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A trajectory-level robot skill learning method based on fuzzy systems

Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Joint Probabilistic Scheduling and Resource Allocation for Wireless Networked Control Systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 258-260页

作者： Meng Zheng Lei Zhang Wei Liang IEEE the State Key Laboratory of Robotics and the Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences the University of Chinese Academy of Sciences

Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.

关键词： Networked control systems

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An improved YOLOv3 model for detection of invasive Saccharomyces Cerevisiae infections

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Multimedia Tools and Applications 2025年第15期84卷 14605-14622页

作者： Li, Gongxin Li, Shuai Fang, Xing Luan, Xiaoli Liu, Fei Institute of Automation Jiangnan University Wuxi214122 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Saccharomyces Cerevisiae (S. cerevisiae) is a fermenting strain that commonly used in the baking and brewing industries and also used as a probiotic for the prevention and treatment of various diarrhea and related diseases. However, cases of invasive S. cerevisiae infection due to the ingestion of S. cerevisiae have been increasing over the last two decades, especially in elderly, immunocompromised and critically ill patients. In addition, the currently methods used in clinical for the diagnosis of invasive S. cerevisiae infections are time-consuming and easily miss the optimal time for diagnosis and early prevention. Here, we propose an improved YOLOv3 model-based deep learning method to detect S. cerevisiae with higher efficient. The improved YOLOv3 model is derived from the YOLOv3 model by re-constructing its’ model structure with a lightweight network (GhostNet) and other related functions to improve the detection precision and efficiency, and the improved performances were experimentally validated on blood samples. Furthermore, the detection performance by the method was also experimentally compared with that by other several typical deep learning methods. The experimental results showed that the average precision and average detection time on a single image of the improved YOLOv3 model is 97.83% and 0.021 s, respectively, and the both indexes significantly outperform the other methods. The study provides a novel and potential method to detect invasive S. cerevisiae infection with higher precision and faster rate, and also offers a new approach for timely diagnosis and early prevention of the related diseases reduced by microorganism. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Yeast

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Theoretical Model Construction and Static Performance Analysis of Multi-Leaf Journal Foil Bearings

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Journal of Tribology 2025年第7期147卷 074101页

作者： Hu, Yang Ding, Pengjing School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200072 China

Multi-leaf journal foil bearing (MLJFB) structures are complex, making performance prediction challenging, and the accurate prediction of gas film thickness and pressure distributions is crucial. In this study, comsol finite-element software was used to establish an initial foil assembly and structural deformation simulation model for an MLJFB with bump foils. Using matlab, a compressible gas lubrication model was established and solved using the finite volume method. Real-time data transfer between comsol and matlab enabled a fully coupled fluid-structure interaction simulation. This model meticulously accounts for the pre-tension force in the multi-leaf bearing assembly, contact friction between adjacent foils, and the interaction between the top foil and gas film pressure. The effects of journal movement distance, direction, and top foil overlapping ratio on the bearing capacity as well as the effects of different external load directions on the journal equilibrium position were analyzed based on this model. The results indicate that increasing the top foil overlapping ratio enhances the bearing capacity of the MLJFB. Furthermore, compared to the direction of the top foil overlap position, external loads applied in the direction of the top foil midpoint are more beneficial for bearing operational stability. Copyright © 2024 by ASME.

关键词： MATLAB

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Automated Force Curve Methods Based Micropipette Force Sensors

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IEEE Transactions on Nanotechnology 2025年 24卷 224-230页

作者： Shi, Huiyao Tang, Si Shi, Jialin Yu, Peng Yang, Tie Su, Chanmin Liu, Lianqing State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China University of the Chinese Academy of Sciences Beijing100049 China

Force curve is the most important techniques for accurate measuring the stiffness, adhesion and energy dissipation. However, due to the challenges of probe-cell localization, this type of single-cell analysis tool has become a labor-intensive and time-consuming procedure. Here, we demonstrate an automatic positioning methods based on micropipette for force curves acquisition. The automation covers the detection of cells in label-free images, pre-positioning and automatic focusing of micropipette, as well as automated force curves. This new method discards silicon-based probes used in traditional AFM, and instead utilizing a transparent micropipette with an unobstructed tip as the sensor. This advancement enables accurate localization of the probe tip and cell under an optical microscope. Furthermore, during the probe positioning process, we have implemented a pre-localization method using focused laser projection. This allows for manual adjustment of the probe tip within a range of 70 micrometers, providing precise region of interests (ROI) for automatic tip focusing. Combining with the aforementioned techniques. We also demonstrate the high-precision localization and force curve acquisition of eight fixed cells on a single image. The measurement results indicate that the positioning error will not exceed 3.4 μm. Our work has effectively enhanced the efficiency of cellular mechanical measurements and holds the potential to achieve automated high-throughput single-cell analysis and drug screening. © 2002-2012 IEEE.

关键词： Probes

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Model predictive control and replanning for magnetic microrobots in fluid environments

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Science China Technological Sciences 2025年第5期68卷 142-154页

作者： Junjian ZHOU Mengyue LI Na LI Huanyu ZHOU Xiaodong WANG Jingyi WANG Lianqing LIU Niandong JIAO State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences College of Information and Electrical Engineering Shenyang Agricultural University

Magnetic microrobots, given their unique characteristics, hold great potential in biomedical applications such as targeted therapy and microscale operations and are receiving widespread attention. Research on the autonomous navigation of magnetic microrobots is highly focused, as it is an essential prerequisite for achieving functions such as targeted delivery in medical settings. The success of autonomous navigation determines the level of intelligence and precision in the motion of magnetic microrobots. However, uncertainties stemming from environmental changes and time-varying disturbances in electromagnetic systems adversely affect the control accuracy of magnetic microrobots. Additionally, the random appearance of dynamic obstacles along expected trajectories challenges their autonomous navigation. In this study, we demonstrate a method for the exact autonomous navigation of magnetic microrobots in fluid environments, successfully avoiding dynamic obstacles that suddenly appear in predefined trajectories. Improved versions of the A*algorithm and dynamic window approach are integrated as path planners, that can generate smooth and collision-free trajectories that adhere to kinematic constraints in fluid environments with dynamic obstacles. A learning-based model predictive control strategy is employed, where radial basis function neural networks are used to effectively predict and compensate for fluid disturbances and inevitable errors introduced by electromagnetic system coupling, thereby ensuring the control accuracy of the magnetic microrobot in a flowing environment. Experiments in a constructed microfluidic environment validate the effectiveness of our navigation approach in motion control, autonomous navigation, and replanning, with an average error of less than 8% of the body length of the microrobot.

关键词： magnetic microrobot model predictive control path planning radial basis function neural network

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Deep Learning-Based Automatic Control of Magnetic Diatom Biohybrid Microrobots for Targeted Delivery

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IEEE Transactions on robotics 2025年 41卷 2990-3003页

作者： Li, Mengyue Li, Liang Zhou, Junjian Liu, Lianqing Jiao, Niandong Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

Biohybrid microrobots with autonomous movement capabilities have broad application prospects in targeted delivery, attracting researchers to study their movement characteristics. However, its automatic control is still challenging, and exploring real-time detection of its environment for path planning to achieve stable closed-loop control is highly important for its practical application. Here, we applied deep learning for the detection of biohybrid microrobots and their targets and obstacles, followed by real-time path planning and trajectory tracking of biohybrid microrobots for targeted delivery. The proposed detection algorithm introduces attention and multiscale feature fusion mechanisms in YOLOv7 algorithm (AM-YOLOv7) with the aim of enhancing the precision of detecting small-scale targets when robots, obstacles and targets are displayed globally, and the detection capabilities are verified through simulations and experiments. The proposed planning algorithm introduces a turning penalty function and a path smoothing strategy into A∗ algorithm (PS-A∗) to make the planned path short and smooth, which has been verified through simulation and experiments. The adaptive fuzzy PID method is used to track the robot's trajectory, and experiments and simulations show that the biohybrid microrobot can move according to the preset trajectory better. The final cell scene experimental results show that the biohybrid microrobot using this system can effectively avoid obstacle cells and be delivered to target cells. The system can detect biohybrid microrobots, obstacle cells and target cells, plan short and smooth trajectories, and track them accurately. The proposed method has certain generalizability and broad application prospects in targeted delivery. © 2004-2012 IEEE.

关键词： Robot programming

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Nanograting‑Based Dynamic Structural Colors Using Heterogeneous Materials

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Nano-Micro Letters 2025年第3期17卷 138-151页

作者： Jingang Wang Haibo Yu Jianchen Zheng Yuzhao Zhang Hongji Guo Ye Qiu Xiaoduo Wang Yongliang Yang Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China

Dynamic structuralcolors can change in response todifferent environmental *** ability remains effectiveeven when the size of the speciesresponsible for the structural coloris reduced to a few micrometers,providing a promising sensingmechanism for solving microenvironmentalsensing problems inmicro-robotics and ***, the lack of dynamicstructural colors that can encoderapidly, easily integrate, and accuratelyreflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensionaldynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogelwith an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollenstate, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structuralcolors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamicstructural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.

关键词： Dynamic structural colors Four-dimensional printing pH-responsive Nanogrid Heterogeneous materials

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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Science China(Information Sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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An Untethered Bionic Soft Robotic Vehicle With Magnetic Actuation and Control for Multi-Scenario Applications

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IEEE robotics and automation Letters 2025年第6期10卷 5289-5296页

作者： Yang, Wenguang Qiao, Zezheng Gao, Zhizheng Yu, Haibo Yantai University School of Electromechanical and Automotive Engineering Yantai264005 China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China

In this study, a bionic soft robotic vehicle is designed with overall dimensions of 22.7 mm in length and 22 mm in width and weighs just 1.69 g. The robotic vehicle can realize precise motion control through a rotating magnetic field generated by Helmholtz coils. In the rotating magnetic field environment, by changing the parameters of the input current waveform, the vehicle can perform a variety of modes of motion, such as forward, backward, steering, and more. Furthermore, the robotic vehicle exhibits excellent motion performance. It is capable of moving stably on a variety of surfaces, including glass, wooden board, frosted board, and sandy soil, and can even successfully climb over obstacles as high as 30°. In particular, it can climb over a 20° barrier at 11.87 mm/s when loaded with three times its own weight (5 g). At the functional level, the robotic vehicle showcases its capabilities in obstacle clearance, path planning, and detection, underscoring its potential for performing complex tasks. © 2016 IEEE.

关键词： Motion planning

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