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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 678-686页

作者： Lin, Chengyu Cheng, Kong Hoi Pan, Wei Sun, Jinxin Gou, Guotao Fu, Junyun Leng, Yuquan Fu, Chenglong Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities Department of Mechanical and Energy Engineering Shenzhen518055 China Guilin University of Electronic Technology Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center School of Life and Environmental Sciences Guilin541004 China

Grasping is a critical motor skill essential for daily activities, but it is often compromised in individuals with neural impairments. Functional Electrical Stimulation (FES) has emerged as a promising intervention, utilizing electrical pulses to stimulate muscles and thereby restore impaired motor functions. However, existing closed-loop FES systems depend on pre-calibrated angles or forces specific to individual objects, which limits their practicality in dynamic, real-world environments with varying object properties. This paper presents a novel closed-loop FES (CLFES) system with visual feedback, designed to dynamically adjust stimulation parameters based on real-time interaction states without requiring object-specific calibration. The system employs a finite state machine to manage sequential grasp-release tasks and integrates a visual perception module for slip detection and intent recognition. The system was tested with two individuals with disabilities on five common household objects. Experimental results demonstrate significant improvements, including a 42.6% increase in success rate and a 45.9% reduction in task completion time compared to tasks performed without the system. These results underscore the system's potential to improve daily task performance for individuals with neural impairments. © 2018 IEEE.

关键词： Visual servoing

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Navigating Problematic Bauhaus Inheritances: Critiques, Implications, and Questions from the Bauhaus of the Seas NEB Lighthouse

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DESIGN ISSUES 2024年第3期40卷 105-117页

作者： Torretta, Nicholas B. Pestana, Mariana Duarte, Frederico Predroso-Roussado, Cristiano Seixas, Luisa Metelo Nisi, Valentina Nunes, Nuno Jardim Interactive Technologies Institute Lisbon University Instituto Superior Técnico University Faculdade de Belas-Artes da Universidade de Lisboa (FBAUL) Interactive Technologies Institute - Laboratory of Robotics and Engineering Systems (ITI/LARSyS) Instituto Superior Técnico University NOVA FCSH Instituto Superior Técnico University Malmö University

In 2020, Europe announced the New European Bauhaus (NEB). While the initiative intends to achieve EU sustainability goals, framing it under the name of the Bauhaus brings various challenges and issues to the fore. In this article, we analyze the critiques of the original Bauhaus and the NEB to understand the challenges that the NEB lighthouse project Bauhaus of the Seas Sails (BoSS) inherits by adhering to the Bauhaus vision and name. We unveil the problematic dynamics of Eurocentric modernity's myths of universalism and better living through technology and on the Bauhaus's and NEB's position in global power structures. Instead of assuming a tabula rasa approach and replicating problematic structures unknowingly, we bring these three aspects to BoSS to find questions as orientation points to help steer away from problematic aspects inherited by reanimating the Bauhaus name and its legacy.

关键词： Bauhaus New European Bauhaus decolonial criticality regenerative design

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Nonlinear Model Predictive Control for Optimal Dose Administration in Radiotherapy 15th

Nonlinear Model Predictive Control for Optimal Dose Administ...

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15th Advanced Doctoral Conference on Computing, Electrical and Industrial systems, DoCEIS 2024

作者： Araújo, João C. G. Guerreiro, Bruno J. Oliveira, Luis B. da Silva, Filipe Ferreira CTS-UNINOVA and LASI Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica2829-516 Portugal Institute for Systems and Robotics LARSyS Lisbon1049-001 Portugal CEFITEC Department of Physics NOVA School of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9783031638503

This work discusses the nonlinear modeling of healthy and cancer cell population growth under the influence of radiotherapy, and proposes predictive control techniques to determine the optimal dose for tumor control, while sparing surrounding healthy tissue. Radiation effects are modelled by a linear-quadratic formalism, coupled with a novel population-specific delayed dose response. Numerical simulations of the open- and closed-loop system are conducted, and robustness against model uncertainty is studied. Results illustrate the controller’s performance when dealing with imperfect information and unexpected system behavior, and how it can be tuned to produce the desired treatment outcome. However, optimal operation is conditioned by a limited prediction horizon, as well as strict adherence to treatment schedules and dose restrictions. © IFIP International Federation for Information Processing 2024.

关键词： Nonlinear systems

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Physics-Informed Neural Network for Multirotor Slung Load systems Modeling

Physics-Informed Neural Network for Multirotor Slung Load Sy...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Gil Serrano Marcelo Jacinto José Ribeiro-Gomes João Pinto Bruno J. Guerreiro Alexandre Bernardino Rita Cunha Institute for Systems and Robotics (ISR) Laboratory of Robotics and Engineering Systems (LARSyS) Instituto Superior Técnico University of Lisbon Portugal CTS/Uninova NOVA School of Science and Technology (FCT-NOVA) Caparica Portugal

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent advances in aerial robotics have enabled the use of multirotor vehicles for autonomous payload transportation. Resorting only to classical methods to reliably model a quadrotor carrying a cable-slung load poses significant challenges. On the other hand, purely data-driven learning methods do not comply by design with the problem’s physical constraints, especially in states that are not densely represented in training data. In this work, we explore the use of physics-informed neural networks to learn an end-to-end model of the multirotor-slung-load system and, at a given time, estimate a sequence of the future system states. An LSTM encoder-decoder with an attention mechanism is used to capture the dynamics of the system. To guarantee the cohesiveness between the multiple predicted states of the system, we propose the use of a physics-based term in the loss function, which includes a discretized physical model derived from first principles together with slack variables that allow for a small mismatch between expected and predicted values. To train the model, a dataset using a real-world quadrotor carrying a slung load was curated and is made available. Prediction results are presented and corroborate the feasibility of the approach. The proposed method outperforms both the first principles physical model and a comparable neural network model trained without the physics regularization proposed.

关键词： Training Neural networks Transportation Training data Predictive models systems modeling Vehicle dynamics

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Distributed accelerated primal-dual neurodynamic approaches for resource allocation problem

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science China(Technological sciences) 2023年第12期66卷 3639-3650页

作者： ZHAO You HE Xing YU JunZhi HUANG TingWen College of Electronic and Information Engineering Southwest UniversityChongqing 400715China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China Science Program Texas A&M University at QatarDoha 2387Qatar

This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems(RAP)where the objective functions are generally *** the help of projection operators,a primal-dual framework,and Nesterov's accelerated method,we first design a distributed accelerated primal-dual projection neurodynamic approach(DAPDP),and its convergence rate of the primal-dual gap is O(1/(t^(2)))by selecting appropriate parameters and initial ***,when the local closed convex sets are convex inequalities which have no closed-form solutions of their projection operators,we further propose a distributed accelerated penalty primal-dual neurodynamic approach(DAPPD)on the strength of the penalty method,primal-dual framework,and Nesterov's accelerated *** on the above analysis,we prove that DAPPD also has a convergence rate O(1/(t^(2)))of the primal-dual *** with the distributed dynamical approaches based on the classical primal-dual framework,our proposed distributed accelerated neurodynamic approaches have faster convergence *** simulations demonstrate that our proposed neurodynamic approaches are feasible and effective.

关键词： accelerated primal-dual neurodynamic approaches RAP projection operators penalty method convergence rate O(1/(t^(2)))

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Integrated Cross-Scale Manipulation and Modulable Encapsulation of Cell-Laden Hydrogel for Constructing Tissue-Mimicking Microstructures

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Research 2025年第1期2024卷 704-717页

作者： Yanfeng Zhao Xinyi Dong Yang Li Juan Cui Qing Shi Hen-Wei Huang Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Peking University First Hospital Xicheng DistrictBeijing 100034China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of EducationNorth University of ChinaTaiyuan 030051China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Laboratory for Translational Engineering Harvard Medical SchoolCambridgeMA 02139USA Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of EducationBeijing 100081China

Engineered microstructures that mimic in vivo tissues have demonstrated great potential for applications in regenerative medicine,drug screening,and cell behavior ***,current methods for engineering microstructures that mimic the multi-extracellular matrix and multicellular features of natural tissues to realize tissue-mimicking microstructures in vitro remain ***,we propose a versatile method for constructing tissue-mimicking heterogeneous microstructures by orderly integration of macroscopic hydrogel exchange,microscopic cell manipulation,and encapsulation ***,various cell-laden hydrogel droplets are manipulated at the millimeter scale using electrowetting on dielectric to achieve efficient hydrogel ***,the cells are manipulated at the micrometer scale using dielectrophoresis to adjust their density and arrangement within the hydrogel ***,the photopolymerization of these hydrogel droplets is triggered in designated regions by dynamically modulating the shape and position of the excitation ultraviolet ***,heterogeneous microstructures with different extracellular matrix geometries and components were constructed,including specific cell densities and *** resulting heterogeneous microstructure supported long-term culture of hepatocytes and fibroblasts with high cell viability(over 90%).Moreover,the density and distribution of the 2 cell types had significant effects on the cell proliferation and urea *** propose that our method can lead to the construction of additional biomimetic heterogeneous microstructures with unprecedented potential for use in future tissue engineering applications.

关键词： cell behavior explorationhowevercurrent tissue mimicking microstructures cell laden hydrogel electrowetting dielectric regenerative medicinedrug screeningand heterogeneous microstructures engineered microstructures orderly integration

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Droplet-based Microfluidic Platform for High-throughput Formation of Multicellular Spheroids

Droplet-based Microfluidic Platform for High-throughput Form...

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2023 Annual IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2023

作者： Zhan, Zhen Xie, Hanhan Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen China

ISBN: (纸本)9798350344486

Cell culture in vitro is essential for drug screening, disease modeling, and stem cell-related research. Previous studies have indicated that two-dimensional (2D) cellular environments can significantly differ from physiological cells in vivo, leading to a high failure rate when translating therapeutics developed in 2D cell models to clinical applications. Therefore, there is a strong focus on developing three-dimensional (3D) cell culture techniques that more closely mimic in vivo cell microenvironments. These techniques hold great promise for enhancing precision in drug discovery and tissue regeneration. In this study, we introduce a novel microfluidic technique that utilizes droplets to fabricate hydrogel microspheres, each exhibiting a unique internal morphology suitable for 3D cell culture applications. Specifically, we employed this method to cultivate human umbilical vein endothelial cells (HUVECs) within the interior canals of the hydrogel microspheres, forming monolayers on the surfaces of the spiral canals. This setup effectively mimicked the configurations of native blood vessels. Furthermore, we successfully demonstrated the fabrication of heterogeneous spheroids with spiral blood vessel lumens, which can be utilized to construct spiral vascularized bone tissue. © 2023 IEEE.

关键词： Microfluidics

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Instability-Induced Origami Design by Topology Optimization

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Acta Mechanica Solida Sinica 2023年第4期36卷 506-513页

作者： Weiwei Wang Ke Liu Meiqi Wu Hongyuan Li Pengyu Lv Huiling Duan State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China CAPT HEDPS and IFSACollaborative Innovation Center of MoEPeking UniversityBeijing 100871China

Instability-induced wrinkle patterns of thin sheets are ubiquitous in nature,which often result in origami-like patterns that provide inspiration for the engineering of origami *** by instability-induced origami patterns,we propose a computational origami design method based on the nonlinear analysis of loaded thin sheets and topology *** bar-and-hinge model is employed for the nonlinear structural analysis,added with a displacement perturbation strategy to initiate out-of-plane *** ideas from topology optimization,a continuous crease indicator is introduced as the design variable to indicate the state of a crease,which is penalized by power functions to establish the mapping relationships between the crease indicator and hinge *** the structural strain energy with a crease length constraint,we are able to evolve a thin sheet into an origami structure with an optimized crease *** examples with different initial setups are illustrated,demonstrating the effectiveness and feasibility of the method.

关键词： Origami structure Instability Topology optimization Bar-and-hinge model

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