A DYNAMIC PROGRAMMING MODEL FOR NETWORK SERVICE SCHEDULING

Jesuk Ko

Video on Demand (VOD) is one of the most promising services in Broadband Integrated Services Digital Network (B-ISDN) for the next generation. VOD can be classified into two types of services: Near VOD (NVOD) and Interactive VOD (IVOD). For either service, some video servers should be installed at some nodes in the tree structured VOD network, so that each node with a video server stores video programs and distributes stored programs to customers. Given a tree-structured VOD network and the total number of programs being served in the network, the resource allocation problem in a VOD network providing a mixture of IVOD and NVOD services is to determine where to install video servers for IVOD service and both IVOD and NVOD services. In this paper we develop an efficient dynamic programming algorithm for solving the problem. We also implement the algorithm based on a service policy assumed in this paper.

A MULTI-AGENT HOME AUTOMATION SYSTEM FOR POWER MANAGEMENT

Shadi Abras, Stéphane Ploix, Sylvie Pesty and Mireille Jacomino

This paper presents the principles of a Home Automation system dedicated to power management that adapts power consumption to available power ressources according to user comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent is embedded into a power resource or an equipment, which may be an environment (thermal-air, thermal-water, ventilation, luminous) or a service (washing, cooking), and cooperates and coordinates its action with others in order to find acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: an emergency mechanism, which protects from constraint violations, and an anticipation mechanism, which computes the best future set-points according to predicted consumptions and productions and to user criteria. The paper details a negotiation protocol used by the both mechanisms and presents some preliminary simulation results.

CONSCIOUSNESS FOR MODELING INTELLIGENCE - Simulating the Evolution by Closure to the Inverse

Tudor Niculiu, Cristian Lupu and Sorin Cotofana

Intelligence = Consciousness x Adaptability x Intention and Faith = Intuition x Inspiration x Imagination, are the complementary parts of human mind. Conscience = Consciousness x Inspiration is the link between. The subtitle refers to an interpretation of Way, Truth, and Life. It is a strategy of development. Conscience simulation demands transcending from computability to simulability, by an intensive effort on extensive research to integrate essential mathematical and physical knowledge guided by philosophical goals. A way to begin is hierarchical simulation. Coexistent interdependent hierarchies structure the universe of models for complex systems, e.g., hardware - software ones. They belong to different hierarchy types, defined by simulation abstraction levels, autonomous modules, classes, symbols, and knowledge abstractions. Applying Divide et Impera et Intellige to hierarchy types reveals their importance for intelligent simulation. The power of abstraction is the real measure for the human mind. Turning the abstraction into comprehensive construction could be the aim of humanity, the unique God for different cultures of free humans. The way to freedom is by understanding necessity. We have to recall our conscience, to reintegrate our mind, and to remember that society has to assist humans to live among humans, not to consider that they only have to work for it. An operating system serves the autonomous programs, both for the function of the hard and for development of the soft. The society has to be reasonable, to assure health and education for every human, and to encourage search and research for every conscient human.

FAULT MAINTENANCE IN EMBEDDED SYSTEMS APPLICATIONS - Multiple Lift Control System as Safety Critical Embedded Application

Miroslav Sveda and Radimir Vrba

This paper describes principles of a designed multiple lift control system based on a dedicated embedded architecture. After reviewing dependable concepts and design method used, the main attention is focused on the hardware architecture, software, and communication services and protocols fitting the application requirements. The multiple lift control system presents in this case a real-world solution of a safety critical embedded system application. The design employs fail-stop safety model and dedicated distributed architecture to meet application constraints efficiently. The paper stresses those features that distin-guish the real project from a demonstration case study.

A NEURAL NETWORK-BASED SENSOR FOR ELDER FALLING DETECTION

Jiann-I Pan, Cheng-Jie Yung and Chung Chao Liang

Falling down is going to be a crucial problem to an elder today. In many countries, unintentional injury was being one of the leading causes of death in persons over age 65 years. As many of the elder are live alone on their own and because of the isolation, it is necessary to design an intelligent and sensitive falling detector for the elderly people. In this paper, we present an intelligent and portable fall detection device based on artificial neural network technology. This fall detector consisted of two main components: accelerometer and micro controller. The tri-axis accelerometer is used to continuously measure the variation of elder’s 3 ways acceleration. The micro controller reads the signals from the accelerometer and then through a back-propagation neural network model to perform the fall activity recognition. This device is integrated in a small box which can be holding on the belt for elder.

DATA FLOW FORMALIZATION

Thouraya Bouabana-Tebibel

The Object Constraint Language OCL is an extension of the UML notation for the expression of restrictions over the static and dynamic diagrams. We propose to take advantage of its formal capabilities for validating whether the UML model matches with the system properties. For this purpose, we develop an approach based on Petri nets and temporal logics. This approach allows the integration of the temporal logic properties translated from the OCL invariants with the Petri nets obtained from the UML modeling. A case study is given throughout the paper to illustrate the approach..

A MODEL PREDICTIVE CONTROLLER BASED ON SUPPORT VECTOR REGRESSION AND GENETIC OPTIMIZATION FOR AN SP-100 SPACE NUCLEAR REACTOR

Man Gyun Na and Belle R. Upadhyaya

In this work, a model predictive control method combined with support vector regression, is applied to the design of the thermoelectric (TE) power control in the SP-100 space reactor. The future TE power is predicted by using the support vector regression. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives are subject to maximum and minimum control drum angle and maximum drum angle variation speed. The genetic algorithm is used to optimize the model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulations to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satisfying all control constraints.

DETERMINING ELLIPSOIDAL BASINS OF ATTRACTION OF FUZZY SYSTEMS

Carlos Ariño and Antonio Sala

This paper discusses how to obtain local stability results from a fuzzy system for which global ones cannot be obtained, basically due to infeasibility of some associated LMI problems. Two different approaches are compared: modifying the consequent models vs. setting up some relaxed LMI conditions if bounds on the memberships are known. Some examples are used to illustrate the approaches.

KEY PERFORMANCE INDICATORS IN PLANT-WIDE CONTROL

Sebastjan Zorzut, Vladimir Jovan and Alenka Žnidaršič

Abstract: To improve production performance it is necessary to define production goals with a proper implementation strategy and a suitable closed-loop control for their achievement. Closed-loop control structures for simple systems such as temperature or velocity control are well defined, but a synthesis of plant-wide control structures is still recognized as the most important design problem in production management in process industries. A crucial issue to be resolved is the translation of implicit operating objectives, such as minimization of production costs, to a set of measurable variables that can then be used in a feedback control. A promising solution is the use of the Key Performance Indicators (KPIs) approach. To verify the idea of production feedback control using production KPIs as referenced controlled variables, a procedural model of a production process for a polymerisation plant has been developed. The model has been used during a number of simulation runs performed with the aim of developing and verifying the idea of KPI-based production control.

MODELLING ADAPTIVE CONTROLLERS WITH EVOLVING LOGIC PROGRAMS

Pierangelo Dell'Acqua, Anna Lombardi and Luís Moniz Pereira

The paper presents the use of Evolving Logic Programming (EVOLP) to model adaptive controllers. The resulting adaptive logic-based control system can be seen in the general framework of model reference adaptive systems. Two case studies are illustrated: in the first case the controller is implemented by using EVOLP, while in the second case EVOLP is used for the supervisor module with a generic controller. The advantage of using well-defined, self-evolving logic-based controllers is that it is possible to model dynamic environments, and to formally prove systems' requirements.

ENCODING FUZZY DIAGNOSIS RULES AS OPTIMISATION PROBLEMS

Antonio Sala, Alicia Esparza, Carlos Ariño and Jose V. Roig

This paper discusses how to encode fuzzy knowledge bases for diagnostic tasks (i.e., list of symptoms produced by each fault, in linguistic terms described by fuzzy sets) as constrained optimisation problems. The proposed setting allows more flexibility than some fuzzy-logic inference rulebases in the specification of the diagnostic rules in a transparent, user-understandable way (in a first approximation, rules map to zeros and ones in a matrix), using widely-known techniques such as linear and quadratic programming.

ENHANCEMENT OF MANEUVERABILITY OF A POWER ASSIST OMNI-DIRECTIONAL WHEELCHAIR BY APPLICATION OF NEURO-FUZZY CONTROL

Kazuhiko Terashima, Juan Urbano and Hideo Kitagawa

For helping attendants of handicapped people and elderly people, a power assist system has been added to an Omni-directional Wheelchair (OMW). With this addition it is possible for the attendants to deal with heavy loads, but there is a problem of operability when the attendants want to easily move OMW laterally or rotate around OMW's Gravity Center (CG). To solve the present problem, this paper provides a fuzzy reasoning method for estimating the navigation direction according to the force added by the attendants to the handgrips of the handle of OMW. A neuro-fuzzy system (ANFIS) is used for auto-tuning of the membership functions of the fuzzy system according to each attendant's characteristics, by using input data of attendants.

AUTOMATIC GENERATION OF OPTIMAL CONTROLLERS THROUGH MODEL CHECKING TECHNIQUES

Giuseppe Della Penna, Daniele Magazzeni, Alberto Tofani, Benedetto Intrigila, Igor Melatti and Enrico Tronci

We present a methodology for the synthesis of controllers, which exploits \emph{(explicit) model checking techniques}. That is, we can cope with the systematic exploration of a very large state space.\\ This methodology can be applied to systems where other approaches fail. In particular, we can consider systems with an \emph{highly non-linear dynamics} and \emph{lacking a uniform mathematical description (model)}.\\ We can also consider situations where the required control action cannot be specified as a local action, and rather a kind of \emph{planning} is required.\\ Our methodology individuates first a raw optimal controller, then extends it to obtain a more robust one.\\ A case study is presented which considers the well known \emph{truck-trailer obstacle avoidance parking problem}, in a parking lot with \emph{obstacles} on it. The complex non-linear dynamic of the truck-trailer system, within the presence of obstacles, makes the parking problem extremely hard.\\ We show how, by our methodology, we can obtain optimal controllers with different degrees of robustness.

INFLUENCE OF NBMAX AND TABU LIST IN THE SCHEDULING PROBLEM

Antonio Gabriel Rodrigues and Arthur Tórgo Gómez

In this paper is proposed a computational model that considers the Part Selection Problem and the Job Shop Scheduling Problem in a Flexible Manufacturing Cell. The objective of the proposed model is generate a schedule which reflects the management of three components in a objective function: (i) tardiness time, (ii) number of setups and (iii) number of tool switches. Previous experiments identify the conflict between tardiness time and setup and tool switches numbers. To manage this conflict, new experiments were made, in which the Tabu Search parameters (nbmax and tabu list size) are variated. This variation influences the objective function results, allowing better results in minimization of tardiness time.

COMPUTATIONAL FRAMEWORK FOR POWER ECONOMIC DISPATCH USING GENETIC ALGORITHM

Tahir Nadeem Malik, Abdul Qudus Abbasi and Aftab Ahmad

Economic Dispatch Problem (EDP) is the important step in Power System operation and is non-convex optimization problem. It has been solved comprehensively with mathematical programming approaches. However, these approaches handle non-convexity with assumption and resulting in an inaccurate dispatch. Genetic algorithms are potential tools for Economic dispatch and can handle it effectively. Computational framework “PED_Frame” has been developed which can handle economic dispatch using classical optimization and Genetic Algorithm based approaches independently and in hybrid form. It has been tested on standard three machine and twenty machine test systems.

FUZZY LOGIC BASED UAV ALLOCATION AND COORDINATION

James F. Smith III and ThanhVu H. Nguyen

A fuzzy logic resource allocation algorithm that enables a collection of unmanned air vehicles (UAVs) to automatically cooperate will be discussed. The goal of the UAVs’ coordinated effort is to measure the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy logic based planning algorithm determines the optimal trajectory and points each UAV will sample, while taking into account the UAVs’ risk, risk tolerance, reliability, and mission priority for sampling in certain regions. It also considers fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV renders the UAVs autonomous allowing them to change course immediately without consulting with any commander, requests other UAVs to help, and change the points that will be sampled when observing interesting phenomena. Simulations show the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team’s likelihood of success.

AN OPTIMIZATION ALGORITHM TO IMPROVE SECURITY OF ELECTRICAL ENERGY SYSTEMS - An Hybrid Approach Based on Linear Programming and Load Flow Calculations

José V. Canto dos Santos, Arthur T. Gómez and Antônio G. Rodriguez

Power system restoration is one of the main problems in the electrical engineering area, due to the improving dependency of electricity of the modern industrial society. The restoration of large electrical power systems after the occurrence of serious blackouts is a complex problem where the basic goal is to obtain the system configuration in order to supply loads with different priorities. The restoration is done through stages and in each stage the service is restored to a predetermined set of loads. A method to solve the basic problem in a real power system restoration process is presented in this work. The solution takes into account the nonlinear electric network model (AC model) as well as its constraints and operational limits. The fictitious network concept is extended to the reactive model. Linear programming, a new model for the linearized power flow and conventional load flow calculation are also used. Results obtained with a test system and with a large realistic system are presented.

A SOLUTION TO THE VEHICLE ROUTING PROBLEM USING TABU SEARCH

Etiene Pozzobom Lazzeris Simas and Arthur Tórgo Gómez

This paper presents a model to the Vehicle Routing Problem using Tabu Search. The Vehicle Routing Problem aims to serve a set of clients by a fleet of vehicle through the creation of least-cost routes that satisfy some constraints. In this paper, only the vehicle capacity is considered. The objective of this model is to design least-cost routes to serve a set of clients with know demands in such way that some constraints that were defined are satisfied. An application to construct this model is proposed using Tabu Search. There were generated experiments that confirm that an increase in diversification on search space politics can generate results that are more qualified.

ON THE USE OF OPTIMIZATION METHODS FOR THE MINIMIZATION OF FERTILIZER APPLICATION ERROR WITH CENTRIFUGAL SPREADERS

Teddy Virin, Jonas Koko, Emmanuel Piron and Philippe Martinet

Fertilizer application is one of the most important operations in agricultural production. Thanks to their low cost and robustness, centrifugal spreaders are widely used to carry out this task. However, when distances between successive paths followed by the tractor in the field are not constant, application errors occur. These ones generally consist in over and under-application of nutrients. In the first case, over dosages can result in waters pollution. In the contrary case, economic issues occur with important yield losses. In this paper, to limit harmful environmental effects and disastrous drop in production due to centrifugal spreading, we propose an approach based on optimization techniques to improve the fertilization quality. An optimization criterion relying on a spatial distribution model, obtained in previous works, is considered. To compute optimal parameters which should be used as reference variables for the control of the spreader in the future, mechanical constraints are introduced. Faced with a large scale problem, we use an augmented lagrangian algorithm combined with a l-bfgs technique. Simulations results show low application error values comparing to fertilization inaccuracies found without optimization.

STABILITY OF TAKAGI-SUGENO FUZZY SYSTEMS

İlker Üstoğlu

Takagi-Sugeno (T-S) fuzzy models are usually used to describe nonlinear systems by a set of IF-THEN rules that gives local linear representations of subsystems. The overall model of the system is then formed as a fuzzy blending of these subsystems. It is important to study their stability or the synthesis of stabilizing controllers. The stability of TS models has been derived by means of several methods: Lyapunov approach, switching systems theory, linear system with modeling uncertainties, etc. In this study, the uniform stability, and uniform exponential stability of a discrete time T-S model is examined, a pointwise-in-time eigenvalue condition for exponential stability based on Rayleigh-Ritz inequality is presented. Moreover, a perturbation result and an instability condition is given. The subsystems of T-S models that is studied here are time varying and a new exponential stability theorem is given for these types of TS models by examining the existence of a common matrix sequence.

CONSIDERATIONS FOR SELECTING FUNCTIONS AND TERMINALS IN GENETIC PROGRAMMING FOR FAULT-DETECTION IN EMBEDDED SYSTEMS

Matej Šprogar, Domen Verber and Matjaž Colnarič

The article describes the terminals and functions used by genetic programming to discover specific parameters for fault-detection in embedded control systems design. Choice of different functions and terminals affects the convergence speed. The state of embedded controller is mapped into a space of valid/invalid points and genetic programming is used to divide the space into hypercubes that can be used to trivially recognize faults during system operation. The fault-detection logic operates by monitoring the input and output variables of the embedded controller. It is based on acquired and built-in knowledge about the normal behaviour in order to detect abnormalities. The fault-detection problem is approched by the use of monitoring cells, which implement the system supervising logic.

FEATURE SELECTION FOR IDENTIFICATION OF SPOT WELDING PROCESSES

Eija Haapalainen, Perttu Laurinen, Heli Junno, Lauri Tuovinen and Juha Röning

Process identification in the field of resistance spot welding can be used to improve welding quality and to speed up the set-up of a new welding process. Previously, good classification results of welding processes have been obtained using a feature set consisting of $54$ features extracted from current and voltage signals recorded during welding. In this study, the usability of the individual features is evaluated and various feature selection methods are tested to find an optimal feature subset to be used in classification. Ways are sought to further improve classification accuracy by discarding features containing less classification-relevant information. The use of a small feature set is profitable in that it facilitates both feature extraction and classification. It is discovered that the classification of welding processes can be performed using a substantially reduced feature set. In addition, careful selection of the features used also improves classification accuracy. In conclusion, selection of the feature subset to be used in classification notably improves the performance of the spot welding process identification system.

DYNAMIC GOAL COORDINATION IN PHYSICAL AGENTS

Jose Antonio Martin H and Javier de Lope

A general framework for the problem of coordination of multiple competing goals in dynamic environments for physical agents is presented. This approach to goal coordination is a novel tool to incorporate a deep coordination ability to pure reactive agents. The framework is based on the notion of multi-objective optimization. We propose a kind of “aggregating functions” formulation with the particularity that the aggregation is weighted by means of a dynamic weighting unitary vector w(S) which is dependant from the system dynamic state allowing the agent to dynamically coordinate the priorities of it’s single goals. This dynamic weighting unitary vector is represented as a n ? 1 set of angles. The dynamic coordination must be established by means of a mapping between the state of the agent’s environment S to the set of angles ?i (S) by means of any sort of machine learning tool. In this work we investigate the use of Reinforcement Learning as a first approach to learn that mapping.

NEURAL NETWORK MODEL BASED ON FUZZY ARTMAP FOR FORECASTING OF HIGHWAY TRAFFIC DATA

D. Boto-Giralda, M. Antón-Rodríguez, F. J. Díaz -Pernas and J. F. Díez Higuera

In this article, a neural network model is presented for forecasting the average speed values at highway traffic detectors locations using the Fuzzy ARTMAP theory. The performance of the model is measured by the deviation between the speed values provided by the loop detectors and the predicted speed values. Different Fuzzy ARTMAP configuration cases are analysed in their training and testing phases. Some ad-hoc mechanisms added to the basic Fuzzy ARTMAP structure are also described to improve the entire model performance. The achieved results make this model suitable for being implemented on advanced traffic management systems (ATMS) and advanced traveller information system (ATIS).

A FUZZY APPROACH FOR FAULT DETECTION AND ISOLATION OF UNCERTAIN PARAMETER SYSTEMS AND COMPARISON TO BINARY LOGIC

Salma Bouslama Bouabdallah and Moncef Tagina

This paper deals with fault detection and isolation off-line affecting sensors and actuators of uncertain parameter systems modelled by bond graph. A fuzzy approach for fault detection based on residual fuzzification is proposed. Besides, an isolation method based on fuzzy processing of the detection results is proposed. Finally binary approach and fuzzy one are compared through an illustrative example.

MATRIX-BASED HIERARCHICAL FUZZY SYSTEMS

Santiago Aja-Fernández and Carlos Alberola-López

A matrix inference method for fuzzy systems is used to deal with hierarchical fuzzy systems (HFSs). A method to decompose a multiple input fuzzy system into a HFS is presented. This method is based in representing the structure of a fuzzy system using matrices. An example of such a conversion for a three-input system is included.

FINDING A COMMON QUADRATIC LYAPUNOV FUNCTION USING CONICAL HULLS

Rianto Adhy Sasongko and J. C. Allwright

Consider a set of linear time-invariant continuous-time systems that is a convex hull with vertices formed by a given set of systems. The problem of finding a common Lyapunov function $v$, specified in terms of a symmetric positive definite matrix, for the convex hull of systems is tackled by searching for a symmetric positive definite (PD) matrix $P$ which causes $d\mrm{v} \slash dt$ to be negative definite for each vertex system. The approach involves an extension of an existing method for solving optimization problems for positive semidefinite (PSD) matrices that is based on a representation of the cone of PSD matrices as a conical hull. The condition that the derivative of the Lyapunov function for each vertex system is negative definite is converted naturally into the condition that the matrix $P$ belongs to the interior of the intersection of several conical hulls: one for each vertex system to ensure $d\rm{v}\slash dt$ for it is negative definite. The determination of a $P$ in the intersection is viewed as the solution of a quadratic programme on the product space of the cones. Then the existing theory and algorithms for conical hull problems are adapted to the solution of the quadratic programme. Initial numerical results indicate that the new approach appears to be about $26\%$ slower than the projective method used by MATLAB however the times for the new approach have been obtained using MATLAB scripts whereas MATLAB's projective algorithm was used as a executable file obtained from C. One would expect, therefore, that the algorithms should be competitive after the scripts have been converted into such executable files.

HYBRID LEARNING METHOD FOR DISCRETE MANUFACTURING CONTROL USING KNOWLEDGE BASED MODEL

Ewa Dudek and Tadeusz Dyduch

A conception of a hybrid learning method for discrete manufacturing processes control is presented. The method is based on a special form of a knowledge based model of discrete manufacturing process, named here hybrid knowledge based model (HKBM). The model consists of two parts, each of a different type of model: algebraic-logical model in a state spacethat is created on a basis of process technology description and set of expert rules referring to control. A general scheme of HKBM of a vast class of discrete manufacturing processes (DMP) is given in the paper. Then the method of synthesis of intelligent, learning algorithms that use information on the process gained in previous iterations as well as an expert knowledge is described. To illustrate the presented ideas, the scheduling algorithm for a special NP-hard problem is given.

DISTRIBUTED CONTROL SYSTEMS BASED ON COTS COMMUNICATION DATA BUS

Martin Švéda, István Szabó and Vladimír Opluštil

This paper deals with the distributed commercial off the shelf (COTS) databus based on Controller Area Network (CAN) used as a communication databus for Autonomous Locomotion Robot (ALR) and a System of Avionics Modules (SAM) that is used in civil aircraft Ae270. This article describes main characteristics of CAN communication databus and its higher layer protocol CANaerospace that are used in communication system of ALR and SAM. The basic idea of distributed control systems are described and their main characteristics are presented. Developed control systems proved that the CAN with HLP CANaerospace is eficient and reliable communication databus that can be used in safety critical applications like mobile robots, automotive, and avionics systems.

PREEMPTIVE SCHEDULING IN A TWO-STAGE MULTIPROCESSOR FLOWSHOP WITH RESOURCE CONSTRAINTS

Ewa Figielska

A heuristic combining the column generation technique and a genetic algorithm is proposed for solving the problem of preemptive scheduling in a two-stage flowshop with parallel unrelated machines and renewable resources at the first stage and a single machine at the second stage. The objective is to minimize the makespan. The lower bound on the optimal makespan is derived to be used in the performance analysis of the heuristic. The performance of the heuristic is analyzed by a computational experiment. The results show that the heuristic is able to find near-optimal solutions in reasonable computation time.

DISTRIBUTED EMERGENCY MANAGEMENT WITH SPATIAL SCENARIOS

Peter Sapaty, Robert Finkelstein and Joaquim Filipe

A radically new approach will be described for the fully distributed and dynamic management of advanced crisis relief operations and missions. It is based on the installation of a universal “social” module in many existing and massively used data processing and control devices, including (but not limited to) internet hosts, laptops, mobile robots and mobile phones. These modules can collectively interpret a special scenario language while exchanging higher-level program code with accompanying data and control in parallel. This can dynamically integrate any scattered post-disaster human and technical resources into an operable distributed system which, from one side, is effectively supervised externally, and from the other side, is capable of solving complex self-analysis, coordination, survivability, relief, and reconstruction problems autonomously.

NEURAL NETWORK SYSTEM FOR WASTE-WATER RECOGNITION

Radek Kuchta and Radimir Vrba

This paper presents modern method of using neural network for waste-water recognition by using sensor array. Each sensor in sensor array detects chemicals in waste-water with different sensitivity. Set of measured data is digitized and recognized by a neural network. Measuring process doesn’t need any human operator. The result gives the only information: contaminated or not contaminated

HEAT-AND-POWER PROCESSES OPTIMIZATION BY MEANS OF MODEL-BASED SIMULATION

Dmitry Antropov, Rosica Ivanova, Renat Sadykov, Sergey Yeryomin and Rauf Kafiatullin

A firmware was developed for simulation of heat-mass transfer processes in power equipment such as steam or water boilers and dryers. Hardware of this pilot plant is based on modern microprocessors control devices. Software rests on specially developed mathematical models. The functions and structure of the model of fully automated boiler and dryer control system (B-DCS) are described in detail. One of the variants of implementation of B-DCS on the example of the dryer unit for drying bioactive products is considered. The analysis of the optimality criterion problem and selection of the optimal control structure are reviewed using Pontryagin’s maximum principle. The objective of optimization is to reduce expenditures of operational process.

HYBRID EVOLUTIONARY COMPUTATIONS - Application for Industry Investment Problem

Tadeusz Dyduch

The aim of the paper is two-fold. Firstly, it is to present in more details a special type of hybrid evolutionary computation, named by the authors Two-Level Adaptive Evolutionary Computation (TLAEC). The method consists in combination of evolutionary computation with deterministic optimization algorithms in a hierarchical system. Novelty of the method consists also in a new type of adaptation mechanism. Post optimal analysis of the lower level optimization task is utilized in order to modify probability distributions for new genotype generating. The second aim of the paper is to present an algorithm based on TLAEC method, solving a difficult optimization problem. A mathematical model of this problem assumes the form of mixed discrete-continuous programming. A concept of the algorithm is described in the paper and the proposed, new adaptation mechanism that is implemented in the algorithm is described in detail. The results of computation experiments as well as their analysis are also given.

THE HIERARCHICAL MAP FORMING MODEL

Luis Eduardo Rodriguez Soto and Cheng-Yuan Liou

In the present paper we propose a motor control model inspired by organizational priciples of the cerebral cortex. Speci…cally the model is based on cortical maps and functional hierarchy in sensory and motor areas of the brain. Self-Organizing Maps (SOM) have proven to be useful in modeling cortical topological maps (Palakal et al., 1995). A hierarchical SOM provides a natural way to extract hierarchical informa- tion from the environment, which we propose may in turn be used to select actions hierarchically. We use a neighborhood update version of the Q-learning algorithm, so the …nal model maps a continuous input space to a continuous action space in a hierarchical, topology preserving manner. The model is called the Hierarchical Map Forming model (HMF) due to the way in which it forms maps in both the input and output spaces in a hierarchical manner.

DETECTING LICENSE PLATE USING CLUSTER RUN LENGTH SMOOTHING ALGORITHM

Siti Norul Huda Sheikh Abdullah, Marzuki Khalid, Rubiyah Yusof and Khairuddin Omar

Vehicle license plat recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image processing library is developed in-house which we referred to as Vision System Development Platform (VSDP). The Kirsch Edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of about 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.

N-ARY TREES CLASSIFIER

Duarte Duque, Henrique Santos and Paulo Cortez

This paper addresses the problem of automatic detection and prediction of abnormal human behaviours in public spaces. For this propose a novel classifier, called N-ary trees, is presented. The classifier processes time series of attributes like the object position, velocity, perimeter and area, to infer the type of action performed. This innovative classifier can detect three types of events: normal; unusual; or abnormal events. In order to evaluate the performance of the N-ary trees classifier, we carry out a preliminary study with 180 synthetic tracks and one restricted area. The results revealed a great level of accuracy and that the proposed method can be used in surveillance systems.

VISUAL TOPOLOGICAL MAP BUILDING IN SELF-SIMILAR ENVIRONMENTS

Toon Goedemé, Tinne Tuytelaars and Luc Van Gool

This paper describes a method to automatically build topological maps for robot navigation out of a sequence of visual observations taken from a camera mounted on the robot. This direct non-metrical approach relies completely on the detection of loop closings, i.e. repeated visitations of one particular place. In natural environments, visual loop closing can be very hard, for two reasons. Firstly, the environment at one place can look differently at different time instances due to illumination changes and viewpoint differences. Secondly, there can be different places that look alike, i.e. the environment is self-similar. Here we propose a method that combines state-of-the-art visual comparison techniques and evidence collection based on Dempster-Shafer probability theory to tackle this problem.

LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization

Adam Milstein and Tao Wang

Localization is the problem of determining a robot’s location in an environment. Monte Carlo Localization (MCL) is a method of solving this problem by using a partially observable Markov decision process to find the robot’s state based on its sensor readings, given a static map of the environment. MCL requires a model of each sensor in order to work properly. One of the most important sensors involved is the estimation of the robot’s motion, based on its encoders that report what motion the robot has performed. Since these encoders are inaccurate, MCL involves using other sensors to correct the robot’s location. Usually, a motion model is created that predicts the robot’s actual motion, given a reported motion. The parameters of this model must be determined manually using exhaustive tests. Although an accurate motion model can be determined in advance, a single model cannot optimally represent a robot’s motion in all cases. With a terrestrial robot the ground surface, slope, motor wear, and possibly tire inflation level will all alter the characteristics of the motion model. Thus, it is necessary to have a generalized model with enough error to compensate for all possible situations. However, if the localization algorithm is working properly, the result is a series of predicted motions, together with the corrections determined by the algorithm that alter the motions to the correct location. In this case, we demonstrate a technique to process these motions and corrections and dynamically determine revised motion parameters that more accurately reflect the robot’s motion. We also link these parameters to different locations so that area dependent conditions, such as surface changes, can be taken into account. These parameters might even be used to identify surface changes by examining the various parameters. By using the fact that MCL is working, we have improved the algorithm to adapt to changing conditions so as to handle even more complex situations.

HIERARCHICAL MULTI-ROBOT COORDINATION - Aggregation Strategies Using Hybrid Communication

Yan Meng, Jeffrey V. Nickerson and Jing Gan

Multi-robot coordination is important for searching tasks. Usually discussions of this coordination presuppose a reliable explicit communication infrastructure. However, limited power, low radio range, and an ever changing environment all hinder communication. Maintaining weakened connections will cause robots to cluster during searching, which may be suboptimal with respect to the searching time. In this paper, several integration strategies with a hierarchical networked architecture are proposed to coordinate a team of robots which have lost explicit communication. To speed up the reconnection procedure for the proposed aggregate strategies, implicit communication through vision sensors is proposed in this paper to establish a movement plan to recover the explicit communication. Simulation results are presented and discussed. Experiments with 3 Pioneer robots have been conducted, and the experimental results show that our proposed strategies using a hybrid communication mechanism are feasible and efficient in a searching task. The proposed strategies can be extended to a large-scale searching environment as well as to a combination of humans and robots.

STUDIES ON VISUAL PERCEPTION FOR PERCEPTUAL ROBOTICS

Özer Ciftcioglu, Michael S. Bittermann and I. Sevil Sariyildiz

Studies on human visual perception measurement for perceptual robotics are described. The visual perception is mathematically modelled as a probabilistic process obtaining and interpreting visual data from an environment. The measurement involves visual openness perception in the virtual reality which has direct implications for navigation issues of actual autonomous robotics. The perception is quantified by means of a mapping function which converts a distance to an elemental perception estimate. The measurement is carried out with the averaging of the elemental perceptions in real time. This is accomplished by means of exponential averaging. The mapping function parameters are optimized uniquely by means of genetic algorithm approach where the data set for model development consists of a number of perception data samples. These are obtained from individuals who are confronted with a number of scenes and asked for their perceptual openness statements. Based on this data, a perception model is developed for a robot where the simulated vision interaction of the robot with the environment is converted to visual openness estimation through the model output. The model outcome is essential visual information for the navigation of an autonomous perceptual robot.

VISUAL SPEECH RECOGNITION USING WAVELET TRANSFORM AND MOMENT BASED FEATURES

Wai C. Yau, Dinesh K. Kumar, Sridhar P. Arjunan and Sanjay Kumar

This paper presents a novel approach using feature extraction that combines Discrete StationaryWavelet Transform (SWT) and image moments to classify utterances consisting of consonants. A view based method is adopted to represent the 3-D image sequence of the mouth movement in a 2-D space using grayscale images named as motion history image (MHI). MHI is produced by applying accumulative image differencing technique on the sequence of images to implicitly capture the temporal information of the mouth movement. A 2-D SWT at level 1 is applied to decompose MHI to produce one approximate and three detail sub images. Three different moment-based features, namely Zernike moments, geometric moments and Hu moments are computed from the approximate representation of MHI to form the feature vectors. Supervised feed forward multilayer perceptron (MLP) artificial neural network (ANN) with back propagation learning algorithm is used to classify the moment-based features. The performance and image representation ability of these features are compared in this paper. The preliminary results show that this method can achieve high recognition rate in classification of 3 consonants.

LOCALITY AND GLOBALITY: ESTIMATIONS OF THE ENCRYPTION COLLECTIVITIES

Cristian Lupu, Tudor Niculiu and Eduard Franţi

In this paper we try to define a collectivity, to model and to measure it. Because N. Bourbaki names "collectivizing relation" the relation defining a set, we name collectivities only the sets selected or built by the help of the relations. The orthogonal interconnections model very well the collectivities. The behavior (structural self-organization) around the origin is different for homogenous and non-homogenous interconnections. How can we measure this behavior? A way is by locality and globality. The locality measures analytically by neighborhoods, neighborhood reserves, {\it Moore} reserves and synthetically by diameters, degrees, average distances. The globality is the behavior of an interconnection around a property. The globality vs. symmetry measures by the compactity, efficiency and interconnecting filling. The locality and the globality are among primary manifestations of the self-organization. In this way, collectivities modeled by self-organizing interconnections can contribute to changing our fundamental view of computers by trying to bring them nearer to the nature.

ONLINE HIERACHICAL CONTROL FOR LEGGED SYSTEMS BASED ON THE INTERACTION FORCES

José R. Puga, Filipe M. Silva and Boaventura R. da Cunha

This paper presents a motion planning and control method with application in the field of legged robots. The general aim is to explore a set of simple underlying principles that govern balance of posture and gait of biped robots, and to develop control methodologies for such a highly unstable and no linear plants. The proposed controller reflects a hierarchical structure based on the interaction forces between the foot and ground and simple feedback rules used online. The algorithms are applied to a simulated 3-D leg model with five degrees of freedom (DOF). The simulation analyses demonstrate the capability of the control system to keep balance when the leg executes different tasks. To validate the proposed method several aspects are investigated, such as the posture robustness on the level ground when subject to external perturbations, the adaptation when standing in a moving platform and the improvements introduced by the compensation of the tangential reaction forces.

DIFFERENT CLASSIFIERS FOR THE PROBLEM OF EVALUATING CORK QUALITY IN AN INDUSTRIAL SYSTEM

Beatriz Paniagua-Paniagua, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido and Juan M. Sánchez-Pérez

In this paper we study the use of different classifiers to solve a classification problem existing in the cork industry: the cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous material, therefore, its automatic classification (usually, seven different quality classes exist) is very difficult. The classifiers, which we present in this paper, work with several quality discriminators (features), that we think could influence cork quality. These discriminators (features) have been checked and evaluated before being used by the different classifiers that will be exposed here. In this paper we attempt to evaluate the performance of a total of 4 different cork quality-based classifiers in order to conclude which of them is the most appropriate for this industry, and therefore, obtains the best cork classification results. In conclusion, our experiments show that the Euclidean classifier is the one which obtains the best results in this application field.

IMPROVING TRACKING TRAJECTORIES WITH MOTION ESTIMATION

Jorge Pomares, Gabriel J. García and Fernando Torres

Up to now, different methods have been proposed to track trajectories using visual servoing systems. However, when these approaches are employed to track trajectories specified with respect to moving objects, different considerations must be included in the visual servoing formulation to progressively decrease the tracking error. This paper shows the main properties of a non-time dependent visual servoing system to track image trajectories. The control action obtained integrates the motion estimation of the object from which the features are extracted. The proposed motion estimator employs information from the measures of the extracted features and from the variation of the camera locations. These variations are obtained determining the Homography matrix between consecutive camera frames.

USING THE TRANSFERABLE BELIEF MODEL TO VEHICLE NAVIGATION SYSTEM

Touil Khalid, Zribi Mourad and Benjelloun Mohammed

In general, navigation systems estimating a vehicle position is done either by using the Global Positioning System (GPS) or the Dead Reckoning (DR) systems. Other modern estimations are based on the combination of the two systems (GPS/DR). However, the position of a vehicle determined by GPS/DR is far from being perfect since it produces many errors. To solve this problem, a map-matching method is proposed in order to reduce the errors of localization caused by GPS/DR. This algorithm, which uses a digital road map, allows the detection of the correct road where a vehicle moves. In this paper, we introduce a new map-matching algorithm that employs the Transferable Belief Model (TBM). The TBM presents a general justification of belief theory and provides a flexible and adapted representation for the measured beliefs. Experimental results show the effectiveness of the utilization of the TBM to the vehicle navigation system.

SIMULTANEOUS LOCALIZATION AND MAPPING IN UNMODIFIED ENVIRONMENTS USING STEREO VISION

A. Gil, O. Reinoso, C. Fernández, M. A. Vicente, A. Rottmann and O. Martínez Mozos

In this paper we describe an approach that builds three dimensional maps using visual landmarks extracted from images of an unmodified environment. We propose a solution to the Simultaneous Localization and Mapping (SLAM) problem for autonomous mobile robots using visual landmarks. Our map is represented by a set of three dimensional landmarks referred to a global reference frame, each landmark containing a visual descriptor that partially differentiates it from others. Significant points extracted from stereo images are used as natural landmarks, in particular we employ SIFT features found in the environment. We estimate both the map and the path of the robot using a Rao-Blackwellized particle filter, thus the problem is decomposed into two parts: one estimation over robot paths using a particle filter, and N independent estimations over landmark positions, each one conditioned on the path estimate. We actively track visual landmarks at a local neighbourhood and select only those that are more stable. When a visual feature has been observed from a significant number of frames it is then integrated in the filter. By this procedure, the total number of landmarks in the map is reduced, compared to prior approaches. Due to the tracking of each landmark, we obtain different examples that represent the same natural landmark. We use this fact to improve data association. Finally, efficient resampling techniques have been applied, which reduces the number of particles needed and avoids the particle depletion problem.

A SOLUTION FOR EVALUATING THE STOPPER QUALITY IN THE CORK INDUSTRY

Beatriz Paniagua-Paniagua, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido and Juan M. Sánchez-Pérez

In this paper we study a possible solution to a problem existing in the cork industry: the cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous material, therefore, its automatic classification (usually, seven different quality classes exist) is very difficult. The solution proposed in this paper shows all the stages made in our study: quality discriminatory features selection and extraction, texture analysis, analysis of different (global and local) automatic thresholding techniques and possible classifiers. In each stage we have given more importance to the study of those aspects that we think could influence the cork quality. In this paper we attempt to evaluate each of the stages in our solution to the problem of the cork classification in an industrial environment, and therefore, finding a way to justify the design of our final classification system. In conclusion, our experiments show that the best results are obtained by a system that works with the following features: total cork area occupied by defects (thresholding with heuristic fixed value 69), textural contrast, textural entropy and size of the biggest defect in the cork, all of them working in an Euclidean classifier. The obtained results have been very encouraging.

DYNAMIC PARAMETERS IDENTIFICATION OF AN OMNI-DIRECTIONAL MOBILE ROBOT

André Scolari Conceição, A. Paulo Moreira and Paulo J. Costa

This paper presents the experimental dynamic parameters identification of an omni-directional mobile robot with four wheels. Three methods of parameters identification related to dynamic equations are described, the parameters are the viscous frictions, the coulomb frictions and the inertia moment of the robot. A simulation environment, simulation results and real results are presented.

TRAJECTORY CONTROL AND MODELLING OF AN OMNI-DIRECTIONAL MOBILE ROBOT

André Scolari Conceição, A. Paulo Moreira and Paulo J. Costa

This paper presents a dynamic and kinematic model and a trajectory controller for an omni-directional mobile robot. The parameters of the controller are optimizated based on trajectory following simulations, with the mobile robot model, take into account aspects like time and errors of position and orientation of the robot. Simulation and real results of trajectory following are presented.

FAULT DETECTION OF THE ACTUATOR BLOCKING - Experimental Results in Robot Control Structures

Matei Vinatoru and Eugen Iancu

In this paper is presented an algorithm, which allows for certain robotic structure, under the terms of an actuator blocking occurrence during the operation, either a correct positioning (if it is possible) or a positioning in an acceptable proximity of the desired co-ordinates by minimising an optimal criteria (through the adequate commands to the functional elements). The paper is proposing the synthesis of the commands to a poly-articulated robotic arm (3 segments). First, a workspace analysis is made, then is presented the algorithm for the actuators, first in the terms of a normal operation (finding the optimal motions) and second in terms of the blocking of some robotic segments.

TWO LAYER CONTROL STRATEGY APPLIED TO BUILDING AUTOMATION

João Figueiredo and José Sá da Costa

In this paper a control and monitoring platform for an intelligent building is developed using a SCADA system (Supervisory Control And Data Acquisition). The control strategy develops a two-level architecture where inner-loops are performed by local PLCs (Programmable Logic Controller), and the outer-loop is managed by the centralized SCADA system that interacts with the entire local PLC network. The outer loop performs an intelligent Generalized Predictive Control strategy (GPC). Tests on a prototype are shown, where all the instrumentation in place is controlled by an industrial PLC master/slave network. The master PLC is connected, in real-time acquisitions, with the SCADA system via MultiPoint Interface (MPI). The experimental work shows the potential of instrumentation, control and monitoring in the future buildings to prevent accidents and improve the quality of living.

A GAIN-SCHEDULING APPROACH FOR AIRSHIP PATH-TRACKING

Alexandra Moutinho and José Raul Azinheira

In this paper a gain scheduled optimal controller is designed to solve the path-tracking problem of an airship. The control law is obtained from a coupled linear model of the airship that allows to control the longitudinal and lateral motions simultaneously. Due to the importance of taking into account wind effects, which are rather important due to the airship large volume, the wind is included in the kinematics, and the dynamics is expressed as function of the air velocity. Two examples are presented with the inclusion of wind, one considering a constant wind input and the other considering in addition a 3D turbulent gust, demonstrating the effectiveness of this single controller tracking a reference path over the entire flight envelope.

DEPTH GRADIENT IMAGE BASED ON SILHOUETTE - A Solution for Reconstruction of Scenes in 3D Environments

Pilar Merchán, Antonio Adán and Santiago Salamanca

Greatest difficulties arise in 3D environments when we have to deal with a scene with dissimilar objects without pose restrictions and where contacts and occlusions are allowed. This work tackles the problem of correspondence and alignment of surfaces in such a kind of scenes. The method presented in this paper is based on a new representation model called Depth Gradient Image Based on Silhouette (DGI-BS) which synthesizes object surface information (through depth) and object shape information (through contour). Recognition and pose problems are efficiently solved for all objects of the scene by using a simple matching algorithm in the DGI-BS space. As a result of this the scene can be virtually reconstructed. This work is part of a robot intelligent manipulation project. The method has been successfully tested in real experimentation environments using range sensors.

A NEW METHOD FOR REJECTION OF UNCERTAINTIES IN THE TRACKING PROBLEM FOR ROBOT MANIPULATORS

Juan A. Méndez, S. Torres, L. Acosta, E. González and V. M. Becerra

This paper presents a new strategy for robust tracking in robot manipulators. The aim of the strategy is to reject parametric uncertainties due to model or load disturbances. The basic controller acting on the manipulator is a robust controller designed by Lyapunov’s direct method. Acting on this controller there is an adaptive system responsible for the adaptation of the basic parameter of the robust feedforward term. The paper describes in detail the theoretical setup of the proposed method. The performance of the strategy is tested in a Puma-560 manipulator. A comparison with existing techniques is done to verify the efficiency of the presented controller

AUTONOMOUS BEHAVIOR-BASED EXPLORATION OF OFFICE ENVIRONMENTS

Daniel Schmidt, Tobias Luksch, Jens Wettach and Karsten Berns

Besides safe motion control the gain of environmental knowledge is a key for a reliable home or office service robot. When being set into a completely unknown environment the robot has to be able to derive a certain abstract internal representation of this world without any user interaction. This knowledge enables the robot to known how to get from its actual place in one room to a target position in another room as a prerequisite transportation tasks for example. In this context, the combination of a behavior-based motion control system and an abstract topological map based on geometric representations of rooms seems promising. As the concept of motion and exploration behaviors facilitates to compete with noisy sensor information and geometrically imprecise maps, it has been used to develop exploration strategies for deriving topological representations of common indoor environments completely autonomously. The only prescribed world knowledge is the fact that these environments are composed of rectangular entities (rooms) which are connected by openings (doors). The developed system has successfully been tested in simulation and reality. Next steps concern the integration of furniture objects into the map as well as increasing the reliability of the mapping strategy in highly cluttered areas.

AUTONOMOUS GAIT PATTERN FOR A DYNAMIC BIPED WALKING

Christophe Sabourin, Kurosh Madani and Olivier Bruneau

In this paper, we propose an autonomous gait pattern for a dynamic biped walking. Our approach takes simultaneously advantage from a Fuzzy-CMAC based computation of robot's swing leg's desired trajectory and a high level control strategy allowing regulating the robot's average velocity. The main interest of this approach is to proffer to the walking robot autonomy and adaptability involving only one parameter: the average velocity. Furthermore, this approach allows increasing the robustness of the walking robot regarding the forwards pushed force.

DISTRIBUTED CONTROL SYSTEM OF AN EXPERIMENTAL ROBOTIC CELL WITH 3D VISION

Andrés S. Vázquez, Antonio Adán, Roberto Torres and Carlos Cerrada

We present a distributed control architecture for the integration of an experimental robotic cell with 3D visual servoing. This architecture allows us to control a 6 DOF robot in hard Real-Time and the global experimental system in soft Real-Time. We have developed distributed applications, based on this architecture, for the robot control (whose characteristics permit us to teleoperate the robot), the 3D data acquisition and for an advanced simulation and visualization. These applications, together with the algorithms developed by our computer vision research group, allow a full intelligent robotic manipulation in complex scenes to be made. This can be useful in manufacturing environments where an automated piece manipulation is necessary.

STATIC FACE DETECTION AND EMOTION RECOGNITION WITH FPGA SUPPORT

Paul Santi-Jones and Dongbing Gu

Throughout history, spoken language and face-to-face communication have been the primary mechanics of interaction between two or more people. While speech processing, it is often advantageous to determine the emotion of the speaker in order to better understand the context of the meaning. This paper looks at our current effort at creating a static based emotion detection system, using previously used techniques along with an FPGA neural network (FFP) to speed up recognition rates.

DESIGN OF A PROTOTYPE ROBOT VACUUM CLEANER - From Virtual Prototyping to Real Development

Leire Maruri, Ana Martinez-Esnaola, Joseba Landaluze, Sergio Casas and Marcos Fernandez

This paper presents the prototype of a robot vacuum cleaner designed and constructed by IKERLAN. It details, above all, the hardware and software components used, as well as the navigation algorithm, designed using fuzzy logic. In conjunction to this an existing virtual prototype of the robot and the domestic environment was updated with a view to fine-tuning and testing the real controller of the autonomous robot by means of SIL (Software-in-the-Loop) simulations. Finally, some of the position estimation problems that arose in the experimental tests are described.

MULTIPLE MOBILE ROBOTS MOTION-PLANNING: AN APPROACH WITH SPACE-TIME MCA

Fabio M. Marchese

In this paper is described a fast Path-Planner for Multi-robot composed by mobile robots having generic shapes and sizes (user defined) and different kinematics. We have developed an algorithm that computes the shortest collision-free path for each robot, from the starting pose to the goal pose, while considering their real shapes, avoiding the collisions with the static obstacles and the other robots. It is based on a directional (anisotropic) propagation of attracting potential values in a 4D Space-Time, using a Multilayered Cellular Automata (MCA) architecture. This algorithm makes a search for all the optimal collision-free trajectories following the minimum valley of a potential hypersurface embedded in a 5D space.

ELECTRONIC SOLUTION BASED ON MICRO-CONTROLLER AT91SAM7S256 FOR PLATOONING MULTI-AGENT SYSTEM IMPLEMENTATION

José M. Rodríguez, AbdelBaset M.H. Awawdeh, Felipe Espinosa, Julio Pastor, Fernando Valdés, Miguel A. Ruiz and Antonio Gil

In this work a low cost electronic solution adapted for control and communication of a convoy of electrical vehicle prototypes based on multi-agent system -MAS- is presented. From the obtained results in previous works, focused on mobile platforms with PC architecture and Bluetooth communication module, a new electronic system has been designed based on the 32 bits microcontroller AT91SAM7S256 and the communication module nRF2401A. With which, it obtains a greater integration of the final mobile prototype and a greater communication capability between the devices connected in wireless network.

NEURO-ADAPTIVE DYNAMIC CONTROL FOR TRAJECTORY TRACKING OF MOBILE ROBOTS

Marvin K. Bugeja and Simon G. Fabri

This paper presents a novel functional-adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed in discrete-time and employs a Gaussian radial basis function neural network for the estimation of the robot's nonlinear dynamic functions, which are assumed to be completely unknown. Optimal on-line weight tuning is achieved by employing the Kalman filter algorithm, based on a specifically formulated stochastic inverse dynamic identification model of the mobile base. A discrete-time dynamic control law employing the estimated functions is proposed and cascaded with a trajectory tracking kinematic controller. The performance of the complete system is analysed and compared by realistic simulations.

Dynamic and Distributed Allocation of Resource Constrained Project Tasks to Robots

Sanem Sariel, Tucker Balch and Nadia Erdogan

In this paper we present the design and implementation of a multi-robot cooperation framework to collectively execute inter-dependent tasks of an overall complex mission requiring diverse capabilities. Given a heterogeneous team of robots and task dependencies, the proposed framework provides a distributed mechanism for assigning tasks to robots in an order that efficiently completes the mission. The approach is robust to unreliable communication and robot failures. It is a distributed auction-based approach, and therefore scalable, but it does not provide guarantees of optimality. In order to obtain optimal allocations effective bid evaluations are needed. Additionally to maintain optimality in noisy environments dynamic re-allocations of tasks are needed as implemented in dynamic task selection and coalition maintenance scheme that we propose. Real-time contingencies are handled by recovery routines, called Plan B precautions in our framework. Here, in this paper, we present performance results of our framework for robustness in simulations that include variable message loss rates and robot failures. Experiments illustrate robustness of our approach against several contingencies.

A STUDY ON ASR/TTS SERVER ARCHITECTURE FOR NETWORK ROBOT SYSTEM

In-Ho Choi and Tae-Hoon Kim

“The URC(Ubiquitous Robotic Companion, Server computer-based networked robotic)” systems exploiting Internet-related technologies and Server computer require effective techniques for timely delivery of requested data to remote clients. In these systems, there is a need to process real-time data in server computer from/to robots and clients during system operation. In this paper, we describe and evaluate ASR, TTS server systems in the context of a real-time environment for the URC applications. Experimental results show that the server-based ASR, TTS support timely delivery of data to a potentially large number of robots during system operation.

STEREO DISPARITY ESTIMATION USING DISCRETE ORTHOGONAL MOMENTS

Tomasz Andrysiak and Michał Choraś

In the article we present various theoretical and experimental approaches to the problem of stereo matching and disparity estimation. We propose to calculate stereo disparity in the moments space, but we also present numerical and correlation based methods. In order to calculate disparity vector we decided to use discrete orthogonal moments of Tchebichef, Zernike and Legendre. In our research of stereo disparity estimation all of these moments were tested and compared. In the article we also propose the original method of determining the global displacement vector between the stereopair images in order to find the common part of these images (adequate for matching) and the margins of these stereo images. Experimental results confirm effectiveness of the presented methods of determining stereo disparity and stereo matching for robotics and machine vision applications.

THE VISIBILITY PROBLEM IN VISUAL SERVOING

C. Pérez, R. Morales, N. García-Aracil, J. M. Azorín and J. M. Sabater

This paper deals with the visibility problems occurring during the execution of a visual servoing task. First, a review of the scientific works related with the visibility are recalled and then the solution proposed by the authors is presented and extended to the case of the sudden disappearance of features on the center of the image. Experimental results demonstrate the improvements (stability and continuity) that can be obtained in the performance of the vision-based control task when the weighted features formulation is used.

IMPROVING THE RESULTS OF THE CONTENT-BASED IMAGE QUERY ON MEDICAL IMAGERY

Liana Stanescu, Dan Dumitru Burdescu, Anca Ion and Marius Brezovan

The article presents a solution for raising the quality of the content-based image query process, namely of the number of the relevant images retrieved from the database for a query image, in the case of the color medical images. The solution combines the content-based image query on color feature with color texture feature. There have been effectuated and presented studies of content-based image query on color images from the field of the digestive apparatus gathered with an endoscope. The color information is represented by the color histograms computed on HSV color space quantized at 166 colors. In order to represent the color texture the co-occurrence matrices are used. To compute the dissimilitude between the images, the histogram intersection has used for the color and the Euclidian distance for the color texture. The union of the results obtained with the two content-based image query methods on color and color texture, performed in parallel, leads to a greater number of retrieved relevant images. The reason is, that, generally, in the case of the considered diseases there are changes in the color and the texture of the sick tissue.

HYBRID IMPEDANCE CONTROL FOR MULTI-SEGMENTED INSPECTION ROBOT Kairo-II

C. Birkenhofer, S. Studer, J. M. Zöllner and R. Dillmann

The huge redundancy of multi-segmented robot KairoII can be utilized to add to a general robot configuration any inspection subtask . For doing so, an extensive control scheme has to be installed that is able to handle both, contact scenarios with the environment and ambiguous robot configurations. A method for implementing an appropriate scheme using transposed Jacobians based on Hybrid Impedance Control (TJ-HIC) is described and validated for multi-segmented robots. Crucial parts of this model are identified and implemented. Those parts are a dynamic robot model that is realized in Recursive Newton-Euler equations (RNE) and a sensory system for apropriate force feedback information.

ROBOTIC ARCHITECTURE BASED ON ELECTRONIC BUSINESS MODELS - From Physics Components to Smart Services

José Vicente Berná-Martínez, Francisco Maciá-Pérez, Virgilio Gilart-Iglesias and Diego Marcos-Jorequera

In this article we presented a view for the robots and robotic systems design based on applying models, architectures, techniques and tools that have allowed contributing valid solutions in other dominions of application, like the electronic business. Before being able to apply these solutions, it is essential to subjugate to the physical elements that compose a robotic system to a process of normalization that allows characterizing them from the point of view of its functional contribution. At this point we showed to the conceptual model and the technical architecture of the robotic system based on services oriented architectures. The work also gathers the implementation of a normalized robotic element according to the exposed techniques that allow verifying the validity of the proposal

PDPT FRAMEWORK - Building Information System with Wireless Connected Mobile Devices

Ondrej Krejcar

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. Additionally, the ability to let a mobile device determine its location in an indoor environment at a fine-grained level supports the creation of a new range of mobile control system applications. Main area of interest is in model of radio-frequency (RF) based system enhancement for locating and tracking users of our control system inside buildings. The framework described here joins the concepts of location and user tracking in an extended existing control system. The experimental framework prototype uses a WiFi network infra-structure to let a mobile device determine its indoor position as well as to de-liver IP connectivity. User location is used to data pre-buffering and pushing information from server to user’s PDA. Experiments show that location deter-mination can be realized with a room level granularity.

MONTE CARLO LOCALIZATION IN HIGHLY SYMMETRIC ENVIRONMENTS

Stephan Sehestedt and Frank E. Schneider

The localization problem is a central issue in mobile robotics. Monte Carlo Localization (MCL) is a popular method to solve the localization problem for mobile robots. Unfortunately, usual MCL has some shortcomings in terms of computational complexity, robustness and the handling of highly symmetric environments. These three issues are adressed in this work. We present three Monte Carlo localization algorithms. The focus lies on two of these, which are especially suitable for highly symmetric environments. These algorithms ai