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Aerial navigation in obstructed environments with embedded nonlinear model predictive control

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arXiv 2018年

作者： Small, Elias Sopasakis, Pantelis Fresk, Emil Patrinos, Panagiotis Nikolakopoulos, George University of Cyprus Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks 1 Panepistimiou Avenue Aglantzia Nicosia2109 Cyprus Robotics Team at Luleå Technical University LuleåSE-97187 Sweden KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20 Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV’s battery, by estimating the thrust *** Codes 49M37, 34H05, 93B52 Copyright © 2018, The Authors. All rights reserved.

关键词： Micro air vehicle (MAV)

An underwater image enhancement benchmark dataset and beyond

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arXiv 2019年

作者： Li, Chongyi Guo, Chunle Ren, Wenqi Cong, Runmin Hou, Junhui Kwong, Sam Tao, Dacheng Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China School of Electrical and Information Engineering Tianjin University Tianjin China Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://***/proj_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

Mobile Robot Localisation and Navigation Using LEGO NXT and Ultrasonic Sensor

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Mobile Robot Localisation and Navigation Using LEGO NXT and ...

IEEE International Conference on robotics and Biomimetics

作者： Yanan Liu Rui Fan Bin Yu M. Junaid Bocus Ming Liu Hepeng Ni Jiahe Fan Shixin Mao Bristol Robotics Laboratory University of Bristol Bristol United Kingdom Hong Kong University of Science and Technology Kowloon HK Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Bristol Bristol Bristol GB Department of Electrical and Electronic Engineering University of Bristol Bristol United Kingdom School of Mechanical Engineering Shandong University Jinan China School of Communication Engineering Jilin University Changchun China Shenzhen Sanhang Industrial Technology Research Institute Shenzhen China

ISBN: (纸本)9781728103785;9781728103778

Mobile robots are becoming increasingly important both for individuals and industries. Mobile robotic technology is not only utilised by experts in this field but is also very popular among amateurs. However, implementing a mobile robot to perform tasks autonomously can be expensive because of the need for various types of sensors and the high price of robot platforms. Hence, in this paper we present a mobile robot localisation and navigation system which uses a LEGO ultrasonic sensor in an indoor map based on the LEGO MINDSTORM NXT. This provides an affordable and ready-to-use option for most robot fans. In this paper, an effective method is proposed to extract useful information from the distorted readings collected by the ultrasonic sensor. Then, the particle filter is used to localise the robot. After robot's position is estimated, a sampling-based path planning method is proposed for the robot navigation. This method reduces the robot accumulative motion error by minimising robot turning times and covering distances. The robot localisation and navigation algorithms are implemented in MATLAB. Simulation results show an average accuracy between 1 and 3 cm for three different indoor map locations. Furthermore, experiments performed in a real setup show the effectiveness of the proposed methods.

关键词： Mobile robot LEGO NXT Localisation and navigation Particle filter Path plan

Field‐Effect Transistors: Low‐Symmetry PdSe2 for High Performance Thermoelectric Applications (Adv. Funct. Mater. 52/2020)

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Advanced Functional Materials 2020年第52期30卷 2070347-2070347页

作者： Yunshan Zhao Peng Yu Gang Zhang Minglei Sun Dongzhi Chi Kedar Hippalgaonkar John T. L. Thong Jing Wu NNU‐SULI Thermal Energy Research Center (NSTER) & Center for Quantum Transport and Thermal Energy Science (CQTES) School of Physics and Technology Nanjing Normal University Nanjing 210023 China Institute of Materials Research and Engineering A*STAR (Agency for Science Technology and Research) 2 Fusionopolis Way Innovis #08‐03 Singapore 138634 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore School of Materials Science and Engineering State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat‐Sen University Guangzhou 510275 P. R. China Institute of High Performance Computing Singapore 138632 Singapore Department of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore

In article number 2004896, John T. L. Thong, Jing Wu, and co‐workers investigate thermoelectric transport in novel 2D palladium diselenide (PdSe2) with a low‐symmetry pentagonal lattice. Due to its sensitive dependence on the interlayer coupling originating from the special lattice structure, the thermoelectric performance can be largely enhanced. The high band convergence and quantum confinement through thickness engineering advance and broaden the applications of thermoelectric such as in soft robotics, health monitoring, and human‐machine interfaces.

关键词： 2D field‐effect transistors palladium diselenide pentagonal crystals thermoelectric devices

Surgical data science-from concepts toward clinical translation

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arXiv 2020年

作者： Maier-Hein, Lena Eisenmann, Matthias Sarikaya, Duygu März, Keno Collins, Toby Malpani, Anand Fallert, Johannes Feussner, Hubertus Giannarou, Stamatia Mascagni, Pietro Nakawala, Hirenkumar Park, Adrian Pugh, Carla Stoyanov, Danail Vedula, Swaroop S. Cleary, Kevin Fichtinger, Gabor Forestier, Germain Gibaud, Bernard Grantcharov, Teodor Hashizume, Makoto Heckmann-Nötzel, Doreen Kenngott, Hannes G. Kikinis, Ron Mündermann, Lars Navab, Nassir Onogur, Sinan Sznitman, Raphael Taylor, Russell H. Tizabi, Minu D. Wagner, Martin Hager, Gregory D. Neumuth, Thomas Padoy, Nicolas Collins, Justin Gockel, Ines Goedeke, Jan Hashimoto, Daniel A. Joyeux, Luc Lam, Kyle Leff, Daniel R. Madani, Amin Marcus, Hani J. Meireles, Ozanan Seitel, Alexander Teber, Dogu Ückert, Frank Müller-Stich, Beat P. Jannin, Pierre Speidel, Stefanie Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey LTSI Inserm UMR 1099 University of Rennes 1 Rennes France IRCAD Strasbourg France The Malone Center for Engineering in Healthcare The Johns Hopkins University BaltimoreMD United States KARL STORZ SE and Co. KG Tuttlingen Germany Department of Surgery Klinikum Rechts der Isar Technical University of Munich Munich Germany The Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom ICube University of Strasbourg CNRS France IHU Strasbourg Strasbourg France Altair Robotics Lab University of Verona Verona Italy Department of Surgery Anne Arundel Health System AnnapolisMD United States Johns Hopkins University School of Medicine BaltimoreMD United States Department of Surgery Stanford University School of Medicine StanfordCA United States Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany The Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital WashingtonDC United States The Perk Lab Queen's University KingstonON Canada University of Haute-Alsace Mulhouse France Faculty of Information Technology Monash University ClaytonVIC Australia University of Toronto TorontoON Canada The Li Ka Shing Knowledge Institute of St. Michael's Hospital TorontoON Canada Kyushu University Fukuoka Japan Kitakyushu Koga Hospital Fukuoka Japan Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Computer Science The Johns Hopkins University Baltim

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process. © 2020, CC BY.

关键词： Deep learning

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Editorial

International Journal of Social robotics 2016年第1期8卷 1-3页

作者： Ge, Shuzhi Sam Khatib, Oussama and the Robotics Research Lab Department of Electrical & Computer Engineering The National University of Singapore Singapore Singapore University of Electronic Science and Technology of China Chengdu China Department of Computer Science Artificial Intelligence Laboratory Stanford University StanfordCA United States

On active decoupling control for horizontal-lateral direction of hypersonic flight based on HIFOO 21

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On active decoupling control for horizontal-lateral directio...

21st AIAA International Space Planes and Hypersonics Technologies Conference, Hypersonics 2017

作者： Ma, Shunjian Zhu, Kai Sun, Mingwei Wang, Zenghui Chen, Zengqiang Dept. of Automation and Intelligent Science Nankai University College of Computer and Control Engineering Tianjin300350 China Beijing Aerospace Technology Institute Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Beijing Aerospace Technology Institute Beijing100074 China Department of Electrical and Mining Engineering University of South Africa FL1710 South Africa

ISBN: (数字)9781624104633

ISBN: (纸本)9781624104633

Nowadays, hypersonic flight vehicles are widely studied. Since their high nonlinearity, strong coupling and uncertain factors, the conventional three-channel independent control design method is often unable to work well. In this paper, an active disturbance rejection controller (ADRC) is utilized to cancel internal uncertainties and exogenous disturbances for longitudinal dynamics. Then, the MIMO robust controller is obtained by choosing proper sensitivity functions and weighting functions to reduce horizontal-lateral direction coupling. The conventional H robust controllor is high-order, which is difficult for practitioners to implement. With frequency transformation, both ADRC for the longitudinal dynamics and MIMO robust controller for the horizontal-lateral dynamics are reformed into the static output feedback structure to reduce the high order of controller. Finally, all parameters in the controllers can be optimized by HIFOO (H-Infinity Fixed Order Optimization). The Winged-Cone, a 6-freedom NASA hypersonic vehicle model, is used as a benchmark, and Monte Carlo simulations demonstrate the effectiveness of the proposed method. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Controllers

Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： Vision

Development of a swarm UAV simulator integrating realistic motion control models for disaster operations

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arXiv 2017年

作者： Siddiqui, Kazi Tanvir Ahmed Feil-Seifer, David Jiang, Tianyi Jose, Sonu Liu, Siming Louis, Sushil Robotics Research Laboratory Computer Science and Engineering Department University of Nevada Reno Reno Nevada89557 United States Evolutionary Computing Systems Lab Computer Science and Engineering Department University of Nevada Reno Reno Nevada89557 United States

Simulation environments for Unmanned Aerial Vehicles (UAVs) can be very useful for prototyping user interfaces and training personnel that will operate UAVs in the real world. The realistic operation of such simulations will only enhance the value of such training. In this paper, we present the integration of a model-based waypoint navigation controller into the Reno Rescue Simulator for the purposes of providing a more realistic user interface in simulated environments. We also present potential uses for such simulations, even for real-world operation of UAVs. Copyright © 2017, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)