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On the use of positive feedback for improved torque control

Control Theory and Technology 2015年第3期13卷 266-285页

作者： Houman DALLALI Gustavo A.MEDRANO-CERDA Michele FOCCHI Thiago BOAVENTURA Marco FRIGERIO Claudio SEMINI Jonas BUCHLI Darwin G.CALDWELL Department of Advanced Robotics Istituto Italiano di Tecnologia(IIT) Agile and Dexterous Robotics Lab Institute of Robotics and Intelligent SystemsETH

This paper considers the torque control problem for robots with flexible joints driven by electrical actuators. It is shown that the achievable closed-loop tracking bandwidth using PI torque controllers may be limited due to transmission zeros introduced by the load dynamics. This limitation is overcome by using positive feedback from the load motion in unison with PI torque controllers. The positive feedback is given in terms of load velocity, acceleration and jerk. Stability conditions for designing decentralized PI torque controllers are derived in terms of Routh-Hurwitz criteria. Disturbance rejection properties of the closed system are characterized and an analysis is carried out investigating the use of approximate positive feedback by omitting acceleration and/or jerk signals. The results of this paper are illustrated for a two DoF （degrees of freedom） system. Experimental results for a one DoF system are also included.

关键词： Force-torque control load motion compensation decentralized control

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A CHRONOLOGICAL REVIEW OF THE APPROACHES USED FORMULTI ROBOT NAVIGATION

A CHRONOLOGICAL REVIEW OF THE APPROACHES USED FORMULTI ROBOT...

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International Conference on Recent Trends in Engineering, Science & Technology

作者： Vishal Garg Ritu Tiwari Robotics and Intelligent Systems Lab Atal Bihari Vajpayee Indian Institute of Information Technology and Management

ISBN: (纸本)9781785617850;9781785617867

The paper provides a systematic overview and a critical discussion on the strategies that have been devised for robot navigation since 1970s. These approaches has been broadly classified into classical and heuristics approaches. Initially classical approach were the main focus of the researches. But because the navigation problem is an NP-complete problem so classical approaches were not that efficient [3]. This lead to a shift to heuristic approaches. The heuristic approaches does not guarantee the solution but when they do they are much efficient. Apart from this extensive amount of work done in this field, there is still a subclass i.e. Moving target environment, that is paid very less attention leading to a massive amount of future scope.

关键词： Robot motion Planning Heuristic algorithms Classical Approaches Target tracking Path planning Heuristic algorithms Target tracking Path planning Heuristics navigation Robot Nondeterministic polynomial-time hard Mobile robot navigation

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Optimal Quantization of Circular Distributions

Optimal Quantization of Circular Distributions

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International Conference on Information Fusion

作者： Igor Gilitschenski Gerhard Kurz Uwe D. Hanebeck Roland Siegwart Autonomous Systems Lab (ASL) Institute of Robotics and Intelligent Systems Swiss Federal Institute of Technology (ETH) Zurich Switzerland Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (纸本)9781509020126

In this work, a new method for approximating circular probability distributions by a mixture of weighted discrete samples is proposed. Particularly, the wrapped normal distribution, the von Mises distribution, and the Bingham distribution are considered. The approximation approach is based on formulating a quantizer and a global optimality measure, which can be optimized directly. Furthermore, a relationship between the Bingham distribution and the von Mises distribution are formulated showing that it is sufficient to approximate a von Mises distribution with suitably chosen parameters in order to obtain an optimal approximation of the Bingham distribution. The resulting approximation is of particular interest for filtering applications, because the involved optimality measure gives rise to a general error estimate in propagation of uncertainties through nontrivial functions in the circular domain.

关键词： Quantization (signal) Probability distribution Gaussian distribution Random variables Measurement uncertainty Distortion Robot sensing systems

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Real-time perception meets reactive motion generation

arXiv

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arXiv 2017年

作者： Kappler, Daniel Meier, Franziska Issac, Jan Mainprice, Jim Cifuentes, Cristina Garcia Wüthrich, Manuel Berenz, Vincent Schaal, Stefan Ratliff, Nathan Bohg, Jeannette Autonomous Motion Dept. at the MPI for Intelligent Systems Germany CLMC lab Univ. of Southern California United States Lula Robotics Inc. United States Univ. of Stuttgart Germany Dept. of Computer Science & Engineering Univ. of Washington United States Dept. of Computer Science Stanford Univ United States

We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with reactive motion generation methods in dynamic manipulation scenarios. We compare three different systems that are instantiations of the most common architectures in the field: (i) a traditional sense-plan-act approach that is still widely used, (ii) a myopic controller that only reacts to local environment dynamics and (iii) a reactive planner that integrates feedback control and motion optimization. All architectures rely on the same components for real-time perception and reactive motion generation to allow a quantitative evaluation. We extensively evaluate the systems on a real robotic platform in four scenarios that exhibit either a challenging workspace geometry or a dynamic environment. In 333 experiments, we quantify the robustness and accuracy that is due to integrating real-time feedback at different time scales in a reactive motion generation system. We also report on the lessons learned for system building. Copyright © 2017, The Authors. All rights reserved.

关键词： Feedback

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A Poisson-spectral model for modelling temporal patterns in human data observed by a robot

A Poisson-spectral model for modelling temporal patterns in ...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Ferdian Jovan Jeremy Wyatt Nick Hawes Tomas Krajnik Intelligent Robotics Lab School of Computer Science University of Birmingham United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln United Kingdom

The efficiency of autonomous robots depends on how well they understand their operating environment. While most of the traditional environment models focus on the spatial representation, long-term mobile robot operati... 详细信息

ISBN: (纸本)9781509037636

关键词： Robot human data Autonomous robots Operating Environment Human behavior basic model Environmental models Modeling Mobile robots

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen 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-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre 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. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider 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 assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Mineralization‐Inspired Synthesis of Magnetic Zeolitic Imidazole Framework Composites

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Angewandte Chemie 2019年第38期131卷

作者： Anastasia Terzopoulou Marcus Hoop Xiang‐Zhong Chen Ann M. Hirt Michalis Charilaou Yang Shen Fajer Mushtaq Angel Pérez del Pino Constantin Logofatu Laura Simonelli Andrew J. de Mello Christian J. Doonan Jordi Sort Bradley J. Nelson Salvador Pané Josep Puigmartí‐Luis Multi-Scale Robotics Lab (MSRL) Institute of Robotics and Intelligent Systems (IRIS) ETH Zurich Tannenstrasse 3 8092 Zurich Switzerland Institute of Geophysics ETH Zurich Sonnegstrasse 5 8092 Zurich Switzerland Laboratory of Metal Physics and Technology Department of Materials ETH Zurich Vladimir Prelog Weg 1 8093 Zurich Switzerland Institute of Food Nutrition and Health ETH Zurich Schmelzbergstrasse 7 8092 Zurich Switzerland Institute of Materials Science of Barcelona (ICMAB-CSIC) Campus UAB 08193 Bellaterra Spain National Institute for Materials Physics PO Box MG 7 77125 Bucharest Romania CELLS—ALBA Synchrotron Radiation Facility Carrer de la Llum 2–26 08290 Cerdanyola del Valles Barcelona Spain Institute of Chemical and Bioengineering ETH Zurich Vladimir Prelog Weg 1 8093 Zurich Switzerland School of Chemistry and Physics University of Adelaide Adelaide South Australia 5005 Australia Institució Catalana de Recerca i Estudis Avançats (ICREA) and Departament de Física Universitat Autònoma de Barcelona 08193 Bellaterra Spain

Metal–organic frameworks (MOFs) capable of mobility and manipulation are attractive materials for potential applications in targeted drug delivery, catalysis, and small‐scale machines. One way of rendering MOFs navigable is incorporating magnetically responsive nanostructures, which usually involve at least two preparation steps: the growth of the magnetic nanomaterial and its incorporation during the synthesis of the MOF crystals. Now, by using optimal combinations of salts and ligands, zeolitic imidazolate framework composite structures with ferrimagnetic behavior can be readily obtained via a one‐step synthetic procedure, that is, without the incorporation of extrinsic magnetic components. The ferrimagnetism of the composite originates from binary oxides of iron and transition metals such as cobalt. This approach exhibits similarities to the natural mineralization of iron oxide species, as is observed in ores and in biomineralization.

关键词： Biomedizinische Anwendungen Magnetismus Metall-organische Gerüste Zeolith-Imidazolat-Gerüste

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Sampled-data robust feedback linearization using estimator

Sampled-data robust feedback linearization using estimator

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International Workshop on Advanced Motion Control (AMC)

作者： Asim Zaheer Yasar Ayaz Momena Hasan Muhammad Salman Robotics and Intelligent Systems Engineering Lab National University of Sciences and Technology Islamabad Pakistan Department of Electrical Engineering National University of Sciences and Technology Islamabad Pakistan

In this paper, robust control schemes are presented to achieve sampled-data output feedback tracking, for the cases of unknown and known nonlinear minimum phase second order plant (system) models. For known system model case, system output tracks reference trajectory using Extended Kalman Filter (EKF), Unscented Kalman filter (UKF), and Cubature Kalman Filter (CKF). Whereas, for unknown system model case; EKF, UKF and CKF cannot be utilized. For this case, in this paper; State-Space Recursive Least Squares (SSRLS) and Sliding Mode observer (SMO) are employed. SSRLS uses constant velocity model, whereas, SMO requires information about input function only, to track the reference signal. Emulation Design based discrete feedback linearization controller utilizes estimated states to generate control input for plant. The robustness of these sampled-data output feedback control schemes (using estimators) against disturbance and parameter perturbation is demonstrated. It is presented via simulations for magnetic levitation system, that robust tracking is achieved on using estimators (Kalman filters and SMO) in sampled-data output feedback configuration as compared to performing tracking using sampled-data state feedback scheme. Simulation results show that SMO based output feedback tracking is most robust, followed by CKF and EKF based output feedback scheme. UKF based output feedback scheme is robust against external disturbance force, but for case of system parameter perturbation, UKF tracking error takes longer time to converge. SSRLS based scheme behaves poorly in presence of external disturbance force, as SSRLS estimation is based on constant velocity model and not on actual nonlinear system model.

关键词： Output feedback State feedback Kalman filters Force Emulation Robustness Magnetic levitation

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Magnetoelectrics: Hybrid Magnetoelectric Nanowires for Nanorobotic Applications: Fabrication, Magnetoelectric Coupling, and Magnetically Assisted In Vitro Targeted Drug Delivery (Adv. Mater. 8/2017)

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Advanced Materials 2017年第8期29卷

作者： Xiang‐Zhong Chen Marcus Hoop Naveen Shamsudhin Tianyun Huang Berna Özkale Qian Li Erdem Siringil Fajer Mushtaq Luca Di Tizio Bradley J. Nelson Salvador Pané Multi‐Scale Robotics Lab (MSRL) Institute of Robotics & Intelligent Systems (IRIS) ETH Zurich Zurich 8092 Switzerland Center for Nanophase Materials Sciences and Institute for Functional Imaging of Materials Oak Ridge National Laboratory Oak Ridge TN 37831 USA

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Towards robust online inverse dynamics learning

Towards robust online inverse dynamics learning

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Franziska Meier Daniel Kappler Nathan Ratliff Stefan Schaal CLMC Lab University of Southern California Los Angeles USA Autonomous Motion Department MPI for Intelligent Systems Tubingen Germany Lula Robotics Inc. United States

ISBN: (纸本)9781509037636

Learning of inverse dynamics modeling errors is key for compliant or force control when analytical models are only rough approximations. Thus, designing real time capable function approximation algorithms has been a necessary focus towards the goal of online model learning. However, because these approaches learn a mapping from actual state and acceleration to torque, good tracking is required to observe data points on the desired path. Recently it has been shown how online gradient descent on a simple modeling error offset term to minimize tracking at acceleration level can address this issue. However, to adapt to larger errors a high learning rate of the online learner is required, resulting in reduced compliancy. Thus, here we propose to combine both approaches: The online adapted offset term ensures good tracking such that a nonlinear function approximator is able to learn an error model on the desired trajectory. This, in turn, reduces the load on the adaptive feedback, enabling it to use a lower learning rate. Combined this creates a controller with variable feedback and low gains, and a feedforward model that can account for larger modeling errors. We demonstrate the effectiveness of this framework, in simulation and on a real system.

关键词： inverse kinematics error model on-line model Force control learning rate acceleration ANALYSIS MODELS tracking Learning Controllers

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