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Loop exploration for SLAM with fusion of advanced sonar features and laser polar scan matching

Loop exploration for SLAM with fusion of advanced sonar feat...

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2009 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Fredy Tungadi Lindsay Kleeman Intelligent Robotics Research Centre Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

SLAM is a well studied technique for robots to build a map of environments while at the same time keeping track of their pose (position and orientation). However SLAM does not provide control approaches for how the robot moves around the environment. This paper presents an integrated approach to create a fully autonomous exploring and mapping robot. An EKF-SLAM approach is used to fuse Advanced Sonar and Laser Scan-Matching. This also tackles the problem of map-drifts in some types of environment where lasers do not supply sufficient information in some directions such as along a corridor. In addition, the proposed exploration algorithm takes advantage of the characteristic of the Voronoi Graph to enable the robot to strategically explore the environments in a loop-closing fashion and safe manner. By revisiting areas to close loops as early as possible, the robot can build a more stable map incrementally while still reliably tracking its pose. Experimental results of the integrated approach are shown to demonstrate the algorithm provides real-time exploration of a mobile robot in an initially unknown real environment. Experimental comparisons of exploration strategies with and without early local loop closing demonstrate the benefits of the approach in the map quality.

关键词： Simultaneous localization and mapping Sonar Laser fusion intelligent robots Tracking loops Mobile robots Path planning systems engineering and theory Uncertainty USA Councils

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Load forecasting of a desert: A computational intelligence approach

Load forecasting of a desert: A computational intelligence a...

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2009 15th International Conference on intelligent System Applications to Power systems, ISAP '09

作者： Saber, Ahmed Yousuf Al-Shareef, Abdulaziz Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409-0040 United States Electrical and Computer Engineering Department King Abdulaziz University Jeddah KSA Saudi Arabia

ISBN: (纸本)9781424450985

This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research, two models are implemented - firstly load forecasting model for prediction;however, it is not sufficient for desired level of accurate forecasting, and secondly, optimization to improve the results up to at least better than existing results. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is a mathematical tool for mapping complex relations;it is well proved for the successful use of prediction, function approximation with dynamics, categorization, classification, and so forth. In this research, 24- step ahead calculations are performed in the ANN model and results are moderate. On the other hand, PSO is the most promising optimization tool. It is a swarmed based optimization method;it has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;it can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization tool that is applied on the weight matrix of ANN to improve results. In this research, PSO reliably and accurately tracks the continuously changing weights of ANN for uncertain load demand. By analyzing the model of ANN for the load-forecasting problem of SEC-WOA with hundreds of thousands of data and changing-uncertain load demand, the PSO is applied for the ANN weight adjustment and to optimize the uncertain load demand, as the ANN is not an optimization method. Results

关键词： Neural networks

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Fuzzy controller for cooperative object pushing with variable line contact

Fuzzy controller for cooperative object pushing with variabl...

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International Conference on Mechatronics (ICM)

作者： Mahsa Aliakbar Golkar Sarah Taghavi Namin Hossein Aminaiee Control and Intelligent Processing Center of Excellence Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Iran

Designing an appropriate cooperation protocol in multi-robot systems such as multi-robot object manipulation systems is a challenging task. In this paper designing and implementing a cooperation protocol between two robots pushing an object toward an arbitrary goal configuration is investigated. The proposed method benefits from task decoupling which simplifies the cooperation protocol design. Each robot gets its proper action from a particular fuzzy inference system independently. The fuzzy controllers outputs are low-level robot commands by which the need to a robot path planning algorithm is eliminated. Simulation results show that robots could push the object to the goal configuration cooperatively. Experiments have been conducted to approve the proposed system.

关键词： Fuzzy control Robot kinematics Protocols Fuzzy systems Electric variables control Control systems Multirobot systems Path planning Inference algorithms Mobile robots

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Comparison of feedforward and feedback neural network architectures for short term wind speed prediction

Comparison of feedforward and feedback neural network archit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Richard L. Welch Stephen M. Ruffing Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri S & T MO USA

This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP performance is comparable. The better performance of the feedback architectures is also shown using the mean absolute relative error. While the SRN performance is superior, the increase in required training time for the SRN over the other networks may be a constraint, depending on the application.

关键词： Neurofeedback Neural networks Feedforward neural networks Wind speed Recurrent neural networks Multi-layer neural network Particle swarm optimization Multilayer perceptrons Testing Meteorology

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Accelerometer-based control of an industrial robotic arm

Accelerometer-based control of an industrial robotic arm

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Pedro Neto J. Norberto Pires A. Paulo Moreira Industrial Robotics Laboratory-Mechanical Engineering Department POLO II University of Coimbra Coimbra Portugal Institute of Systems and Robotics (ISR)-Department of Electrical and Computer Engineering University of Porta Porto Portugal

Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.

关键词： Industrial control Service robots electrical equipment industry Defense industry Educational robots Robot control Control systems Accelerometers Artificial neural networks Delay

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Getting an NSF CAREER award and beyond

Getting an NSF CAREER award and beyond

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IEEE PES Power systems Conference and Exposition, PSCE

作者： Ganesh K. Venayagamoorthy Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

The National Science Foundation (NSF), USA, CAREER program is a premier program that emphasizes the importance the Foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper presents the research and education experiences gained by the principal investigator - Venayagamoorthy and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the Power, Control and Adaptive Networks (PCAN) program of the electrical, Communications and Cyber systems Division, effective June 1, 2004. The main emphasis of this paper is how and what a CAREER proposal awardee do to be a double winner in all aspects - research, education and outreach. In addition, suggestions on writing a winning NSF CAREER proposal are presented.

关键词： Awards

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Economic dispatch of a differential evolution based generator maintenance scheduling of a power system

Economic dispatch of a differential evolution based generato...

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IEEE General Meeting Power& Energy Society

作者： Y. Yare G. K. Venayagamoorthy A. Y. Saber Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

This paper presents generator maintenance scheduling (GMS) and economic dispatch (ED) of the Indonesian power system based on minimization of an economic cost function for the economical and reliable operation of a power system, while satisfying crew/manpower, maintenance window, load demand, generation limits and spinning reserve constraints. Differential evolution (DE), an evolutionary computation technique and an optimization algorithm that utilizes the differential information to guide its further search, is known to effectively solve large scale optimization problems and has been widely applied in power system. In this paper, the DE approach is proposed for solving the GMS problem and the resulting optimal maintenance schedules are used by the Lambda-iteration (LI) method to calculate the ED for the 19 generating units supplying power to two major industrial parks located in Bintan and Batam in Indonesian. The simulation results show great prospect for application in control centers by solving GMS and ED problems of many utilities which seek to minimize running costs. The results obtained offer an effective alternative for solving the GMS and ED problems.

关键词： Power system economics Power generation economics Power generation Power systems Power system reliability Maintenance Job shop scheduling Processor scheduling Cost function Spinning

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Minimal exposure path algorithms for directional sensor networks

Minimal exposure path algorithms for directional sensor netw...

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2009 IEEE Global Telecommunications Conference, GLOBECOM 2009

作者： Liu, Liang Zhang, Xi Ma, Huadong Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A and M University College Station TX 77843 United States Beijing Key Lab. of Intelligent Telecomm. Software and Multimedia Beijing University of Posts and Telecomm. Beijing 100876 China

ISBN: (纸本)9781424441488

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for directional sensor networks. We first develop a directional sensing model: directional sensitivity model. Then, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function, and generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. Based on the above weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive simulations to validate and evaluate our proposed models and algorithms.

关键词： Wireless sensor networks

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Exposure-path prevention in directional sensor networks using sector model based percolation

Exposure-path prevention in directional sensor networks usin...

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2009 IEEE International Conference on Communications, ICC 2009

作者： Liang, Liu Xi, Zhang Huadong, Ma Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A and M University College Station TX 77843 United States Beijing Key Lab. of Intelligent Telecomm. Software and Multimedia Beijing University of Posts and Telecomm. Beijing 100876 China

ISBN: (纸本)9781424434350

In wireless sensor networks, most existing works on region coverage mainly concentrate on the omnidirectional sensor based full coverage, which ensures that all points in the sensor-deployed region are covered. In contrast, this paper studies the problem of exposure-path prevention for the region coverage in directional sensor networks. Because the exposure paths are prevented as long as no moving objects or phenomena can go through a sensor-deployed region without being detected, exposure-path prevention does not require full coverage, and instead it only needs the partial coverage. Towards this end, we apply the percolation theory to solve the exposure path problem for directional sensor networks. In particular, we map the exposure path problem into a sector based percolation model, and then derive the bounds of critical density where directional sensors are deployed according to a 2-dimensional Poisson process. Also conducted is a set of extensive simulations to validate and evaluate our developed models and schemes. ©2009 IEEE.

关键词： Wireless sensor networks

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Mining discriminative adjectives and prepositions for natural scene recognition

Mining discriminative adjectives and prepositions for natura...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Bangpeng Yao Juan Carlos Niebles Li Fei-Fei Department of Computer Science Princeton University NJ USA Robotics and Intelligent Systems Group Universidad del Norte Colombia Department of Electrical Engineering Princeton University NJ USA

This paper presents a method that considers not only patch appearances, but also patch relationships in the form of adjectives and prepositions for natural scene recognition. Most of the existing scene categorization approaches only use patch appearances or co-occurrence of patch appearances to determine the scene categories, but the relationships among patches remain ignored. Those relationships are, however, critical for recognition and understanding. For example, a `beach' scene can be characterized by a `sky' region above `sand', and a `water' region between `sky' and `sand'. We believe that exploiting such relations between image regions can improve scene recognition. In our approach, each image is represented as a spatial pyramid, from which we obtain a collection of patch appearances with spatial layout information. We apply a feature mining approach to get discriminative patch combinations. The mined patch combinations can be interpreted as adjectives or prepositions, which are used for scene understanding and recognition. Experimental results on a fifteen class scene dataset show that our approach achieves competitive state-of-the-art recognition accuracy, while providing a rich description of the scene classes in terms of the mined adjectives and prepositions.

关键词： Layout Road transportation Image representation intelligent robots Image recognition Data mining computer science intelligent systems Histograms

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