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Proposal of a new real-time cooperative challenge in mobile robotics

IFAC Proceedings Volumes 2011年第1期44卷 9836-9841页

作者： P. Costa A. Moreira J. Gonçalves J. Lima Faculty of Engineering of University of Porto Department of Electrical Engineering and Computers Porto Portugal Polytechnic Institute of Bragança Department of Electrical Engineering Bragança Portugal Robotics and Intelligent Systems Research Group INESC Porto Portugal

Abstract In this paper it is presented a new robot competition that is going to be included in Robotica 2011, the main robotics Portuguese Competition. The robot competition takes place in an emulated factory plant, where Automatic Guided Vehicles (AGVs) must cooperate to perform tasks. To accomplish their goals the AGVs must deal with localization, navigation, scheduling and cooperation problems, that must be solved autonomously. One option of prototyping the AGVs is the use of the Lego Mindstorms NXT technology. The presented example can play an important role in education due to the inherent multi-disciplinary concepts that are involved, motivating students to technological areas. It also plays an important role in research and development, because it is expected that the outcomes that will emerge here, will later be transfered to other application areas, such as service robots and manufacturing.

关键词： robotics Education Manufacturing

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Design and application of Soft Sensor using Ensemble Methods

Design and application of Soft Sensor using Ensemble Methods

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Symone Soares Rui Araújo Pedro Sousa Francisco Souza Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

Industries are faced with the choice of suitable process control policies to improve costs, quality and raw material consumption. In the paper pulp industry, it is important to estimate quickly the Chemical Oxygen Demand (COD), a parameter that is highly correlated to product quality. Soft Sensors (SSs) have been established as alternative to hardware sensors and laboratory measurements for monitoring and control purposes. However, in real setups it is often difficult to get sufficient data for SS development. This work proposes Ensemble Methods (EMs) as a way to improve the SS performance for small datasets. EMs use a set of models to obtain better prediction. Their success is usually attributed to the diversity. Bootstrap and noise injection are used to produce diverse models. Several combinations of EMs are compared. The SS is successfully applied to estimate COD in a pulp process.

关键词： Noise Training Artificial neural networks Diversity reception Chemicals Paper pulp Predictive models

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Stable indirect adaptive predictive fuzzy control for industrial processes

Stable indirect adaptive predictive fuzzy control for indust...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jérôme Mendes Rui Araújo Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

The paper proposes a stable indirect adaptive fuzzy predictive control, which is based on a discrete-time Takagi-Sugeno (T-S) fuzzy model and on the Generalized predictive control (GPC) algorithm. The T-S fuzzy model is used to approximate the unknown nonlinear plant, that to provide good accuracy in identification of unknown model parameters, three online adaptive laws are proposed. It is demonstrated that the tracking error remains bounded. The stability of closed-loop control system is studied and proved via the Lyapunov stability theory. To validate the theoretical developments and to demonstrate the performance of the proposed control, the controller is applied on a nonlinear simulated laboratory-scale liquid-level process. The simulation results show that the proposed method has a good performance and disturbance rejection capacity in industrial processes.

关键词： Adaptation models Mathematical model Predictive control Predictive models Nonlinear systems Computational modeling

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Variable and time-lag selection using empirical data

Variable and time-lag selection using empirical data

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Francisco Souza Rui Araújo Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

The paper proposes a method to select the best variables and respective time-lags for industrial applications when the objective is the estimation of a target variable using the information content of empirical data. No further information is assumed about the process. The problem of jointly selecting the best variables and the respective time-lags is treated as a variable selection problem. This assumption implies an increase of input dimensionality and multicollinearity into input space. Then, a multidimensional mutual information estimator based on the l-nearest neighbor algorithm is used in a forward search procedure to select the best variables and and respective time-lags. To verify the performance of selected variables and delays, the method was successfully applied in two data sets. A least squares support vector machine was used as the main model for the soft sensor in both cases.

关键词： Input variables Delay Mutual information Correlation Computational modeling Estimation Predictive models

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Co-evolutionary genetic Multilayer Perceptron for feature selection and model design

Co-evolutionary genetic Multilayer Perceptron for feature se...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Francisco Souza Tiago Matias Rui Araójo Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

This paper proposes a method for Soft Sensors design using a Multilayer Perceptron model based on co-evolutionary genetic algorithms, called CEV-MLP. This method jointly and automatically selects the best input variables and the best configuration of the network for the prediction setting. The CEV-MLP is constituted by three levels, the first level selects the best input variables and respective delays set, the second level is composed by the parameters of hidden layers to be optimized (number of neurons in the hidden layers and transfer function), and the third level is the combination of first and second level. The method was successfully applied, and compared with two state-of-the-art methods, in three real datasets. In all the experiments, the proposed method shows the best approximation accuracy, while all the design of the prediction setting is performed automatically.

关键词： Input variables Biological cells Neurons Delay Genetic algorithms Sensors Training

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Automatic extraction of the fuzzy control system for industrial processes

Automatic extraction of the fuzzy control system for industr...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jérôme Mendes Ricardo Seco Rui Araújo Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

The paper proposes a new method to automatically extract all fuzzy parameters of a Fuzzy Logic Controller (FLC) in order to control nonlinear industrial processes. The learning of the FLC is performed from controller input/output data and by a hierarchical genetic algorithm (HGA). The algorithm is composed by a five level structure, where the first level is responsible for the selection of an adequate set of input variables. The second level considers the encoding of the membership functions. The individual rules are defined on the third level. The set of rules are obtained on the fourth level, and finally, the fifth level, selects the elements of the previous levels, as well as, the t-norm operator, inference engine and defuzzifier methods which constitute the FLC. To demonstrate and validate the effectiveness of the proposed algorithm, it is applied to control a simulated water tank level process.

关键词： Fuzzy systems Input variables Genetic algorithms Process control Encoding Delay Genetics

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Adaptive predictive control with recurrent fuzzy neural network for industrial processes

Adaptive predictive control with recurrent fuzzy neural netw...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jérôme Mendes Nuno Sousa Rui Araújo Institute of Systems and Robotics (ISR-UC) and Department of Electrical and Computer Engineering (DEEC-UC) University of Coimbra Coimbra Portugal

The paper proposes an adaptive fuzzy predictive control method. The proposed controller is based on the Generalized predictive control (GPC) algorithm, and a recurrent fuzzy neural network (RFNN) is used to approximate the unknown nonlinear plant. To provide good accuracy in identification of unknown model parameters, an online adaptive law is proposed to adapt the consequent part of the RFNN, and its antecedent part is adapted by back-propagation method. The stability of closed-loop control system is studied and proved via the Lyapunov stability theory. A nonlinear lab oratory-scale liquid-level process is used to validate and demonstrate the performance of the proposed control. The simulation results show that the proposed method has good performance and disturbance rejection capacity in industrial processes and outperforms the PID and the classical GPC controllers.

关键词： Adaptation models Predictive models Predictive control Fuzzy control Mathematical model Fuzzy neural networks Equations

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Collaborative sequential-based detection in wireless sensor networks

Collaborative sequential-based detection in wireless sensor ...

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Asilomar Conference on Signals, systems & computers

作者： Sabina Zejnilović Joao Pedro Gomes Bruno Sinopoli Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics-Instituto Superior Técnico (ISR/IST) Technical University of Lisbon Lisboa Portugal

Due to limited power resources, energy efficiency is an important aspect of detection in wireless sensor networks. We propose a collaborative detection scheme, based on sequential hypothesis testing, where a randomly chosen node may initiate a collaboration, collecting observations from neighboring nodes to test the hypotheses. Our simulation results show that for large networks and high SNR, the proposed scheme leads to lower communication cost and similar performance when compared to a standard detection setup, where all the observations are collected in a fusion node. We also examine how the energy efficiency of this scheme evolves as a function of the network structure.

关键词： Collaboration Wireless sensor networks Signal to noise ratio Testing Network topology Monitoring Wireless communication

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Large deviations analysis of consensus+innovations detection in random networks

Large deviations analysis of consensus+innovations detection...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Dragana Bajović Dušan Jakovetić José M. F. Moura João Xavier Bruno Sinopoli Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics (ISR) Instituto Superior Técnico (IST) Technical University of Lisbon Lisboa Portugal

We study the large deviations performance of consensus+innovations distributed detection over random networks, where each sensor, at each time k, weight averages its decision variable with its neighbors decision variables (consensus), and accounts for its new observation (innovation). Sensor observations are independent identically distributed (i.i.d.) both in time and space, but have generic (non Gaussian) distributions. The underlying network is random, described by a sequence of i.i.d. stochastic, symmetric weight matrices W(k); we measure the corresponding speed of consensus by |log r|, where r is the second largest eigenvalue of the second moment of W(k). We show that distributed detection exhibits a phase transition behavior with respect to |log r|: when |log r| is above a threshold, distributed detection is equivalent to the optimal centralized detector, i.e., has the error exponent equal to the Chernoff information. We explicitly quantify the optimality threshold for |log r| as a function of the log-moment generating function Λ 0 (·) of a sensor's log- likelihood ratio. When below the threshold, we analytically find the achievable error exponent as a function of r and Λ 0 (·). Finally, we illustrate by an example the dependence of the optimality threshold on the type of the sensor observations distribution.

关键词： Detectors Technological innovation Robot sensing systems Educational institutions Vectors USA Councils Symmetric matrices

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Towards generic fitting using multiple features Discriminative Active Appearance Models

Towards generic fitting using multiple features Discriminati...

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IEEE International Conference on Image Processing

作者： Pedro Martins Jorge Batista Institute of Systems and Robotics Department of Electrical Engineering and Computers University of Coimbra Portugal

A solution for Discriminative Active Appearance Models is proposed. The model consists in a set of descriptors which are covariances of multiple features evaluated over the neighborhood of the landmarks whose locations are governed by a Point Distribution Model (PDM). The covariance matrices are a special set of tensors that lie on a Riemannian manifold, which make it possible to measure the dissimilarity and to update them, imposing the temporal appearance consistency. The discriminative fitting method produce patch response maps found by convolution around the current landmark position. Since the minimum of the responce map isn't always the correct solution due to detection ambiguities, our method finds candidates to solutions based on a mean-shift algorithm, followed by an unsupervised clustering technique used to locate and group the candidates. A mahalanobis based metric is used to select the best solution that is consistent with the PDM. Finally the global PDM optimization step is performed using a weighted least-squares warp update, based on the Lucas Kanade framework. The weights were extracted from a landmark matching score statistics. The effectiveness of the proposed approach was evaluated on unseen data on the challenging Talking Face video sequence, demonstrating the improvement in performance.

关键词： Shape Computational modeling Training Covariance matrix Manifolds Face Optimization

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