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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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Research trends in combinatorial optimisation

arXiv

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arXiv 2020年

作者： Weinand, Jann Michael Sörensen, Kenneth Segundo, Pablo San Kleinebrahm, Max McKenna, Russell Chair of Energy Economics Institute for Industrial Production Karlsruhe Institute of Technology Germany Department of Engineering Management University of Antwerp Antwerp2000 Belgium Universidad Politécnica de Madrid Centre for Automation and Robotics Madrid Spain Chair in Energy Transition School of Engineering University of Aberdeen United Kingdom

Real-world problems are becoming highly complex and, therefore, have to be solved with combinatorial optimisation (CO) techniques. Motivated by the strong increase of publications on CO, 8,393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organisations and authors as well as their collaborations, the most relevant CO problems, solution methods and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem-oriented studies deal particularly with real-world applications within the energy sector, production sector or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area. © 2020, CC BY.

关键词： Genetic algorithms

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Depth selection for deep ReLU nets in feature extraction and generalization

arXiv

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arXiv 2020年

作者： Han, Zhi Yu, Siquan Lin, Shao-Bo Zhou, Ding-Xuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Northeastern University Shenyang China Center of Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University Xi'an China School of Data Science Department of Mathematics City University of Hong Kong Hong Kong

Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantage of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the most important challenge of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

arXiv

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arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

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Development of a Tetris playing robot controlled by KNR 15

Development of a Tetris playing robot controlled by KNR

引用

15th IEEE International Conference on Mechatronics and automation, ICMA 2018

作者： Wang, Ming Zhang, Yanlu Dong, Huifang Li, Xu Yu, Junzhi Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan250100 China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538660720

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system. © 2018 IEEE.

关键词： Controllers

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Corrigendum to “Smart implements by leveraging ISOBUS: Development and evaluation of field applications” [Smart Agricultural Technology 6 (2023) 1-10/100341]

引用

Smart Agricultural Technology 2023年 6卷

作者： Galibjon M. Sharipov Andreas Heiß Trim Bresilla Ard T. Nieuwenhuizen Jochen Hemming Frits K. van Evert Suzanne Baron Amar Benrais Ioannis Avgoustakis Nikos Mylonas Spyros Fountas Panagiotis Vasilaros Panagiotis Karagiannis Josep Vidal Dimitrios S. Paraforos University of Hohenheim Institute of Agricultural Engineering Technology in Crop Production Garbenstr. 9 D-70599 Stuttgart Germany Geisenheim University Department of Agricultural Engineering Von-Lade-Str 1 D-65366 Geisenheim Germany Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Agricultural Machinery and Technology 100000 Qori Niyoziy 39 Tashkent Uzbekistan Wageningen Plant Research Wageningen University and Research P.O. Box 16 Wageningen 6700 AA the Netherlands AgreenCulture 20 avenue Didier Daurat 31400 Toulouse France Department of Natural Resources Management and Agricultural Engineering Agricultural University of Athens 11855 Athens Greece Laboratory for Manufacturing Systems & Automation University of Patras 26504 Rio Patras Greece Teyme Tecnología Agrícola Pol. Ind. Sau 3 Calle Girona 3 25131 Torre-Serona Lleida Spain

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Robots in Crisis management: A Survey 4th

Robots in Crisis Management: A Survey

引用

4th International Conference on Information Systems for Crisis Response and management in Mediterranean Countries, ISCRAM-med 2017

作者： Kostavelis, Ioannis Gasteratos, Antonios Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace Vas. Sophias 12 Xanthi671 32 Greece

ISBN: (纸本)9783319676326

Robots are flexible intelligent machines designed to replace human operators in dangerous, dirty, and dull tasks. Therefore, an apparent application of robotics is to provide assistance in managing critical situations, by carrying out tasks that might be harmful for us. Indeed, almost any police corps around the globe is nowadays equipped with a a counter improvise explosive device (IED) robot. Although counter-IED robots constitute the most famous robots for crisis management, they formulate just one of the areas that robots can be used for. An abundance of research has been performed through the last ten years in safety, security and rescue robotics. This paper attempts to review the efforts reported so far. Moreover, it attempts to systemize the robots for crisis management, in the sense that it categorizes them according to the specific use and examines which architecture would be most suitable for each case. © Springer International Publishing AG 2017.

关键词： Explosives

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PROGRAMS project approach to maintenance management

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IFAC-PapersOnLine 2020年第3期53卷 313-318页

作者： M. Surico R. Ricatto A. Merlo I. Németh A. Sardelis M. Villoslada E. Montejo N. Frenkel P. Aivaliotis I.M. de la Pera Celada J. Sidiropoulos A. Eytan A. Papavasileiou F. Aggogeri FIDIA S.p.A. Corso Lombardia 11 San Mauro Torinese Italy CeSI Centro Studi Industriali via Tintoretto 10 Cologno Monzese (MI) 20093 Italy Department of Manufacturing Science and Engineering Budapest University of Technology and Economics 1111 Budapest Műegyetem rkp. 3 Hungary CASP S.A. 64 Michalakopoulou str Athens 11528 Greece Savvy Data Systems S.L. San Sebastian Gipuzkoa 20018 Spain Ideko S.Coop Arriaga Kalea 2 20870 Elgoibar Gipuzkoa Spain Laboratory for Machine Tools and Production Engineering (WZL) Campus-Boulevard 30 52074 Aachen Germany Laboratory for Manufacturing Systems & Automation University of Patras Rion Patras 26504 Greece Aurrenak S. Coop Bitorialanda Kalea 15 01010 Vitoria-Gasteiz Araba Spagna Grobotics Gizelis Robotics Kormatzini Area Schimatari Viotias 32009 Greece We Plus S.p.A Strada Torino 43 10043 Orbassano (TO) Italy Calpak-Cicero Hellas S.A. 9 Sygrou Ave. 11743 Athens Greece Department of Mechanical and Industrial Engineering University of Brescia via Branze 38 25123 Brescia Italy

Maintenance management is a vital part of the business of a production company. It contributes to determining the long-term success of the company because poorly maintained resources can stop production activities, causing delays, loss of profit and even personal injuries. While the predictive maintenance approach is nowadays a mainstay of modern factory management, usually only big companies with dedicated research department can deploy it, since its application involves scientific knowledge that is not available in smaller production environments. This paper describes how the EU project PROGRAMS answers the needs of small and medium companies that wish to apply an Industrial Internet of Things (IIoT) approach to maintenance management.

关键词： Maintenance engineering Preventive maintenance Decision support systems Optimization problems Sensors systems

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Parallel Driving in CPSS:A Unified Approach for Transport automation and Vehicle Intelligence

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 577-587页

作者： Fei-Yue Wang Nan-Ning Zheng Dongpu Cao Clara Marina Martinez Li Li Teng Liu IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Advanced Vehicle Engineering Center Cranfield University Department of Automation Tsinghua University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Qingdao Huituo Intelligent Machine Company

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel *** proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.

关键词： ACP theory connected automated driving cyber-physical-social systems(CPSS) iHorizon parallel driving parallel horizon parallel learning parallel reinforcement learning parallel testing

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Development of a Tetris Playing Robot Controlled by KNR

Development of a Tetris Playing Robot Controlled by KNR

引用

IEEE International Conference on Mechatronics and automation

作者： Ming Wang Yanlu Zhang Huifang Dong Xu Li Junzhi Yu Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

关键词： Mobile robots Cameras Robot sensing systems Image color analysis Servomotors Robot kinematics

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