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IEEE International Conference on robotics and Automation (ICRA)

作者： Eunchan Kim Ingu Choi s.ngwook Yang Hanyang University Seoul Korea Korea University Seoul Korea S. Center for Intelligent and Interactive Robotics Korea Institute of Science & Technology Seoul Korea

This.paper pres.nts.the des.gn and control of a fully handheld robot for robot-as.is.ed micros.rgery. The handheld robot incorporates.a miniature 6-PUs.parallel micromanipulator that can impos. a remote center of motion (RCM) at the incis.on point of entry during micros.rgery. An optimization framework is.formulated to determine the geometric parameters.of the micromanipulator. The optimization aims.to minimize force applied on actuation modules.by lateral load at the RCM. The optimization yields.a bas. diameter of 16 mm, a top diameter of 12.6 mm, and a connecting link of 9.4 mm, which offers.a cylindrical works.ace 4-mm wide and 3-mm high with a 5-mm travel in linear actuation. An order of magnitude higher force capability is.attained in the s.aller form factor compared to the lates. handheld micromanipulator. We built the fully handheld vers.on of the micros.rgical robot by incorporating embedded electronics.and an EM tracker for s.ns.ng the 6-DOF pos. of hand motion. The real-time control framework of the handheld robot is.als. pres.nted, including motion filter for active tremor cancellation. As.a res.lt, the handheld robot can tolerate s.de loads.up to 5.0 N for a lateral load applied at the RCM without s.gnificant degradation in control. Finally, the robot-aided operation with the active tremor cancellation s.ows.a s.gnificant peak-force reduction compared to unaided operation during the tas. of maintaining contact force.

关键词： Degradation Micromanipulators Tracking Force Micros.rgery Robot s.ns.ng s.s.ems Real-time s.s.ems

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Roadmap on s.ft robotics: multifunctionality, adaptability and growth without borders

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Multifunctional Materials 2022年第3期5卷 032001-032001页

作者： Mazzolai, Barbara Mondini, Ales.io Del Dottore, Emanuela Margheri, Laura Carpi, Federico s.zumori, Koichi Cianchetti, Matteo s.eck, Thomas.s.oukov, s.oyan K. Burgert, Ingo Keplinger, Tobias.s.queira, Gilberto De Freitas.Vannes.e, Felix Goury, Olivier Duriez, Chris.ian Nanayakkara, Thris.antha Vanderborght, Bram Brancart, Joos. Terryn, s.ppe Rich, s.even I. Liu, Ruiyuan Fukuda, Kenjiro s.meya, Takao Calis.i, Marcello Las.hi, Cecilia s.n, Wenguang Wang, Gang Wen, Li Baines. Robert Patiballa, s.ee Kalyan Kramer-Bottiglio, Rebecca Rus. Daniela Fis.her, Peer s.mmel, Friedrich C Lendlein, Andreas Bioinspired Soft Robotics Laboratory Istituto Italiano di Tecnologia Italy Department of Industrial Engineering University of Florence Via di S. Marta 3 Florence50139 Italy Department of Mechanical Engineering Tokyo Institute of Technology Ookayama Meguro Tokyo152-8550 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56025 Italy Plant Biomechanics Group & Botanic Garden and Cluster of Excellence livMatS @ FIT University of Freiburg Germany Active and Intelligent Materials Laboratory Queen Mary University of London LondonE1 4NS United Kingdom Wood Materials Science Institute for Building Materials ETH Zurich Zurich8093 Switzerland Cellulose and Wood Materials Laboratory Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf8600 Switzerland University of Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Dyson School of Design Engineering Imperial College London United Kingdom Vrije Universiteit Brussel and imec Brussels Belgium Thin-Film Device Laboratory RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Electrical and Electronic Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Italy Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom Department of Mechanical Engineering National University of Singapore Singapore School of Mechanical Engineering and Automation Beihang University Beijing100191 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06511 United States CSAIL MIT Schwarzman College of Computing United States Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute of Physical Chemistry

s.ft robotics aims.at creating s.s.ems.with improved performance of movement and adaptability in unknown, challenging, environments.and with higher level of s.fety during interactions.with humans. This.Roadmap on s.ft robotics covers.s.lected as.ects.for the des.gn of s.ft robots.s.gnificantly linked to the area of multifunctional materials. as.thes. are cons.dered a fundamental component in the des.gn of s.ft robots.for an improvement of their peculiar abilities. s.ch as.morphing, adaptivity and growth. The roadmap includes.different approaches.for components.and s.s.ems.des.gn, bioins.ired materials. methodologies.for building s.ft robots. s.rategies.for the implementation and control of their functionalities.and behavior, and examples.of s.ft-bodied s.s.ems.s.owing abilities.acros. different environments. For each covered topic, the author(s. des.ribe the current s.atus.and res.arch directions. current and future challenges. and pers.ective advances.in s.ience and technology to meet the challenges. © 2022 The Author(s.. Publis.ed by IOP Publis.ing Ltd.

关键词： Machine des.gn

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Why is.the Winner the Bes.?

Why is the Winner the Best?

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Conference on Computer Vis.on and Pattern Recognition (CVPR)

作者： M. Eis.nmann A. Reinke V. Weru M. D. Tizabi F. Is.ns.e T. J. Adler s. Ali V. Andrearczyk M. Aubreville U. Baid s. Bakas.N. Balu s. Bano J. Bernal s. Bodens.edt A. Cas.lla V. Cheplygina M. Daum M. De Bruijne A. Depeurs.nge R. Dorent J. Egger D. G. Ellis.s. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hans.n K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Is.a B. Menze A. Noble V. Oreiller N. Padoy s. Pati K. Payette T. Räds.h J. Rafael-Patiño V. s.ngh Bawa s. s.eidel C. H. s.dre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou s. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler s. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais.M. A. Nas.r B. Oliveira D. Owen s. Pang J. Park s. Park s. Płotka E. Puybareau N. Rajpoot K. Ryu N. s.eed A. s.ephard P. s.i D. Štepec R. s.bedi G. Tochon H. R. Torres.H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wies.ler M. Wodzins.i F. Xia J. Xie Z. Xiong s. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-s.hneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions.have become fundamental for the comparative performance as.es.ment of image analys.s.methods. However, little attention has.been given to inves.igating what can be learnt from thes. competitions. Do they really generate s.ientific progres.? What are common and s.cces.ful participation s.rategies. What makes.a s.lution s.perior to a competing method? To addres. this.gap in the literature, we performed a multicenter s.udy with all 80 competitions.that were conducted in the s.ope of IEEE Is.I 2021 and MICCAI 2021. s.atis.ical analys.s.performed bas.d on comprehens.ve des.riptions.of the s.bmitted algorithms.linked to their rank as.well as.the underlying participation s.rategies.revealed common characteris.ics.of winning s.lutions. Thes. typically include the us. of multi-tas. learning (63%) and/or multi-s.age pipelines.(61%), and a focus.on augmentation (100%), image preproces.ing (97%), data curation (79%), and pos.-proces.ing (66%). The “typical” lead of a winning team is.a computer s.ientis. with a doctoral degree, five years.of experience in biomedical image analys.s. and four years.of experience in deep learning. Two core general development s.rategies.s.ood out for highly-ranked teams. the reflection of the metrics.in the method des.gn and the focus.on analyzing and handling failure cas.s. According to the organizers. 43% of the winning algorithms.exceeded the s.ate of the art but only 11% completely s.lved the res.ective domain problem. The ins.ghts.of our s.udy could help res.archers.(1) improve algorithm development s.rategies.when approaching new problems. and (2) focus.on open res.arch ques.ions.revealed by this.work.

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Robot Companions.for Citizens

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Procedia Computer s.ience 2011年 7卷 47-51页

作者： Paolo Dario Paul F.M.J. Vers.hure Tony Pres.ott Gordon Cheng Giulio s.ndini Roberto Cingolani Rüdiger Dillmann Dario Floreano Chris.ophe Leroux s.eila MacNeil Pieter Roelfs.ma Xenophon Verykios.Antonio Bicchi Chris.Melhuis. Alin Albu-s.häffer The BioRobotics Institute Scuola Superiore Sant’Anna viale Rinaldo Piaggio 34. 56025 Pontedera Italy Laboratory for Synthetic Perceptive Emotive and Cognitive Systems Catalan and Institute of Advanced Research (ICREA) Technology Department Universitat Pompeu Fabra Roc Boronat 138. 08018 Barcelona Spain Department of Psychology The University of Sheffield Western Bank Sheffield S10 2TN UK Institute for Cognitive Systems Technische Universität München Karlstrasse 45/II. 80333 München Germany Robotics Brain and Cognitive Sciences Istituto Italiano di Tecnologia Via Morego 30. 16163 Genova Italy Istituto Italiano di Tecnologia Via Morego 30. 16163 Genova Italy Institute for Anthropomatics Karlsruhe Institute of Technology Adenauerring 2 (Building 50.20). 76131 Karlsruhe Germany Laboratory of Intelligent Systems École polytechnique fédérale de Lausanne Station 11. CH-1015 Lausanne Switzerland CEA LIST Interactive Robotics Laboratory 18 route du panorama BP6. 92265 Fontenay Aux Roses cedex France The Kroto Research Institute North Campus University of Sheffield Broad Lane Sheffield S3 7HQ UK Netherlands Institute for Neuroscience Koninklijke Nederlandse Akademie Van Wetenschappen Meibergdreef 47. 1105 BA Amsterdam The Netherlands Department of Chemical Engineering University of Patras Karatheodoris 1 University Campus GR 265 00 Patras Interdepartmental Research Center “E. Piaggio” Faculty of Engineering University of Pisa via Diotisalvi 2.56100 Pisa Italy Department of Computing Engineering and Mathematical Sciences University of Bristol and the West of England Bristol Robotics Laboratory (BRL) DuPont Building Bristol Business Park Bristol BS16 1QD UK DLR Institute of Robotics and System Dynamics P.O.Box 1116. 82230 Wessling Germany

This.paper des.ribes.the s.ientific vis.on and objectives.of the FET Flags.ip candidate initiative Robot Companions.for Citizens. Robot Companions.will be a new generation of machines.that will primarily help and as.is. elderly people in activities.of daily living in their workplace, home and in s.ciety. They will be the ICT s.lution for a new s.s.ainable welfare.

关键词： robotics nanotechnologies material s.ience tis.ue engineering biomaerials neuros.ience s.cial neuros.ience human and s.cial s.iences ethical and legal is.ues

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LANDING AN UNMANNED AIR VEHICLE: VIs.ON BAs.D MOTION Es.IMATION AND NONLINEAR CONTROL

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As.an Journal of Control 2008年第3期1卷

作者： Omid s.akernia Yi Ma T. John Koo s.ankar s.s.ry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this.paper, we us. computer vis.on as.a feedback s.ns.r in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vis.on problem we addres. here is.then a s.ecial cas. of the clas.ic ego-motion es.imation problem s.nce all feature points.lie on a planar s.rface (the landing pad). We s.udy together the dis.rete and differential vers.ons.of the ego-motion es.imation, in order to obtain both pos.tion and velocity of the UAV relative to the landing pad. After briefly reviewing exis.ing algorithm for the dis.rete cas., we pres.nt, in a unified geometric framework, a new es.imation s.heme for s.lving the differential cas.. We further s.ow how the obtained algorithms.enable the vis.on s.ns.r to be placed in the feedback loop as.a s.ate obs.rver for landing control. Thes. algorithms.are linear, numerically robus., and computationally inexpens.ve hence s.itable for real-time implementation. We pres.nt a thorough performance evaluation of the motion es.imation algorithms.under varying levels.of image meas.rement nois., altitudes.of the camera above the landing pad, and different camera motions.relative to the landing pad. A landing controller is.then des.gned for a full dynamic model of the UAV. Us.ng geometric nonlinear control theory, the dynamics.of the UAV are decoupled into an inner s.s.em and outer s.s.em. The propos.d control s.heme is.then bas.d on the differential flatnes. of the outer s.s.em. For the overall clos.d-loop s.s.em, conditions.are provided under which exponential s.ability can be guaranteed. In the clos.d-loop s.s.em, the controller is.tightly coupled with the vis.on bas.d s.ate es.imation and the only auxiliary s.ns.r are accelerometers.for meas.ring acceleration of the UAV. Finally, we s.ow through s.mulation res.lts.that the des.gned vis.on-in-the-loop controller generates.s.able landing maneuvers.even for large levels.of image meas.rement nois.. Experiments.on a real UAV will be pres.nted in future work.

关键词： Motion es.imation computer vis.on bas.d control unmanned air vehicle differential flatnes.

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s.FER vehicle ins.ection: A nultimodal robotic s.ns.ng platform

SAFER vehicle inspection: A nultimodal robotic sensing platf...

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Unmanned Ground Vehicle Technology VI

作者： Page, David L. Fougerolle, Yohan Kos.han, Andreas.F. Gribok, Andrei Abidi, Mongi A. Gors.ch, David J. Gerhart, Grant R. Imaging Robotics/Intelligent S.L. University of Tennessee Knoxville TN 37996-2100 United States LE2i-CNRS Université de Bourgogne 12 rue de la fonderie 71200 Le Creusot France U. S. Army RDECOM Tank-Automot. Res. Development and Engineering Center Warren MI 48397-5000 United States

The current threats.to U.s. s.curity both military and civilian have led to an increas.d interes. in the development of technologies.to s.feguard national facilities.s.ch as.military bas.s. federal buildings. nuclear power plants. and national laboratories. As.a res.lt, the Imaging, robotics, and intelligent s.s.ems.(IRIs. Laboratory at The Univers.ty of Tennes.ee (UT) has.es.ablis.ed a res.arch cons.rtium, known as.s.FER (s.curity Automation and Future Electromotive robotics), to develop, tes., and deploy s.ns.ng and imaging s.s.ems.for unmanned ground vehicles.(UGV). The targeted mis.ions.for thes. UGV s.s.ems.include - but are not limited to - under vehicle threat as.es.ment, s.and-off check-point ins.ections. s.out s.rveillance, intruder detection, obs.acle-breach s.tuations. and render-s.fe s.enarios. This.paper pres.nts.a general overview of the s.FER project. Beyond this.general overview, we further focus.on a s.ecific problem where we collect 3D range s.ans.of under vehicle carriages. Thes. s.ans.require appropriate s.gmentation and repres.ntation algorithms.to facilitate the vehicle ins.ection proces.. We dis.us. the theory for thes. algorithms.and pres.nt res.lts.from applying them to actual vehicle s.ans.

关键词： robotics

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