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International Conference on Intelligent Transportation

作者： Lindong Guo Ming Yang Bing Wang Chunxiang Wang Department of Automation The Key Laboratory of System Control and Information Processing Shanghai China Chunxiang Wang is with the Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018901

Localization is considered as a key capability for autonomous vehicles act in urban environments. Though have been proved to be able to perform convictive results, localization methods using neither laser scanners nor vision sensors could achieve the goal about balancing between accuracy and cost. In this paper, an occupancy grid based localization framework is presented in order to obtain a precise positioning result with relatively low-cost sensor configuration in large scale urban environment. The proposed approach takes a prebuilt global grid map as prior knowledge for localization. Model based feature extraction method is introduced to provide laser points classification, with each extracted point allocated a specified weight to describe local characteristic. The prior grid map is generated from weighted point cloud to be able to describe the local metric features such as curbs and building facades. Localization function is then carried out with a weight point based maximum likelihood matching method to determine the correspondence between local point cloud and the reference grid map. There are also position initialization and reference map management modules to make the framework more practical and reliable. In the end, the proposed approach has been validated by promising experimental results with long distance tests in large urban environments.

关键词： Three-dimensional displays Feature extraction Sensors Laser modes Roads Vehicles

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Road DNA based localization for autonomous vehicles

Road DNA based localization for autonomous vehicles

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IEEE Symposium on Intelligent Vehicle

作者： Liang Li Ming Yang Chunxiang Wang Bing Wang Department of Automation Key Laboratory of System Control and Information Processing Shanghai CN Chunxiang Wang is with Research Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018222

High-precision and reliable localization is current research focus in the area of autonomous vehicles. Previous studies rely on either high-cost sensors or some specific characteristics, which means that the methods are limited to only a bit given situations. In this paper, a road DNA based localization method is proposed. It could afford high-precision result and does not have the shortcomings of previous methods at the same time. The scenery on both sides of the roads are used to generate the prior-map. The map is presented as grid map by the joint probability of occupation and reflectivity. With this type of map, different environments show different properties, which means that this method is not limited to specific environments and is effective in most cases. It costs much less memory than the previous maps. The map and live road scene flatting are both generated by data collected by low-cost LIDAR. Normalized information Distance is utilized to align the live road scene flatting with the road DNA. Experiments show the validation and precision of this method.

关键词： Roads Three-dimensional displays Vehicles DNA Trajectory Simultaneous localization and mapping Autonomous automobiles

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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A robust terrain-based road vehicle localization algorithm

A robust terrain-based road vehicle localization algorithm

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IEEE Symposium on Intelligent Vehicle

作者： Tianyi Li Ming Yang Xujin Zhou Chunxiang Wang Department of Automation Key Laboratory of System Control and Information Processing Shanghai China Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Research Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018222

Terrain-based localization is an alternate to the global positioning system (GPS) in signal blocked areas. However, terrain-based localization technique may suffer from low accuracy or even fail when brake vibration occurs. This paper presents a real-time algorithm for vehicle localization which is robust against brake vibration. The input includes a reference map of pitch difference and measurements from rear wheel encoders and inertial measurement units (IMU). This method consists of two steps. In the first step, terrain map is generated using pitch difference at equidistant intervals. After that, the Bayesian inference and particle filters are adopted in the second step to identify the vehicle location during travel. To enhance system stability, we propose dynamic distributions of filter variances according to acceleration input. Experimental results demonstrate that the localization method with dynamic distributions can localize the vehicle quickly with high accuracy even when a quite severe shuddering happens.

关键词： Vehicles Roads Brakes Acceleration Heuristic algorithms Vibrations Standards

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Research on Pursuit-evasion Games with Multiple Heterogeneous Pursuers and A High Speed Evader

Research on Pursuit-evasion Games with Multiple Heterogeneou...

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第27届中国控制与决策会议

作者： Hongpeng Wang Qiang Yue Jingtai Liu Institute of Robotics & Automation Information System Nankai University State Key Laboratory of Robotics Tianjin Key Laboratory of Intelligent Robotics

We deal with Pursuit-evasion games with a high speed evader which has superiority in velocity over a group of heterogeneous pursuers in this *** in the group of pursuers is expressed as heterogeneity in the individual maximum *** introduce Apollonius circles generated by every pursuer and the evader to analyse the criteria for a successful *** robots pursuit problem under unknown environment is investigated from the view of behavior-based control method,and Motor Schema-based reactive control architecture is *** basic behaviors using for pursuers are designed,namely Movetogoal,Avoidobatacle and Hunting,wherein hunting behavior is realized through a kind of reinforcement learning algorithm called *** of the evader is based on synthesized behavior,generated through summarizing the outputs of all behaviors *** of simulation experiments validate the effectiveness of our method.

关键词： Pursuit-evasion games Heterogeneity Apollonius circle Q-Learning Motor Schema

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Salient Region Detection by Learning Accurate Background Template

Salient Region Detection by Learning Accurate Background Tem...

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第27届中国控制与决策会议

作者： Hongpeng Wang Pu Zhang Jingtai Liu Institute of Robotics and Automation Information System Nankai University State Key Laboratory of Robotics Tianjin Key Laboratory of Intelligent Robotics

ISBN: (纸本)9781479970186

In this paper,a salient region detection algorithm based on priors which estimate the likely position of background which we make it more accurate,is *** of considering the contrast between each element（pixel or region） and their surrounding regions,we consider background cues in different ways such as PCA and sparse *** the saliency value of each element（superpixel） is obtained by computing the similarity with background *** such a problem which the background template based on prior probably includes the interference of foreground,so we use graph-based manifold ranking method to eliminate the interference of foreground in background *** a detailed experimental evaluation on large benchmark database,the results shows the proposed algorithm perform well when compare with other state-of-the-art methods and can effectively eliminate the interference of foreground in initial background templates.

关键词： salient region detection background template superpixel PCA sparse representation graph-based manifold ranking

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A Visual Feedback Control Framework Oriented to Lung Branching Pattern Formation Simulation

A Visual Feedback Control Framework Oriented to Lung Branchi...

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2015年中国智能自动化学术会议

作者： Hui Xu Mingzhu Sun Jianda Han Xin Zhao Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

Mathematical modeling based on reaction-diffusion partial differential equations,is a well-known way to study the mechanism of lung branching pattern ***,model parameter identification is one of the key problems in the research,which is both labor-intensive and of low *** work out this problem,a visual feedback control framework is proposed in this *** framework based on automatic control theory is built,by means of pattern feature feedback,to identify model *** results suggest that this framework is effective and feasible.

关键词： Visual feedback control framework Lung branching pattern formation Model parameter identification Pattern feature extraction

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RS-CPS: A Distributed Architecture of Robotic Surveillance Cyber-Physical system in the Nature Environment

RS-CPS: A Distributed Architecture of Robotic Surveillance C...

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IEEE International Conference on Cyber Technology in automation, Control, and Intelligent systems

作者： Hongpeng Wang Jingtai Liu Jianda Han Institute of Robotic and Automatic Information System. Nankai University State Key Laboratory of Robotics (Shenyang Institute of Automation Chinese Academy of Sciences)

ISBN: (纸本)9781479987313

Modern nature surveillance systems are highly automated and require distributed architectures in order to increase understanding of the environment by integrating data from various monitors and sensors, which are mounted not only on fixed stations, but also on robotic actuators. In this paper, we propose a distributed architecture of robotic surveillance cyber-physical system (RS-CPS) in the nature environment, which is designed as integration of computation and physical processes. The RS-CPS system consists of five levels including smart connection, data-to-information conversion, cyber communication, cognition, and configuration. Based on this architecture, the nature risk such as wildfires, the activities of wild animals and the climate changes in plants can be constantly monitored by the cooperation of multiple agents such as autonomous UAVs, robotic pan-tilt-zoom cameras, forest infrared cameras, songbird acoustic recorders, GPS tracking collars and ecological factor sensors located in field. The experiments demonstrate the feasibility and effectiveness of the new architecture.

关键词： ARCHITECTURE Fixed platforms Environments Building construction new buildings MONITOR Distribute Sensors Wild Animals Tracking collars

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A Direct Swing Constraint-based Trajectory Planning Method for Underactuated Overhead Cranes

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自动化学报 2014年第11期40卷 2414-2419页

作者： WANG Peng-Cheng FANG Yong-Chun JIANG Zi-Ya Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin 300071 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin 300071 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016. China

This paper proposes a novel swing constraint-based trajectory planning method for nonlinear overhead crane systems. To enhance the efficiency and security of the transportation process, some desired trajectories are designed to achieve the following merits： 1） leading the trolley to reach the destination sufficiently fast; 2） keeping the payload swing in an acceptable domain; 3） eliminating the residue swing when the trolley stops at the desired position. Specifically, the trajectories are divided into three stages. For each stage, the desired curve of the swing angle is directly constructed in accordance with anti-swing and zero-residual swing requirements, based on which the trolley trajectory is then obtained by analyzing the nonlinear kinematics of the crane system. An optimization mechanism is introduced to make intelligent compromises among the indices of transportation time, maximal swing angle, and so on. Both simulation and experimental results are provided to demonstrate the performance of the proposed direct swing constraint-based trajectory planning method.

关键词：桥式起重机基于约束 Swing 欠驱动规划法非线性运动学载荷摆动轨迹规划

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