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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mohammad Bajelani S. Ahmad Khalilpour M. Isaac Hosseini Hamid D. Taghirad Philippe Cardou Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Robotics Laboratory Laval University Quebec City Canada

ISBN: (纸本)9781665420952

Concerning the lack of knowledge about non- linearity and uncertainties existing in the cable-driven robot models, an intelligent controller is proposed in this paper to overcome the lack of knowledge. Brain Emotional Learning is one of the bio-inspired algorithms which mimics the emotional part of the mammals’ brain. Not only does the Brain Emotional Learning Based Intelligent Controller (BELBIC) enable us to reach quick adaptation and robustness, but the computations are also very efficient. By defining the BELBIC learning functions with saturation functions, it is shown that the need to calculate the Jacobian matrix and forward kinematics in the feedback loop is eliminated, while guaranteeing positive tensions to the robot. The performance of the proposed method is examined by experiments, and results show that BELBIC can perform well in terms of tracking error.

关键词： Jacobian matrices Parallel robots Uncertainty Mechatronics Trajectory tracking MIMICs Linearity

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A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

arXiv

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arXiv 2022年

作者： Salagame, Adarsh Manjikian, Shoghair Wang, Chenghao Krishnamurthy, Kaushik Venkatesh Pitroda, Shreyansh Gupta, Bibek Jacob, Tobias Mottis, Benjamin Sihite, Eric Ramezani, Milad Ramezani, Alireza The Department of Electrical and Computer Engineering Northeastern University SiliconSynapse Laboratory BostonMA United States The Department of Aerospace Engineering California Institute of Technology PasadenaCA United States The Robotics and Autonomous Systems Group Data61 CSIRO BrisbaneQLD Australia

Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

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A colorimetric-bionic sensor for multimodal molecular sensing using hierarchical metamaterials

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Device 2025年第5期3卷

作者： Geng, Wenhui Zhou, Ying Huang, Kang Ying, Yibin Xie, Lijuan College of Biosystems Engineering and Food Science Zhejiang University 866 Yuhangtang Road Hangzhou 310058 China The National Key Laboratory of Agricultural Equipment Technology Beijing 100083 China Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture Hangzhou 310058 China Hangzhou Customs Technology Center 398 Jianshe San Road Xiaoshan District Hangzhou City China Department of Biological Systems Engineering Washington State University Pullman 99164 WA United States

Metamaterials can enhance a local electric field for molecular sensing. However, the difficulty in specifically recognizing target molecules and integrating multiple functionalities restricts their development. Here, we present a hierarchical metamaterial-based colorimetric-bionic sensor (HMm-CBs) using a layer-by-layer assembly method for multimodal molecular sensing. The CBs layers offer molecular recognition ability by enabling the specific perception of analytes, and the HMm layers provide coupling of vibrations between analyte molecules at the terahertz (THz) frequency. The HMm-CBs have broadband adaptability in controlling electromagnetic waves from optical to THz frequency. As a proof of principle, we designed a sensor for analyzing tetracycline hydrochlorides (TCHs). The sensor, in conjunction with a back propagation neural network (BPNN), identifies four types of TCHs. As a demonstration, a multichannel indicator is created for real-time monitoring of shrimp freshness and volatile metabolites. © 2025 The Author(s)

关键词： antibiotics carbon nanotubes chemometric algorithms colorimetric sensor array DTI-3: Develop metamaterials

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A Composite Robust Position Control Design for an Actuated Knee Rehabilitation Exoskeleton

A Composite Robust Position Control Design for an Actuated K...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Sahar Jenhani Hassène Gritli Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies & RISC Lab (LR16ES07) University of Carthage Tunis Tunisia

Exoskeletons or orthotics, also known as wearable technology, are often used to help the elderly and handicapped humans with limb disability to recover their physical mobility. In this work, we are interested in knee exoskeletons employed for the rehabilitation purpose. Our main goal is to control the exoskeleton at the knee joint level by considering external disturbances. Thus, to achieve this robust stabilization, we introduce a linear state-feedback control law combined with a nonlinear controller. The design of the linear control law is mainly relying on the LMI methodology. Simulation results on the robotic system are presented at the end of this paper to prove the performance of the suggested composite controller.

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Direct and quantitative assessments of near-infrared light attenuation and spectroscopic detection depth in biological tissues using surface-enhanced Raman scattering

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Med-X 2023年第1期1卷 62-78页

作者： Li Lin Haoqi He Ruiyang Xue Yumin Zhang Ziwen Wang Shuming Nie Jian Ye State Key Laboratory of Systems Medicine for Cancer School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiP.R.China Department of Bioengineering University of Illinois at Urbana-ChampaignIllinoisUSA Shanghai Key Laboratory of Gynecologic Oncology Ren Ji HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiP.R.China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghaiP.R.China

Optical imaging and spectroscopic modalities are of broad interest for in-vivo molecular imaging,fluorescence guided cancer surgery,minimally invasive diagnostic procedures,and wearable ***,considerable debate still exists as to how deeply visible and near-infrared(NIR)light could penetrate normal and diseased tissues under clinically relevant *** we report the use of surface-enhanced Raman scattering(SERS)nanotags embedded in ex-vivo animal tissues for direct and quantitative measurements of light attenuation and spectroscopic detection depth at both the NIR-I and NIR-II spectral *** nanotags are well suited for this purpose because of their sharp spectral features that can be accurately differentiated from fluorescence and background *** the first time,the spectroscopic detection depth is quantitatively defined and measured as the maximal thickness of tissues through which the embedded SERS nanotags are still detected at a signal-to-noise ratio(SNR)of three(99.7%confidence level).Based on data from six types of fresh ex-vivo tissues(brain,kidney,liver,muscle,fat,and skin),we find that the maximum detection depth values range from 1—3 mm in the NIR-I window,to 3—6 mm in the NIR-II *** depth values are largely determined by two factors-the intrinsic optical properties of the tissue,and the overall SNRs of the system without the tissue(system SNR,a result of nanotag brightness,instrument efficiency,and data acquisition parameters).In particular,there is an approximately linearlogarithmic relationship between the system SNR and maximum detection ***,the detection of hidden or occult lesions can be improved by three strategies-reducing tissue attenuation,minimizing background noise,and maximizing the system’s performance as judged by SNR.

关键词： Surface-enhanced Raman scattering Near-infrared window Biological detection Detection depth

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Hydrothermally Synthesis of Binary Niznfe2o4-Cnt Nanocomposite for Energy Storage Application

SSRN

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SSRN 2023年

作者： Ghadimi, Laleh Saleh Tabrizi, Amin Goljanian Rezaie, Ehsan Parvini, Elahe Tavakoli, Mahmoud Abouzari-Lotf, Ebrahim Hajalilou, Abdollah Arsalani, Nasser Pahlevani, Majid Department of Electrical and Computer Engineering Queen's University KingstonON Canada Research Laboratory of Polymer Department of Organic and Biochemistry Faculty of Chemistry University of Tabriz Iran Laboratory for Advanced Materials Faculty of Natural Sciences Comenius University Bratislava842 15 Slovakia Soft and Printed Microelectronics Laboratory Institute of Systems and Robotics University of Coimbra Coimbra Portugal Electrochemical Energy Storage Helmholtzstraße 11 UlmD-89081 Germany

Our research aimed to enhance carbon-based electrochemical double-layer capacitors (EDLCs) by incorporating faradic redox processes. We synthesized a Ni0.5Zn0.5Fe2O4-Carbon Nanotube (NZF-CNT) nanocomposite via hydrothermal methods at various pH levels. X-ray diffraction (XRD) analysis at pH 12 confirmed the formation of spinel nickel-zinc ferrite with 15 nm particles. X-ray photoelectron spectroscopy (XPS) verified cation distribution changes, and high-resolution transmission electron microscopy (HRTEM) revealed well-arranged NZF crystals around CNTs. The successful fusion of NZF and CNTs into the nanocomposite framework was demonstrated. NZF-CNT exhibited a remarkable specific capacitance of approximately 368 F/g at 1.25 A/g, surpassing pure ferrite's 110 F/g. Even after 5000 charge/discharge cycles at 1 A/g, it retained 88.5% of its initial capacitance. Our findings showcase the potential of NZF-CNT as a promising material for energy storage devices, offering a unique combination of EDLC and pseudo-capacitive properties. This approach opens new avenues for improving supercapacitor performance and their diverse applications. © 2023, The Authors. All rights reserved.

关键词： Supercapacitor

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Exploration of Canals: a German-Pakistani collaboration in education and research in marine robotics

Exploration of Canals: a German-Pakistani collaboration in e...

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OCEANS

作者： Francesco Maurelli Suleman Mazhar Syed Atif Mehdi Marine Systems and Robotics Jacobs University Bremen Bremen Germany Acoustic Science and Technology Laboratory Harbin Engineering University Harbin Heilongjiang China Center of Robotics University of Central Punjab Lahore Pakistan

ISBN: (纸本)9781665427883

This paper presents a collaborative project between Jacobs University Bremen and University of Central Punjab, supported by the German Academic Exchange Agency - Deutscher Akademischer Austauschdienst (DAAD). It focuses on the partnership activities and how they have been pivotal to support education and research in marine robotics. This is an example of a successful international project, which has contributed to bring together scientists from different backgrounds working for a common goal.

关键词： Jacobian matrices Irrigation Oceans Education Collaboration Robots

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Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms

Nonparametric Adaptive Control and Prediction: Theory and Ra...

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IEEE Conference on Decision and Control

作者： Nicholas M. Boffi Stephen Tu Jean-Jacques Slotine John A. Paulson School of Engineering and Applied Sciences Harvard University Google Brain Robotics Nonlinear Systems Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9781665436601

A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algorithms, it limits applications to very specific classes of systems. We introduce a novel nonparametric adaptive algorithm that learns an infinite-dimensional parameter density to cancel an unknown disturbance in a reproducing kernel Hilbert space. Surprisingly, the resulting control input admits an analytical expression that enables its implementation despite its underlying infinite-dimensional structure. While this adaptive input is rich and expressive – subsuming, for example, traditional linear parameterizations – its computational complexity grows linearly with time, making it comparatively more expensive than its parametric counterparts. Leveraging the theory of random Fourier features, we provide an efficient randomized implementation which recovers the computational complexity of classical parametric methods while provably retaining the expressiveness of the nonparametric input. In particular, our explicit bounds only depend polynomially on the underlying parameters of the system, allowing our proposed algorithms to efficiently scale to high-dimensional systems. As an illustration of the method, we demonstrate the ability of the algorithm to learn a predictive model for a 60-dimensional system consisting of ten point masses interacting through Newtonian gravitation.

关键词： Uncertainty Conferences Adaptive algorithms Predictive models Prediction theory Prediction algorithms Hilbert space

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A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties

A Haptic Exploration and Surface Classification of Objects w...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Peng Qi Yunfeng Wu Tianliang Yao Bo Lu Yi Sun Jian S. Dai Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai P. R. China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou Jiangsu China Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Mechanical and Energy Engineering College of Engineering Southern University of Science and Technology Shenzhen P. R. China School of Natural Mathematical and Engineering Sciences King's College London

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object; however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index $K$ is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%.

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Transfer Learning in robotics: An Upcoming Breakthrough? A Review of Promises and Challenges

arXiv

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arXiv 2023年

作者： Jaquier, Noémie Welle, Michael C. Gams, Andrej Yao, Kunpeng Fichera, Bernardo Billard, Aude Ude, Aleš Asfour, Tamim Kragic, Danica Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany KTH Royal Institute of Technology Stockholm Sweden Jožef Stefan Institute Ljubljana Slovenia Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Faculty of Electrical Engineering University of Ljubljana Slovenia

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept — reusing prior knowledge to learn in and from novel situations — is successfully leveraged by humans to handle novel situations. In recent years, transfer learning has received renewed interest from the community from different perspectives, including imitation learning, domain adaptation, and transfer of experience from simulation to the real world, among others. In this paper, we unify the concept of transfer learning in robotics and provide the first taxonomy of its kind considering the key concepts of robot, task, and environment. Through a review of the promises and challenges in the field, we identify the need of transferring at different abstraction levels, the need of quantifying the transfer gap and the quality of transfer, as well as the dangers of negative transfer. Via this position paper, we hope to channel the effort of the community towards the most significant roadblocks to realize the full potential of transfer learning in robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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