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A Distributed Market-based Algorithm for the Multi-robot Assignment Problem

Procedia computer Science 2014年 32卷 1108-1114页

作者： Sahar Trigui Anis Koubaa Omar Cheikhrouhou Habib Youssef Hachemi Bennaceur Mohamed-Foued Sriti Yasir Javed University of Manouba ENSI Tunisia Cooperative Intelligent Networked Systems (COINS) Research Group Riyadh Saudi Arabia Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia CISTER/INESC-TEC ISEP Polytechnic Institute of Porto Porto Portugal University of Sfax Computer and Embedded Systems Lab Tunisia University of Sousse PRINCE Research Unit Tunisia Al Imam Mohammad Ibn Saud Islamic University Computer Science Department Saudi Arabia

Assigning tasks to a set of robots is a fundamental problem in robotics. It consists in finding the best task assignment to the available robots. In this paper, we present two distributed market-based algorithms to solve the assignment problem where n robots compete for n tasks with the assumption that each robot can be assigned to only one task. The first algorithm, called DMB, represents a Distributed Market-Based algorithm where each robot bids for every task. The second algorithm is an extension of the DMB. It consists in swapping tasks between robots in order to improve the efficiency of the whole assignment. We conducted both real-world experimental testing, and MATlab simulations to evaluate performance of the proposed algorithms and compare them against the centralized Hungarian algorithm in terms of traveled distance. Simulation results show that the IDMB algorithm produces near optimal solutions and in several cases it gives the optimal solution. In addition, we demonstrated the feasibility of our algorithms through real-world experimentation on robots.

关键词： Task Assignment Market-based Algorithms Multi-Robot Task Allocation ROS.

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Re-identifying people based on indexing structure and manifold appearance modeling

Re-identifying people based on indexing structure and manifo...

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2013 26th Conference on Graphics, Patterns and Images, SIBGRAPI 2013

作者： Dutra, Cristianne R.S. Schwartz, William Robson Souza, Tiago Alves, Raul Oliveira, Luciano Department of Computer Science Universidade Federal de Minas Gerais UFMG Belo Horizonte-MG 31270-901 Brazil Dept. of Computer Science Intelligent Vision Research Lab Federal University of Bahia Salvador Brazil

ISBN: (纸本)9780769550992

The role of person re-identification has increased in the recent years due to the large camera networks employed in surveillance systems. The goal in this case is to identify individuals that have been previously identified in a different camera. Even though several approaches have been proposed, there are still challenges to be addressed, such as illumination changes, pose variation, low acquisition quality, appearance modeling and the management of the large number of subjects being monitored by the surveillance system. The present work tackles the last problem by developing an indexing structure based on inverted lists and a predominance filter descriptor with the aim of ranking candidates with more probability of being the target search person. With this initial ranking, a more strong classification is done by means of a mean Riemann covariance method, which is based on a appearance strategy. Experimental results show that the proposed indexing structure returns an accurate short-list containing the most likely candidates, and that manifold appearance model is able to set the correct candidate among the initial ranks in the identification process. The proposed method is comparable to other state-of-the-art approaches. © 2013 IEEE.

关键词： Cameras

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A parallel framework for multipoint spiral search in ab initio protein structure prediction

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Advances in Bioinformatics 2014年第unknown期2014卷 985968页

作者： Rashid, Mahmood A. Shatabda, Swakkhar Newton, M. A. Hakim Hoque, Md Tamjidul Sattar, Abdul Institute for Integrated and Intelligent Systems Science 2 (N34) 1.45 Nathan QLD 4111 170 Kessels Road Australia Queensland Research Lab National ICT Australia Brisbane QLD 4000 2 George Street Australia Computer Science Math 308 New Orleans LA 70148 2000 Lakeshore Drive United States

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20×20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads. © 2014 Mahmood A. Rashid et al.

关键词： resolution hydrophobic-polar energy multipoint Parallel Lines Resolving power Energy sources Spiral ENERGY MODELS SEARCHING ALGORITHM Protein Structure Searches Frameworks

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Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

Pedestrian detection based on LIDAR-driven sliding window an...

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IEEE Symposium on intelligent Vehicle

作者： Luciano Oliveira Urbano Nunes Intelligent Vision Research Laboratory Federal University Bahia Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.

关键词： Detectors Laser radar Feature extraction Support vector machines Training Probabilistic logic Vectors

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Premature Ventricular Contraction Arrhythmia Detection and Classification with Gaussian Process and S Transform

Premature Ventricular Contraction Arrhythmia Detection and C...

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International Conference on Computational Intelligence, Communication systems and Networks

作者： Yacoub Bazi Haikel Hichri Naif Alajlan Nassim Ammour Advanced Lab for Intelligent Systems Research (ALISR) College of Computer and Information Sciences King Saud University

ISBN: (纸本)9781479905874

This paper presents an efficient Bayesian classification system based on Gaussian process classifiers (GPC) for detecting premature ventricular contraction (PVC) beats in electrocardiographic (ECG) signals. GPC have the advantage over SVM classifiers in that the parameters of its kernel are automatically selected according to the Bayesian estimation procedure based on Laplace approximation. We also propose to feed the classifier with different representations of the ECG signals based on morphology, discrete wavelet transform, and S-transform. The latter representation has never been used for ECG signals before. The experimental results obtained on 48 records (i.e., 109887 heart beats) of the MIT-BIH arrhythmia database showed that for all feature representations adopted in this work, the proposed GP classifier combined with the S-transform and trained with only 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 96% on the whole 48 recordings.

关键词： ECG Gaussian process classification S-transform PVC arrhythmia detection

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A New Evolutionary-Incremental Framework for Feature Selection

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Advances in Artificial Intelligence 2014年第1期2014卷

作者： Mohamad-Hoseyn Sigari Muhammad-Reza Pourshahabi Hamid-Reza Pourreza Machine Vision Research Lab. Computer Engineering Department Ferdowsi University of Mashhad Mashhad 91779 Iranum.ac.ir Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran 14399 Iranut.ac.ir

Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.

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Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

Toward automated driving in cities using close-to-market sen...

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2013 IEEE intelligent Vehicles Symposium, IEEE IV 2013

作者： Furgale, Paul Schwesinger, Ulrich Rufli, Martin Derendarz, Wojciech Grimmett, Hugo Muhlfellner, Peter Wonneberger, Stefan Timpner, Julian Rottmann, Stephan Li, Bo Schmidt, Bastian Nguyen, Thien Nghia Cardarelli, Elena Cattani, Stefano Bruning, Stefan Horstmann, Sven Stellmacher, Martin Mielenz, Holger Koser, Kevin Beermann, Markus Hane, Christian Heng, Lionel Lee, Gim Hee Fraundorfer, Friedrich Iser, Rene Triebel, Rudolph Posner, Ingmar Newman, Paul Wolf, Lars Pollefeys, Marc Brosig, Stefan Effertz, Jan Pradalier, Cedric Siegwart, Roland Autonomous Systems Lab ETH Zürich Switzerland Volkswagen AG Group Research Switzerland University of Oxford United Kingdom IBR Technische Universität Braunschweig Germany Computer Vision and Geometry Group ETH Zürich Switzerland VisLab Università Degli Studi di Parma Italy Carmeq GmbH Germany I AV GmbH Germany Robert Bosch GmbH Corporate Research CR/AEV CR/AEM Germany

ISBN: (纸本)9781467327558

Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times. Automated cars have the potential to reduce the environmental impact of driving, and increase the safety of motor vehicle travel. The current state-of-the-art in vehicle automation requires a suite of expensive sensors. While the cost of these sensors is decreasing, integrating them into electric cars will increase the price and represent another barrier to adoption. The V-Charge Project, funded by the European Commission, seeks to address these problems simultaneously by developing an electric automated car, outfitted with close-to-market sensors, which is able to automate valet parking and recharging for integration into a future transportation system. The final goal is the demonstration of a fully operational system including automated navigation and parking. This paper presents an overview of the V-Charge system, from the platform setup to the mapping, perception, and planning sub-systems. © 2013 IEEE.

关键词： Automotive industry

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Activity recognition in wide aerial video surveillance using entity relationship models 12

Activity recognition in wide aerial video surveillance using...

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20th ACM SIGSPATIAL International Conference on Advances in Geographic Information systems, ACM SIGSPATIAL GIS 2012

作者： Choi, Jongmoo Dumortier, Yann Prokaj, Jan Medioni, Gérard Computer Vision Lab. Institute for Robotics and Intelligent Systems University of Southern California United States

ISBN: (纸本)9781450316910

We present the design and implementation of an activity recognition system in wide area aerial video surveillance using Entity Relationship Models (ERM). In this approach, finding an activity is equivalent to sending a query to a Relational DataBase Management System (RDBMS). By incorporating reference imagery and Geographic Information System (GIS) data, tracked objects can be associated with physical meanings, and several high levels of reasoning, such as traffic patterns or abnormal activity detection, can be performed. We demonstrate that different types of activities, with hierarchical structure, multiple actors, and context information, are effectively and efficiently defined and inferred using the ERM framework. We also show how visual tracks can be better interpreted as activities by using geo information. Experimental results on both real visual tracks and GPS traces validate our approach. © 2012 Authors.

关键词： Security systems

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An extended composite nonlinear state feedback control approach for mimo linear systems with actuator saturation

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ICIC Express Letters 2013年第3 B期7卷 1145-1149页

作者： Peng, Wendong Yin, Jibin Liu, Tao Cao, Chunyao Shanghai Hongjian Intelligent Science and Technology Co. Ltd. No. 333 Weining Road Changning District Shanghai 200336 China Department of Mechanical Engineering Kunming University of Science and Technology No. 68 Wenchang Road 12.1 Street Kunming 650093 China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology No. 68 Wenchang Road 12.1 Street Kunming 650093 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology 185 Miyanokuchi Tosayamada-Cho Kami-City Kochi 782-8502 Japan Jiaxing Planning and Research Institute Co. Ltd. No. 318 Zhonghuan South Road Jiaxing 314001 China

In this paper, a new design method of extended composite nonlinear feedback control is presented to solve the tracking control of time-varying trajectory for MIMO linear systems with saturation nonlinearity. Enlightened by the initial composite nonlinear feedback technique, the extended control approach is established by a linear feedback law with an additional feedforward term and a nonlinear feedback law. It is a nonlinear design method that yields a varying damping ratio, which can allow fast response and almost no overshoot for tracking control. The stability guaranteed on an ellipsoid contained in the domain of attraction is rigorously demonstrated in the presence of saturation. The simulation results show that the closed-loop system has the fast rising time and almost no overshoot and behaves a perfect dynamic performance. © 2013 ICIC International.

关键词： Feedback control

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A manually-actuated MRI compatible robotic manipulator for image-guided interventions

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Physica Medica 2014年 30卷 e55-e56页

作者： Eftychios G. Christoforou Christoforos Keroglou Andrew Webb Nikolaos V. Tsekos Ioannis Seimenis KIOS Research Center for Intelligent Systems and Networks University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus C.J. Gorter Center for High Field MRI Leiden University Medical Center Leiden Netherlands Medical Robotics Laboratory University of Houston Houston TX USA Med. Physics Lab Medical School Democritus University of Thrace Alexandroupolis Greece

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