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IEEE International Conference on robotics and automation (ICRA)

作者： Rui Huang Hong Cheng Hongliang Guo Qiming Chen Xichuan Lin Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781467380270

Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for one pilot in different walking patterns. This characteristic requires that the exoskeletons should have the ability to learn and adapt the motion trajectories as well as controllers online. This paper presents a novel Hierarchical Interactive Learning (HIL) strategy which reduces the complexity of the exoskeleton sensory system and is able to handle varying interaction dynamics. The proposed HIL strategy is composed of two learning hierarchies, namely, high-level motion learning and low-level controller learning. The Dynamic Movement Primitives (DMPs) combined with Locally Weighted Regression (LWR) are employed to model and learn the motion trajectories, while reinforcement learning (RL) is used to learn the model-based controller. We demonstrate the efficiency of proposed HIL strategy on a single degree-of-freedom (DOF) platform as well as a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HIL strategy is able to handle the varying interaction dynamics with less interaction force between the pilot and the exoskeleton when compared to traditional model-based control algorithms.

关键词： Exoskeletons Trajectory Adaptation models Dynamics Robot sensing systems Learning (artificial intelligence) Sensitivity

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Author Correction: Observation of an ultra-high-energy cosmic neutrino with KM3NeT

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Nature 2025年第8057期640卷 E3页

作者： S. Aiello R. Bruno G. Ferrara E. Leonora F. Longhitano N. Randazzo A. Sinopoulou I. Tosta e Melo A. Albert D. Drouhin T. Pradier A. R. Alhebsi S. Thoudam A. van Vliet M. Alshamsi V. Bertin M. Breuhaus J. Brunner J. Busto L. Cerisy P. Coyle B. De Martino D. Dornic J.-G. Ducoin A. Enzenhöfer S. Henry D. Joly S. Lancelin S. Le Stum N. Lumb F. Magnani C. Paries M. Perrin-Terrin G. Vannoye S. Alves Garre A. Bariego-Quintana D. Calvo V. Cecchini A. Garcia Soto S. R. Gozzini J. J. Hernández-Rey A. Lazo N. Lessing J. Palacios González E. J. Pastor Gomez J. Prado D. Real A. Šaina F. Salesa Greus A. Sánchez Losa J. D. Zornoza J. Zúñiga A. Ambrosone M. Bendahman M. Benhassi C. Bozza R. M. Bozza R. Buompane A. Condorelli A. De Benedittis L. A. Fusco L. Gialanella W. Idrissi Ibnsalih A. Manfreda A. Marinelli F. Marzaioli S. Mastroianni G. Miele P. Migliozzi M. L. Mitsou C. M. Mollo L. Morales-Gallegos M. R. Musone V. Oliviero C. Poirè I. C. Rea A. Simonelli B. Spisso D. Vivolo F. Ameli A. Capone S. Celli I. Di Palma S. Gagliardini M. Mastrodicasa C. A. Nicolau A. Tudorache A. Veutro A. Zegarelli M. Andre M. Anghinolfi F. Badaracco B. Caiffi V. Ellajosyula C. Guidi V. Kulikovskiy L. Malerba M. Marconi B. Ó Fearraigh V. Parisi A. Romanov M. Sanguineti M. Taiuti S. Zavatarelli L. Aphecetche F. Bretaudeau R. Dallier V. Decoene L. Martin M. Ardid S. Ardid J. García Méndez C. A. Quiroz-Rangel C. Argüelles N. Kamp A. Y. Wen H. Atmani J. Boumaaza A. Eddymaoui Y. Tayalati J. Aublin B. Baret Y. Becherini T. Cartraud J. A. B. Coelho A. Coleiro S. Colonges A. Creusot C. Donzaud M. Eff S. El Hedri I. Goos P. Goswami A. Ilioni A. Kouchner M. Lindsey Clark E. Oukacha S. Peña Martínez R. Pestes A. Sharma B. Trocmé V. Van Elewyck C. Verilhac I. Lhenry-Yvon L. Bailly-Salins M. Bennani B. Guillon M. Labalme C. Lastoria G. Lehaut I. Mozun Mateo V. Pestel A. Vacheret Z. Bardačová E. Eckerová F. Mamedov Y. Shitov I. Štekl R. Dvornický F. Šimkovic F. Benfenati Gualandi F. Carenini P. Castaldi T. Chiarusi I. Del Rosso G. Levi A. Margiotta R. Muller INFN Sezione di Catania (INFN-CT) Catania Italy INFN Laboratori Nazionali del Sud (LNS) Catania Italy Dipartimento di Fisica e Astronomia “Ettore Majorana” Università di Catania (INFN-CT) Catania Italy Université de Strasbourg CNRS IPHC UMR 7178 Strasbourg France Université de Haute Alsace Mulhouse France Department of Physics Khalifa University Abu Dhabi United Arab Emirates Aix Marseille Université CNRS/IN2P3 CPPM Marseille France IFIC - Instituto de Física Corpuscular (CSIC - Universitat de València) Paterna Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli Italy Università di Napoli “Federico II” Dip. Scienze Fisiche “E. Pancini” Complesso Universitario di Monte S. Angelo Napoli Italy Dipartimento di Matematica e Fisica Università degli Studi della Campania “Luigi Vanvitelli” Caserta Italy Dipartimento di Fisica Università di Salerno e INFN Gruppo Collegato di Salerno Fisciano Italy INFN Sezione di Roma Roma Italy Dipartimento di Fisica Università La Sapienza Roma Italy Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain INFN Sezione di Genova Genova Italy Università di Genova Genova Italy LPC CAEN Normandie Université ENSICAEN UNICAEN CNRS/IN2P3 Caen France Subatech IMT Atlantique IN2P3-CNRS Nantes Université Nantes France Instituto de Investigación para la Gestión Integrada de las Zonas Costeras Universitat Politècnica de València Gandia Spain Department of Physics Harvard University Cambridge MA USA Laboratory for Particle Physics and Cosmology Lyman Laboratory Harvard University Cambridge MA USA Faculty of Sciences University Mohammed V in Rabat Rabat Morocco Institute of Applied Physics Mohammed VI Polytechnic University Ben Guerir Morocco Astroparticule et Cosmologie Université Paris Cité CNRS Paris France Institut Universitaire de France Paris France Institute of Experimental and Applied Physics

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A real-time supervised learning approach for sky segmentation onboard unmanned aerial vehicles

A real-time supervised learning approach for sky segmentatio...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Adrian Carrio Carlos Sampedro Changhong Fu Jean-François Collumeau Pascual Campoy Computer Vision Group Center for Automation and Robotics (UPM-CSIC) Madrid Spain School of Mechanical and Aerospace Engineering and ST Engineering-NTU Corp Laboratory Nanyang Technological University Singapore

Vision-based sky segmentation and horizon line detection can be extremely useful to perform important tasks onboard Unmanned Aerial Vehicles (UAVs), such as pose estimation and collision avoidance. Most of the existing vision-based solutions use traditional image processing methods to identify the horizon line. This results in good overall accuracy and fast computation times. However, difficult environmental conditions such as a foggy or cloudy skies hinder correct sky segmentation. This paper proposes a solution for sky segmentation in RGB images using a supervised Machine Learning approach by first splitting the image into fixed-size patches, extracting and classifying color descriptors for each patch and performing a final post-processing stage to improve segmentation quality. A method for automatic horizon line detection is also proposed. The performance of our approach was evaluated on flight images captured onboard UAVs, achieving performance accuracies above 93% at real-time frame rates.

关键词： Image color analysis Image segmentation Aircraft Image edge detection Histograms Real-time systems Unmanned aerial vehicles

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Learning Cooperative Primitives with physical Human-Robot Interaction for a HUman-powered Lower EXoskeleton

Learning Cooperative Primitives with physical Human-Robot In...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Rui Huang Hong Cheng Hongliang Guo Xichuan Lin Qiming Chen Fuchun Sun School of Automation Center for Robotics University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Nanyang Technological University Singapore School of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781509037636

Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical Human-Robot Interaction (pHRI). As a powerful representation tool for motion trajectories, Dynamic Movement Primitive (DMP) has been used extensively to model human movement trajectories. However, canonical DMPs only offers a general offline representation of human movement trajectory and neglects the real-time interaction term, therefore it cannot be directly applied to lower exoskeletons which need to model human motion trajectories online since different pilots have different trajectories and even one pilot might change his/her intended trajectory during walking. This paper presents a novel Coupled Cooperative Primitives (CCPs) scheme, which models the motion trajectories online. Besides maintaining canonical motion primitives, we also model the interaction term between the pilot and exoskeletons through impedance models and apply a reinforcement learning method based on Policy Improvement and Path Integrals (PI~2) to learn the parameters online. Experimental results on both a single Degree-Of-Freedom (DOF) platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme.

关键词： HUman-powered Augmentation Lower EXoskeleton (HUALEX) Coupled Cooperative Primitive (CCP) Dynamic Movement Primitive (DMP) Reinforcement Learning Physical Human-Robot Interaction

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A replica consistency detection method for text images based on multi-feature assessment

A replica consistency detection method for text images based...

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IEEE Instrumentation and Measurement Technology Conference

作者： Qianli Zhong Jiasheng Hao Lu Yang Yuanzhe Yao School of Automation Engineering Center for Robotics University of Electronic and Science Technology of China (UESTC) Chengdu Sichuan PR CHINA School of Information and Software Engineering University of electronic and Science Technology of China (UESTC) Chengdu Sichuan PR CHINA

ISBN: (纸本)9781467392211

The replica consistency detection of text images is of great significance in office automation. For example, it can be applied to detect the consistency of the printed documents of commercial contracts and the original electronic documents. In this paper, we propose a method for replica consistency detection of text images based on multi-feature assessment. Firstly, we make a photocopy of the printed document, then apply our method to detect the consistency between the photocopied text images and the original text images. Rather than recognizing each character, we only focus on the measurements of the likeness of these two text images, and no OCR techniques are required. The experiment results indicate that the method has high accuracy. Although the Chinese characters are the main research objects, the experiment results show that the method also can be used for detecting the consistency of Latin letters and Arabic numerals.

关键词： Decision support systems Office automation

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Efficient cell electrofusion chip based on micromanipulation

Efficient cell electrofusion chip based on micromanipulation

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International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Junhui Zhu Yong Wang Ruihua Chen Chengsong Shu Changhai Ru School of Mechatronic Engineering and Automation Shanghai University Shanghai China Research Center of Robotics and Micro Systems Soochow University Suzhou China Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou China

ISBN: (纸本)9781509029464

A novel cell electrofusion chip based on the micromanipulation technique was presented. A fast, accurate and flexible cell pairing method was developed for this chip. The chip prototype was fabricated on a indium tin oxide (ITO) substrate that was subsequently covered by a positive photoresist film (~2 μm thickness). Micropore electrodes measuring 25μm in diameter were fabricated on the film by lithography. Electrode arrays with different spacing were manufactured and paired with tungsten needle for cell electrofusion experiment. Result showed that electrodes with a 10mm spacing can pair the cell efficiently, however electrodes with 0.5mm spacing could not attach the cells.

关键词： Electrodes Tungsten Microfluidics Force Needles Dielectrophoresis Tumors

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A piezoelectric inchworm actuator of linear type using symmetrical lever amplification

A piezoelectric inchworm actuator of linear type using symme...

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作者： Ma, Li Xiao, Jintao Zhou, Shasha Sun, Lining School of Mechatronic Engineering and Automation Shanghai University Shanghai200072 China Robotics and Microsystems Center Soochow University Suzhou China

Piezoelectric inchworm actuators have a wide application in the field of nano-positioning and ultra-precision detecting instruments. Ultra-precision positioning equipments are urgently needed in the field of precision optics. A new piezoelectric linear actuator, based on inchworm motion principle, with a symmetry lever displacement amplification mechanism has been designed in this article. The whole structure adopts uniaxial-type double-notch right circular flexible hinge as its main hinges, which offers the driving part a larger displacement and makes clamping part have enough clamping force at the same time. High-precision cross roller guide ways are utilized to improve the positioning accuracy of the actuator. Both theoretical analysis and finite element analysis of clamping mechanism and driving mechanism have been carried out. An experimental test platform has been built, and a controlling program of the actuator is compiled by LabVIEW. The experimental results show that the working stroke of the actuator is 6 25 mm, resolution is 60 nm, the clamping force is 17 N, and the bearing capacity is 11 N;the actuator has a highest speed of 1.259 mm/s at the driving voltage 150 V. © IMechE 2014.

关键词： Linear actuators

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A MICROFLUIDIC SYSTEM FOR ASSESSING FRYING OIL QUALITY

A MICROFLUIDIC SYSTEM FOR ASSESSING FRYING OIL QUALITY

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Mei Liu Shaorong Xie Ji Ge Zhensong Xu Zhizheng Wu Changhai Ru Yu Sun Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University 200072 China Department of Mechanical and Industrial Engineering University of Toronto Toronto ON M5S 3G8 Canada Jiangsu Provincial Key Laboratory of Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou 215123 China

ISBN: (纸本)9781509006090

This paper reports a microfluidic system for assessing frying oil degradation. The hydrodynamic focusing microfluidic device developed for this purpose generates water-in-oil droplets. By correlating droplet size and the widely accepted oil quality metric, the total polar compound (TPM), it was proven that the device is capable of evaluating frying oil degradation. Compared to existing bulky instruments and methods, this device and measurement technique are fast, simple, and cost-effective.

关键词： SYSTEM ASSESSING FRYING oils Frying oils oil degradation

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Output Feedback Control of a Robotic Exoskeleton with Input Deadzone via Neural Networks

Output Feedback Control of a Robotic Exoskeleton with Input ...

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IEEE International Conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Ziting Chen Wei He Yiting Dong Zhijun Li College of Automation Science and Engineering South China University of Technology Center for Robotics and School of Automation Engineering University of Electronic Science and Technology of China

ISBN: (纸本)9781479987313

In this paper, adaptive output feedback control via neural networks is designed for a robotic exoskeleton with unknown dynamics. Neural networks are used to compensate for the unknown deadzone effect induced by the actuators and the unknown dynamics of the robot. High-gain observer is employed to estimate the velocity information and then integrated in the design of output feedback controller. The deadzone effect is approximated by a Radial Basis Function Neural Network (RBFNN) and the tracking error for the deadzone effect is bounded and converging. The unknown dynamics of the robotic exoskeleton are estimated with another RBFNN. The proposed control is able to compensate for the estimated deadzone effect and track the desired trajectory. Finally, numerical simulation and experiment on a two-joint rigid exoskeleton demonstrate the effectiveness of the proposed method.

关键词： Neural network control Deadzone Adaptive control Robotic exoskeleton neural network control Output Feedback Control Neural network adaptive control Robotic Exoskeleton Radial basis function networks Unknown Output feedback

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