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Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism

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Science China(Technological Sciences) 2025年第3期68卷 217-227页

作者： Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Controland Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative ControlGuangdong University of TechnologyGuangzhou510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510006China College of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing400715China

This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN *** method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity ***,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control *** notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control ***,the DMETM is established to reduce the actuation frequency of the *** the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering *** a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed ***,simulation results are presented to validate the effectiveness of the proposed algorithm.

关键词： consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems(MASs)

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FWAF-VID: A Flapping-Wing Aggressive Flight Benchmark Dataset for Visual-Inertial Localization

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IEEE robotics and Automation Letters 2025年第6期10卷 6328-6335页

作者： Jiang, Jizhou Pan, Erzhen Xu, Wenfu Sun, Wei Ye, Jingyang Harbin Institute of Technology School of Robotics and Advanced Manufacture Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China Guangdong Biomimetic Intelligent Unmanned System Engineering Technology Research Center Shenzhen518052 China

Accurate state estimation of micro aerial vehicles (MAVs) in high-speed and dynamic environments poses a significant challenge for visual-inertial odometry (VIO) algorithms. Flapping-wing aerial vehicles (FWAVs), as an emerging flight platform, have attracted significant attention for stealth capabilities and efficient flight characteristics. Despite the availability of numerous MAV visual-inertial datasets for six-degree-of-freedom (6-DoF) state estimation, these datasets are not applicable for FWAVs with pronounced vibrations and agile maneuverability. To address this gap, we propose a large-scale flapping-wing aggressive flight visual-inertial dataset FWAF-VID. It contains diverse integrations of synchronized onboard cameras and IMU sensors. A total of 28 sequences include static flapping, calibration, and real-world flights collected in outdoor and indoor environments. The 17 flight sequences feature variations in illuminations, speeds, trajectories, camera perspectives, and flight modalities. We provide precisely aligned ground truth RTK and UWB measurements for algorithm evaluation. Furthermore, a comprehensive benchmark comparison is conducted, qualitatively and quantitatively evaluating the positioning accuracy of state-of-the-art VIO algorithms. The experimental results indicate that despite the degradation in localization accuracy in high-dynamic environments, FWAF-VID can effectively support the further development of VIO algorithms for FWAVs. To the best of our knowledge, this is the first onboard benchmark dataset tailored for aggressive flight in flapping-wing airborne platforms. © 2025 IEEE.

关键词： Micro air vehicle (MAV)

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Aircraft Engine Remaining Useful Life Prediction Using Attention-Based Convolutional Neural Network - Gated Recurrent Unit

Aircraft Engine Remaining Useful Life Prediction Using Atten...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Sun, Shilong Ding, Hao Huang, Haodong Guangdong Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Guangdong Shenzhen518055 China

ISBN: (纸本)9783031702341

As aircraft usage time increases, the state and performance of aircraft engines gradually deteriorate. In the context of rapid development in smart industries and aviation transport, accurate assessment of aircraft engine status and prediction of Remaining Useful Life (RUL) are crucial for flight safety and maintenance cost reduction. This study focuses on the multivariate time series features of historical data from aircraft engines. It develops an RUL prediction model based on the Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model integrated with an attention mechanism to enhance the focus on key features. Through training and testing on real engine datasets, the effectiveness and accuracy of the proposed method in RUL prediction tasks were validated. Experimental findings demonstrate that the proposed method offers relatively precise predictions of engine RUL, furnishing vital decision support and optimization strategies for airlines and maintenance teams. This research holds significant implications for enhancing flight operational safety, refining maintenance schedules, and reducing operational costs, presenting extensive prospects within the aerospace engineering domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Irrelevant Locomotion Intention Detection for Myoelectric Assistive Lower Limb Robot Control

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 655-665页

作者： Song, Xiaoyu Liu, Jiaqing Pan, Heng Rao, Haotian Wang, Can Wu, Xinyu Southern University of Science and Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

In this study, we propose a robust myoelectric intention recognition framework to recognize human locomotion mode and detect irrelevant locomotion intention. The framework is integrated into the control system of the lower limb exoskeleton robot for experimental validation. Most conventional electromyography (EMG) intention detection methods aim to accurately detect the target motion intentions but ignore the possible effects of irrelevant intentions. In traditional action intention recognition strategies, most researchers did not consider entering irrelevant action intentions into the model during training. Therefore, when using a classification model, if irrelevant action intentions are input, the model will still recognize it as a type of target action intention. That can lead to incorrect recognition results, which will cause the robot to perform wrong movements and pose a safety risk to the wearer. To detect and reject irrelevant motion intentions, we first used the dual-purpose autoencoder-guided temporal convolution network (DA-TCN) to obtain discriminative features of the surface EMG signal. Autoencoders (AE)/Variable Autoencoders (VAE) are then trained for each of the seven deep features of the target motion intention. In addition, irrelevant motion intentions are detected according to the value of their reconstruction error. The recall rate of this method for the detection of irrelevant motion intentions exceeds 99% and the accuracy rate exceeds 99%.At the same time, we replaced the TCN with the LSTM model and compared the performance of the two after adding irrelevant motion discrimination. We collected data on seven goals and three unrelated motor intentions from seven experimenters for testing and completed an online experimental validation. The motion recognition accuracy of all the experimenters can be maintained above 86%. © 2018 IEEE.

关键词： Limbs Legged locomotion Exoskeletons Robots Image color analysis Convolution Stairs Training Feature extraction Autoencoders

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Vision-Based Adaptive Prescribed-Time Control of UAV for Uncooperative Target Tracking with Performance Constraint

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Journal of systems Science & Complexity 2024年第5期37卷 1956-1977页

作者： SHE Xuehua MA Hui REN Hongru LI Hongyi School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of TechnologyGuangzhou 510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou 510006China College of Electronic and Information Engineering and Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing 400715China

This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle(UAV)under the performance constraint and scaled relative velocity constraint,in which the states of the uncooperative target can only be estimated through a vision *** the limited detection range,a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking ***,the scaled relative velocity constraint in the dynamic phase is taken into account,and a time-varying nonlinear transformation is used to solve the constraint problem,which not only overcomes the feasibility condition but also fails to violate the constraint ***,the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are *** is proved that the UAV can follow the uncooperative target at the desired relative position within a prescribed time,thereby improving the applicability of the vision-based tracking *** results have been presented to prove the validity of the proposed control strategy.

关键词： Nonlinear transformation performance constraint prescribed-time tracking uncooperative target vision-based measurement

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Obstacle avoidance in human-robot cooperative transportation with force constraint

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Science China(Information Sciences) 2023年第1期66卷 334-335页

作者： Ying ZHANG Chenguang YANG Sheng XU Yongsheng OU College of Automation Science and Engineering South China University of Technology Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences

Dear editor,Robots are often conceived as human-like machines that can interact with humans naturally and safely. Human-robot interaction means that robots can communicate with people,understand their needs, and act accordingly [1]. This interaction is critical when two people are needed to finish a task cooperatively.

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Inflatable Metamorphic Origami

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Research 2023年第2期6卷 53-63页

作者： Sen Wang Peng Yan Hailin Huang Ning Zhang Bing Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518052P.R.China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of TechnologyShenzhen 518052P.R.China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001P.R.China

This study created a new type of inflatable metamorphic origami that has the advantage of being a highly simplified deployable system capable of realizing multiple sequential motion patterns with a monolithic actuation.

关键词： simplified lata system

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