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Beauty and the beast: Optimal methods meet learning for drone racing

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arXiv 2018年

作者： Kaufmann, Elia Gehrig, Mathias Foehn, Philipp Ranftl, René Dosovitskiy, Alexey Koltun, Vladlen Scaramuzza, Davide Robotics and Perception Group Dep. of Informatics University of Zurich Dep. of Neuroinformatics University of Zurich ETH Zurich Intelligent Systems Lab Intel

Autonomous micro aerial vehicles still struggle with fast and agile maneuvers, dynamic environments, imperfect sensing, and state estimation drift. Autonomous drone racing brings these challenges to the fore. Human pilots can fly a previously unseen track after a handful of practice runs. In contrast, state-of-the-art autonomous navigation algorithms require either a precise metric map of the environment or a large amount of training data collected in the track of interest. To bridge this gap, we propose an approach that can fly a new track in a previously unseen environment without a precise map or expensive data collection. Our approach represents the global track layout with coarse gate locations, which can be easily estimated from a single demonstration flight. At test time, a convolutional network predicts the poses of the closest gates along with their uncertainty. These predictions are incorporated by an extended Kalman filter to maintain optimal maximum-aposteriori estimates of gate locations. This allows the framework to cope with misleading high-variance estimates that could stem from poor observability or lack of visible gates. Given the estimated gate poses, we use model predictive control to quickly and accurately navigate through the track. We conduct extensive experiments in the physical world, demonstrating agile and robust flight through complex and diverse previously-unseen race tracks. The presented approach was used to win the IROS 2018 Autonomous Drone Race Competition, outracing the second-placing team by a factor of two. Copyright © 2018, The Authors. All rights reserved.

关键词： Micro air vehicle (MAV)

Human-machine interface for remote training of robot tasks.

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arXiv 2018年

作者： Spranger, Jordi Buzatoiu, Roxana Polydoros, Athanasios Nalpantidis, Lazaros Boukas, Evangelos Robotics Vision and Machine Intelligence Lab. Materials and Production Dept. Aalborg University Copenhagen Denmark Intelligent and Interactive Systems Lab University of Innsbruck Innsbruck Austria

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Palmprint recognition

Real-time perception meets reactive motion generation

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IEEE robotics and Automation Letters 2018年第3期3卷 1864-1871页

作者： Kappler, Daniel Meier, Franziska Issac, Jan Mainprice, Jim Cifuentes, Cristina Garcia Wuthrich, Manuel Berenz, Vincent Schaal, Stefan Ratliff, Nathan Bohg, Jeannette Autonomous Motion Department MPI for Intelligent Systems Stuttgart70569 Germany Lula Robotics Inc. SeattleWA98102 United States CLMC Lab University of Southern California Los AngelesCA90007 United States Department of Computer Science and Engineering University of Washington SeattleWA98195 United States University of Stuttgart Stuttgart70174 Germany Department of Computer Science Stanford University StanfordCA94305 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: 1) a traditional sense-plan-act approach that is still widely used;2) a myopic controller that only reacts to local environment dynamics;and 3) 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. 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. © 2016 IEEE.

关键词： Memory architecture

4D Printing: 4D Printing of Continuous Shape Representation (Adv. Mater. Technol. 6/2021)

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Advanced Materials Technologies 2021年第6期6卷

作者： Sang-Joon Ahn Junghwan Byun Hyeong-Joon Joo Jae-Hak Jeong Dae-Young Lee Kyu-Jin Cho Biorobotics Lab Soft Robotics Research Center School of Mechanical Engineering/IAMD Institute of Engineering Research Seoul National University Seoul 08826 Republic of Korea Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart 70569 Germany Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea School of Engineering and Applied Sciences and Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA 01451 USA

Candybots: CANDYBOTS: A New Generation of 3D‐Printed Sugar‐Based Transient Small‐Scale Robots (Adv. Mater. 52/2020)

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Advanced Materials 2020年第52期32卷 2070388-2070388页

作者： Simone Gervasoni Anastasia Terzopoulou Carlos Franco Andrea Veciana Norman Pedrini Jan T. Burri Carmela de Marco Erdem C. Siringil Xiang‐Zhong Chen Bradley J. Nelson Josep Puigmartí‐Luis Salvador Pané Multi‐Scale Robotics Lab Institute of Robotics and Intelligent Systems ETH Zurich Tannenstrasse 3 Zurich CH‐8092 Switzerland Departament de Química Física Facultat de Química Universitat de Barcelona Martí i Franquès 1 Barcelona 08028 Spain Catalan Institution for Research and Advanced Studies Pg. Lluís Companys 23 Barcelona 08010 Spain

Have a CANDYBOT! Sugars are among the main sources of energy, and essential building blocks for all forms of life. In article number 2005652, Xiang‐Zhong Chen, Josep Puigmartí‐Luis, Salvador Pané, and co‐workers report the development of sugar‐based architectures via 3D printing. Because of their inherent degradability and biocompatibility characteristics, these structures have the potential to be applied in transient small‐scale robots.

关键词： 3D printing magnetic manipulation magnetic materials small‐scale robotics

Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

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Quantification of Extent of Muscle-skin Shifting by Traversa...

IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Shotaro Okajima Eduardo Iáñez Hiroshi Yamasaki Álvaro Costa García Fady S. Alnajjar Noriaki Hattori Shingo Shimoda Man-Machine Collaboration Research Team Robotics Project Baton Zone Program Science Technology and Innovation Hub RIKEN Nagoya Japan Brain-Machine Interface Systems Lab Miguel Hernández University of Elche Spain Intelligent Behavior Control Unit BTCC CBS RIKEN Nagoya Japan College of IT (CIT) UAE University (UAEU) Global Center for Medical Engineering and Informatics Osaka University

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

Surface electromyography(sEMG) measurement has been an essential approach to analyze human behaviors because we can generally consider that sEMG signals represent the muscle activities as the final output of our nerve system. One of the most serious problems for considering sEMG signal as the muscle activity is the shift of the relative position between muscles and skin depending on a posture. The motion of forearm rotation is the prominent example of muscle-skin shifting depending on postural changes. The sEMG signal from a sensor may represent the different muscle activity when the muscle-skin shifting is happened. In this study, we discuss a method to quantify the muscle-skin shift from the sEMG signals in response to the postural changes. We use the high density sEMG sensor that is possible to measure sEMG signal as the potential map. We proposed the computation algorithm to quantify the amount of muscle-skin shifting based on the change of the sEMG signals in response to the postural changes. We conducted the experiments of wrist extension motions under three different forearm postures: forearm pronation, natural posture and forearm supination. Experimental results from three healthy subjects show that we can quantify the extent of muscle-skin shifting as an angle by using proposed algorithm.

关键词：

Encoding longer-term contextual multi-modal information in a predictive coding model

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arXiv 2018年

作者： Zhong, Junpei Ogata, Tetsuya Cangelosi, Angelo Aomi 2-3-26 Tokyo Japan Centre for Robotics and Neural Systems Plymouth University Plymouth United Kingdom Lab for Intelligent Dynamics and Representation Waseda University Tokyo Japan

Studies suggest that within the hierarchical architecture, the topological higher level possibly represents a conscious category of the current sensory events with a slower changing activities. They attempt to predict the activities on the lower level by relaying the predicted information. On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the novel or surprising signal. We propose a predictive hierarchical artificial neural network model that examines this hypothesis on neurorobotic platforms, based on the AFA-PredNet model. In this neural network model, there are different temporal scales of predictions exist on different levels of the hierarchical predictive coding, which are defined in the temporal parameters in the neurons. Also, both the fastand the slow-changing neural activities are modulated by the active motor activities. A neurorobotic experiment based on the architecture was also conducted based on the data collected from the VRep simulator. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

Design, development and experimental assessment of a robotic end-effector for non-standard concrete applications

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Design, development and experimental assessment of a robotic...

2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Kumar, Nitish Hack, Norman Doerfler, Kathrin Walzer, Alexander Nikolas Rey, Gonzalo Javier Gramazio, Fabio Kohler, Matthias Daniel Buchli, Jonas The Agile & Dexterous Robotics Lab. The Institute of Robotics and Intelligent Systems ETH Zurich Switzerland The Institute of Technology in Architecture ETH Zurich Switzerland Moog Inc. United States

Despite the recent advances in, and the adoption of robotic technologies in the construction industry, the architectural processes which demand a high degree of geometric freedom still remain largely labour intensive and manual. This is due to the inherent difficulties in robotizing the current implementation of such processes coupled with the lack of alternate robotic technologies. A specific example, which is also the focus of this paper, is that of building a steel reinforced concrete structure, with varying curvature or cross-section. This process still remains rather manual and requires extensive support of customized form-work. In this paper, first we describe an alternate novel robotic fabrication process for building steel wire meshes which act as both reinforcement and formwork. The robotization of such a process is discussed with the use of a previously developed mobile robotic system. Based on the specifications derived from the process, design of a novel custom designed robotic end-effector, enabling this process, is detailed. Automation of the full robotic system comprising the mobile robotic system and the robotic end-effector is discussed from simulation to control. Through experimental evaluation of the robotic system, we demonstrate the ability to fully automate the construction of non-standard steel reinforced steel meshes of varying curvature and cell sizes. © 2017 IEEE.

关键词： Construction industry

REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color