MOTOR PARAMETERS INFLUENCE ON STABILITY OF DRIVE FOR INDUSTRIAL ROBOT

Sorin Enache, Monica Adela Enache, Mircea Dobriceanu, Mircea Adrian Drighiciu and Anca Petrisor

This paper analyzes a driving system for an industrial robot from the stability point of view. For doing this, an original analysis method has been conceived. The method has as starting point the two axes mathematical model with equations written in per unit values. A Matlab program has been conceived with their help; this program has led to results and conclusions detailed in this paper. Finally a series of experimental results confirming the conclusions deduced with the new method are presented.

EVOLUTION OF A MOBILE ROBOT’S NEUROCONTROLLER ON THE GRASPING TASK - Is Genetic also Generic?

Philippe Lucidarme

This paper presents a survey on the generic evolution of mobile robot’s neurocontrollers with a particular focus on the capacity to adapt these controllers in several environments. Several experiments on the example of the grasping task (autonomous vacuum cleaner for example) are performed and the results show that the produced neurocontroller is dedicated to the trained conditions and cannot be considered as generic. The last part of the paper discusses of the necessary changes in the fitness function in order to produce generic neurocontrollers.

OPTIMAL CONTROL WITH ADAPTIVE INTERNAL DYNAMICS MODELS

Djordje Mitrovic, Stefan Klanke and Sethu Vijayakumar

Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems with non-linear dynamics and non-quadratic costs can be found by iterative methods, such as the iterative Linear Quadratic Gaussian (iLQG) algorithm. So far this framework relied on an analytic form of the system dynamics, which may often be unknown, difficult to estimate for more realistic control systems or may be subject to frequent systematic changes. In this paper, we present a novel combination of learning a forward dynamics model within the iLQG framework. Utilising such adaptive internal models can compensate for complex dynamic perturbations of the controlled system in an online fashion. The specific adaptive framework introduced lends itself to a computationally more efficient implementation of the iLQG optimisation without sacrificing control accuracy - allowing the method to scale to large DoF systems.

LHTNDT: LEARN HTN METHOD PRECONDITIONS USING DECISION TREE

Fatemeh Nargesian and Gholamreza Ghassem-Sani

In this paper, we describe LHTNDT, an algorithm that learns the preconditions of HTN methods by examining plan traces produced by another planner. LHTNDT extracts conditions for applying methods by using decision tree algorithm. It considers the state of relevant domain objects in both current and goal state. Structurally repetitive training samples are removed using graph isomorphism. In our experiments, LHTNDT converged. It can learn most of preconditions correctly and almost quickly. Approximately 80% of test problems can be solved by preconditions extracted by ¾ of plan traces needed for full convergence.

FEEDING A GENETIC ALGORITHM WITH AN ANT COLONY FOR CONSTRAINED OPTIMIZATION - An Application to the Unit Commitment Problem

Guillaume Sandou, Stéphane Font, Sihem Tebbani, Arnaud Hiret and Christian Mondon

In this paper, a new optimisation strategy for the solution of the classical Unit Commitment problem is proposed. This problem is known to be an often large scale, mixed integer programming problem. Due to high combinatorial complexity, the exact solution is often intractable. Thus, a metaheuristic based method has to be used to compute a very often suitable solution. The main idea of the approach is to use ant colony algorithm, to explicitly deal with the feasibility of the solution, and to feed a genetic algorithm whose goal is to intensively explore the search space. Finally, results show that the proposed method leads to the tractable computation of satisfying solutions for the Unit Commitment problem.

SELF-ORGANISATION OF GAIT PATTERN TRANSITION - An Efficient Approach to Implementing Animal Gaits and Gait Transitions

Zhijun Yang, Juan Huo and Alan Murray

As an engine of almost all life phenomena, the motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. Despite the difficulty of being physically identified, the central pattern generator (CPG), which is a concrete branch of studies on the CNS, is widely recognised to be responsible for generating rhythmic patterns. This paper presents a novel, macroscopic and model-independent approach to the retrieval of different patterns of coupled neural oscillations observed in biological CPGs during the control of legged locomotion. Based on the simple graph dynamics, various types of oscillatory building blocks (OBB) can be reconfigured for the production of complicated rhythmic patterns. Our quadrupedal locomotion experiments show that an OBB-based artificial CPG model alone can integrate all gait patterns and undergo self-organised gait transition between different patterns.

REDUCED ORDER H∞ SYNTHESIS USING A PARTICLE SWARM OPTIMIZATION METHOD

Guillaume Sandou, Gilles Duc and Patrick Boucher

Hinfinity controller synthesis is a well known design method for which efficient dedicated methods have been developed. However, such methods compute a full order controller which has often to be reduced to be implemented. Indeed, the reduced order Hinfinity synthesis is a non convex optimization problem due to rank constraints. In this paper, a particle swarm optimization method is used to solve such a problem. Numerical results show that the computed controller has a lower Hinfinity norm than the controller computed from a classical Hankel reduction of the full order Hinfinity controller.

OBTAINING MINIMUM VARIABILITY OWA OPERATORS UNDER A FUZZY LEVEL OF ORNESS

Kaj-Mikael Björk

Finding the optimal OWA (ordered weighted averaging) operators is important in many decision support problems. The OWA-operators enables the decision maker to model very different kinds of aggregator operators. The weights need to be, however, determined under some criteria, and can be found through the solution of some optimization problems. The important parameter called the level of orness may, in many cases, be uncertain to some degree. Decision makers are often able to estimate the level using fuzzy numbers. Therefore, this paper contributes to the current state of the art in OWA operators with a model that can determine the optimal (minimum variability) OWA operators under a (unsymmetrical triangular) fuzzy level of orness.

SYNCHRONIZATION OF ARM AND HAND ASSISTIVE ROBOTIC DEVICES TO IMPART ACTIVITIES OF DAILY LIVING TASKS

Duygun Erol and Nilanjan Sarkar

Recent research in rehabilitation indicates that tasks that focus on activities of daily living (ADL) is likely to show significant increase in motor recovery after stroke. Most ADL tasks require patients to coordinate their arm and hand movements to complete ADL tasks. This paper presents a new control approach for robot assisted rehabilitation of stroke patients that enables them to perform ADL tasks by providing controlled and coordinated assistance to both arm and hand movements. The control architecture uses hybrid system modelling technique which consists of a high-level controller for decision-making and two low-level assistive controllers (arm and hand controllers) for arm and hand motion assistance. The presented controller is implemented on a test-bed and the results of this implementation are presented to demonstrate the feasibility of the proposed control architecture.

DATA MINING AND KNOWLEDGE DISCOVERY FOR MONITORING AND INTELLIGENT CONTROL OF A WASTEWATER TREATMENT PLANT

S. Manesis, V. Deligiannis and M. Koutri

Intelligent control of medium-scale industrial processes has been applied with success but, as a method of advanced control, can be further improved. Since intelligent control makes use of knowledge-based techniques (such as expert systems, fuzzy logic, neural networks, etc.), a data mining and knowledge discovery subsystem embedded in a control system can support an intelligent controller to achieve a more reliable and robust operation of the controlled process. This paper proposes a combined intelligent control and data mining scheme for monitoring and mainly for controlling a wastewater treatment plant. The intelligent control system is implemented in a programmable logic controller, while the data mining and knowledge discovery system in a personal computer. The entire control system is basically a knowledge-based system which improves drastically the behavior of the wastewater treatment plant.

CONTROLLING INVESTMENT PROPORTION IN CYCLIC CHANGING ENVIRONMENTS

J.-Emeterio Navarro-Barrientos

In this paper, we present an investment strategy to control investment proportions for environments with cyclic changing returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market modeled by stylized returns on investment (RoI). For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the RoI, and we consider also an investment strategy based on technical analysis. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare their performance for RoIs with fixed periodicity (stationary scenario) and for RoIs with changing periodicities (non-stationary scenario).

ENERGY MODEL BASED CONTROL FOR FORMING PROCESSES

Patrick Girard and Vincent Thomson

Thermoforming consists of shaping a plastic material by deforming it at an adequate deformation rate and temperature. It often exhibits abrupt switches between stable and unstable material behaviour that have neither been identified nor controlled up to now. PID control, although adequate for simple parts, has not been able to control very well the forming of complex parts and parts made of newer materials. In this paper, the state parameters that allow the development of predictive models for the forming process and the construction of control systems are identified. A robust, model based control system capable of in-cycle control is presented. It is based on a simulator continuously tuned and supported in real time by intelligent agents that incorporate diagnostic capabilities.

AUTOMATED SIZING OF ANALOG CIRCUITS BASED ON GENETIC ALGORITHM WITH PARAMETER ORTHOGONALIZATION PROCEDURE

Masanori Natsui and Yoshiaki Tadokorot

This paper presents a method for the automated sizing of analog circuits using genetic algorithm (GA). GA is a kind of optimization techniques based on natural selection and genetics. For the rapid and efficient exploration of GA, we introduce the idea of search space sphering and dimension reduction with principal component analysis (PCA). The potential capability of the system is demonstrated through the automated sizing of wide-swing current mirror circuit. Experimental results show that the search space optimization using PCA improves the search efficiency of the system, and the system can estimate sub-optimal parameter set successfully.

DESIGN OF NEURONAL NETWORK TO CONTROL SPIRULINA AQUACULTURE

Ernesto Ponce, Claudio Ponce and Bernardo Barraza

A neural network that was designed to control a Spirulina aquaculture process in a pilot plant in the north of Chile, is presented in this work. Spirulina is a super food, but is a delicate alga and its culture may be suddenly lost by rapid changes in the weather that can affect its temperature, salinity or pH. The neural network control system presented is complex and non linear, and has several variables. The previous automatic control system for the plant proved unable to cope with large climatic variations. The advantage of this new method is the improvement in efficiency of the process, and a reliable control system that is able to adapt to climatic changes. The future application of this work is related to the industrial production of food and fuel from micro algae culture, for the growing world population.

NONLINEAR SYSTEM IDENTIFICATION USING DISCRETE-TIME NEURAL NETWORKS WITH STABLE LEARNING ALGORITHM

Talel Korkobi, Mohamed Djemel and Mohamed Chtourou

This paper presents a stable neural sytem identification for nonlinear systems. An input output discrete time representation is considered. No a priori knowledge about the nonlinearities of the system is assumed. The proposed learning rule is a the backpropagation algorithm under the condition tha the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomenon during the learning process is avoided. A Lyapunov analysis is made in order to extract the new updating formulations which contain a set of inequality constraints. In the constrained learning rate algorithm, the learning rate is updated at each iterative instant by an equation derived using the stability conditions. As a case study, identification of two discrete time systems are considered to demonstrate the effectiveness of the proposed algorithm. Simulation results concerning the considered systems are presented.

IMPROVEMENTS IN THE FIELD OF DEVICE INTEGRATION INTO AUTOMATION SYSTEMS WITH EMBEDDED WEB INTERFACES

Anton Scheibelmasser, Jürgen Menhart and Bernd Eichberger

Web-Technologies which came up in many fields of automation seem to be a solution which improves device integration in many ways. On the one hand the used Ethernet improves the installation techniques with reliable and approved network cables and routing devices. On the other hand the used internet protocols provide several services for the application software development. With the introduction of those services, the local controller of the measurement devices has to execute complex communication protocols in addition to the device specific tasks. This fact has serious influences on the measurement device instrumentation and the execution of the device firmware. Concerning new developments and compatible adaptations of existing instruments several ways for the integration of web technologies are available. The following article is intended to explain the architectural aspects of device integrations using Industrial Ethernet by means of an embedded web server. As a practical example to this architecture, concepts and results of a new developed communication module called EWI (embedded web interface) are given to demonstrate the improvements in measurement device integration in the field of automotive test bed automation.

MERGING OF ADVICES FROM MULTIPLE ADVISORY SYSTEMS - With Evaluation on Rolling Mill Data

Pavel Ettler, Josef Andrýsek, Václav Šmídl and Miroslav Kárný

The problem of evaluation of advisory system quality is studied. Specifically, 18 advisory strategies for operators of a cold rolling mill were designed using different modelling assumptions. Since some assumptions may be more appropriate in different working regimes, we also design a new advising strategy based on on-line merging of advices. In order to measure actual suitability of the advisory systems, we define two measures: operator’s performance index and coincidence of the observed operator’s actions with the advices. A time-variant model of advisory system suitability is proposed. Merging of the advices is achieved using Bayesian theory of decision-making. Final assessment of the original advisory systems and the new system is performed on data recorded during 6 months of operation of a real rolling mill. This task is complicated by the fact that the operator did not follow any of the advisory systems. Validation was thus performed with respect to the proposed measures. It was found that merging of the advising strategies can significantly improve quality of advising. The approach is general enough to be used in many similar problems.

MATHEMATICAL MODELLING OF THERMAL AREA IN CUTTING TOOL

Daschievici Luiza, Ghelase Daniela and Goanta Adrian

Since experimental researches regarding cutting process have stated a proportionality dependence of wear medium intensity on cutting area temperature and because this fact was avoid or ignored by thorough studies and researches, we considered to be helpful developing a physical-mathematical model able to correlate the two phenomena: wear and temperature in the cutting area. The complete and correct research on thermal phenomena in the cutting area is possible only by taking into consideration the feed-back relation between the physical and phenomenological elements of the studied tribosystem and also, by taking into account the splinter movement, resulting in a continuous supplying with cold layers of the splinter area and in heat evacuating by warm splinter movement.

A DISTRIBUTED FAULT TOLERANT POSITION CONTROL SYSTEM FOR A BOAT-LIKE INSPECTION ROBOT

Christoph Walter, Tino Krueger and Norbert Elkmann

Here we present the position control system of a swimming inspection robot for large under-ground con-crete pipes that are partially filled with waste water. The system consists of a laser-based measurement sub-system for position determination and a mechanical rudder to move the robot laterally within the pipe. The required software components are implemented as services following a CORBA-based architecture. To automatically adapt to different environment conditions a self tuning controller with hybrid requirements regarding latency and interarrival times of computed position values is used. We describe the architectural support for this type of application as well as how the system deals with ex-cessive latencies due to transient overload.

SMART SEMANTIC MIDDLEWARE FOR THE INTERNET OF THINGS

Artem Katasonov, Olena Kaykova, Oleksiy Khriyenko, Sergiy Nikitin and Vagan Terziyan

As ubiquitous systems become increasingly complex, traditional solutions to manage and control them reach their limits and pose a need for self-manageability. Also, heterogeneity of the ubiquitous components, standards, data formats, etc, creates significant obstacles for interoperability in such complex systems. The promising technologies to tackle these problems are the Semantic technologies, for interoperability, and the Agent technologies for management of complex systems. This paper describes our vision of a middleware for the Internet of Things, which will allow creation of self-managed complex systems, in particular industrial ones, consisting of distributed and heterogeneous components of different nature. We also present an analysis of issues to be resolved to realize such a middleware.

A GENETIC ALGORITHM APPLIED TO THE POWER SYSTEM RESTORATION PLANNING PROBLEM - A Metaheuristic Approach for a Large Combinatorial Problem

Adelmo Cechin, José Vicente Canto dos Santos, Arthur Tórgo Gómez and Carlos Mendel

This work reports the development of a Genetic Algorithm (GA) to solve the Power Systems Restoration Planning Problem (PSRP). The solution of the PSRP is a plan that informs to the Power System operator strategies or operation plans to be used after the occurrence of interruptions in the electrical energy transmission. The GA generates sequences of operations which are analyzed by a Power Flow program to verify and access their fitness. For this GA, a new genoma representation was developed, as well as two genetic operators, for crossover and mutation. This is one of the main contributions of this work. Tests performed with different electric networks shown the validity of the proposal.

FAIR AND EFFICIENT RESOURCE ALLOCATION - Bicriteria Models for Equitable Optimization

Włodzimierz Ogryczak

Resource allocation problems are concerned with the allocation of limited resources among competing activities so as to achieve the best performances. In systems which serve many users there is a need to respect some fairness rules while looking for the overall efficiency. The so-called Max-Min Fairness is widely used to meet these goals. However, allocating the resource to optimize the worst performance may cause a dramatic worsening of the overall system efficiency. Therefore, several other fair allocation schemes are searched and analyzed. In this paper we focus on mean-equity approaches which quantify the problem in a lucid form of two criteria: the mean outcome representing the overall efficiency and a scalar measure of inequality of outcomes to represent the equity (fairness) aspects. The mean-equity model is appealing to decision makers and allows a simple trade-off analysis. On the other hand, for typical dispersion indices used as inequality measures, the mean-equity approach may lead to inferior conclusions with respect to the outcomes maximization (system efficiency). Some inequality measures, however, can be combined with the mean itself into optimization criteria that remain in harmony with both inequality minimization and maximization of outcomes. In this paper we introduce general conditions for inequality measures sufficient to provide such an equitable consistency. We verify the conditions for the basic inequality measures thus showing how they can be used not leading to inferior distributions of system outcomes.

LOSS MINIMIZATION OF INDUCTION GENERATORS WITH ADAPTIVE FUZZY CONTROLLER

Durval de Almeida Souza*, José Antonio Dominguez Navarro** and Jesús Sallán Arasanz

In this paper a new technique for efficiency optimization of induction generator working a variable speed and load is introduced. The technique combines two distinct control methods, namely, on-line search of the optimal operating point, with a model based efficiency control. For a given operating condition, characterized by a given speed (m) and load torque (TL), the search control is implemented via the “Rosenbrock” method, which determines the flux level that results in the maximum output power. Once the optimal flux level has been found, this information is utilized to update the rule base of a fuzzy controller, which plays the role of an implicit mathematical model of the system. Initially, for any load condition the rule base yields the rated flux value. As the optimum points associated with the several operating conditions are identified, the rule base is progressively updated, such that the fuzzy controller learns to model the optimal operating conditions for the entire torque-speed plane. After every rule base update, the Rosenbrock controller output is reset, but it is kept active to track possible minor deviations of the optimum point.

LEARNING DISCRETE PROBABILISTIC MODELS FOR APPLICATION IN MULTIPLE FAULTS DETECTION

Luis E. Garza Castañón, Francisco J. Cantú Ortíz and Rubén Morales-Menéndez

We present a framework to detect faults in processes or systems based on probabilistic discrete models learned from data. Our work is based on a residual generation scheme, where the prediction of a model for process normal behavior is compared against measured process values. The residuals may indicate the presence of a fault. The model consists of a general statistical inference engine operating on discrete spaces, and represents the maximum entropy joint probability mass function (pmf) consistent with arbitrary lower order probabilities. The joint pmf is a rich model that, once learned, allows us to address inference tasks, which can be used for prediction applications. In our case the model allows the one step-ahead prediction of process variable, given its past values. The relevant dependencies between the forecast variable and past values are learnt by applying an algorithm to discover discrete bayesian network structures from data. The parameters of the statistical engine are also learn by an approximate method proposed by Yan and Miller. We show the performance of the prediction models and their application in power systems fault detection.

GPC AND NEURAL GENERALIZED PREDICTIVE CONTROL

S. Chidrawar, Nikhil Bidwai, L. Waghmare and B. M. Patre

As Model Predictive Control (MPC) relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking two separate systems tested the performances of the system. Simulation result show the effect of Neural network on Generalized Predictive Control. The performance comparison of these two-system configurations has been given in terms of ISE and IAE.

COGNITIVE TECHNICAL SYSTEMS IN A PRODUCTION ENVIRONMENT - Outline of a Possible Approach

Eckart Hauck, Arno Gramatke and Klaus Henning

High-Wage countries face the dilemmas of value- vs. planning orientation and the dilemma of economies of scale vs. economies of scope summed up in the term polylemma. To reduce the dilemma of planning vs. value orientation cognitive technical systems seem to be a promising approach. In this paper the requirements of such a cognitive system in a production environment is presented. Furthermore a first concept of a software architecture is given. To implement a knowledge base for a cognitive technical system certain formalism were scrutinized for their suitability in this approach and a possible use case for such a cognitive technical system is presented.

SELF CONSTRUCTING NEURAL NETWORK ROBOT CONTROLLER BASED ON ON-LINE TASK PERFORMANCE FEEDBACK

Andreas Huemer, Mario Gongora and David Elizondo

In this paper we present a novel methodology to create a powerful controller for robots that minimises the design effort. We show that using the feedback from the robot itself, the system can learn from experience. A method is presented where the interpretation of the sensory feedback is integrated in the creation of the controller, which is achieved by growing a spiking neural network system. The feedback is extracted from a performance measuring function provided at the task definition stage, which takes into consideration the robot actions without the need for external or manual analysis. With this research we aim to create a novel unsupervised design methodology for robot controllers where, starting with the interface between the sensors and actuators and the input and output neurons of a network, new connections are created using a novel structure tied to the performance interpretation of the robot. With this method we have enabled the neural network to optimise the total number of neurons and connections in the final system, creating an efficient learning controller. Results and conclusions are presented showing our contribution to further advance in the use of automated design as a tool for creating robotics control systems efficiently.

ONTOLOGY ADAPTER - Network Management System Interface Model

Lingli Meng, Lusheng Yan and Wenjing Li

This paper proposes a new way to define the interface model of network management system, that is ontology adapter. This model includes three parts, which are ontology agent, ontology knowledge base and ontology resource description. We can realize the uniform presentation of different network resource interface information using it. Therefore, we can take this model as a common data platform to offer the information to the network management system

STOCHASTIC CONTROL STRATEGIES AND ADAPTIVE CRITIC METHODS

Randa Herzallah and David Lowe

Adaptive critic methods have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, nonlinear and nonstationary environments. In this study, a novel probabilistic dual heuristic programming (DHP) based adaptive critic controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) adaptive critic method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterized by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the critic network is then calculated and shown to be equal to the analytically derived correct value.

LIGHT-WEIGHT REINFORCEMENT LEARNING WITH FUNCTION APPROXIMATION FOR REAL-LIFE CONTROL TASKS

Kary Främling

Despite the impressive achievements of reinforcement learning (RL) in playing Backgammon already in the beginning of the 90's, hardly any successful real-world applications of RL have been reported since then. This could be due to the tendency of RL research to focus on discrete Markov Decision Processes that make it difficult to handle tasks with continuous-valued features. Another reason could be a tendency to develop continuously more complex mathematical RL models that are difficult to implement and operate. Both of these issues are addressed in this paper by using the gradient-descent Sarsa($\lambda$) method together with a Normalised Radial Basis Function neural net. The experimental results on three typical benchmark control tasks show that these methods outperform most previously reported results on these tasks, while remaining computationally feasible to implement even as embedded software. Therefore the presented results can serve as a reference both regarding learning performance and computational applicability of RL for real-life applications.

AN APPROACH FOR A KNOWLEDGE-BASED NC PROGRAMMING SYSTEM

Ulrich Berger, Ralf Kretzschmann and Jan Noack

There are existing significant deficiencies in the information flow and access along the NC (Numerical Control) process chain. The deficiencies are solved insufficient by introducing CAD/CAM systems and feature-oriented specification languages. In contrast to that the application of new production and new machining systems requires an intensive information exchange. The introduced approach enables the preparation for feature-based work plans with methods known from the graph theory as a knowledge-based NC programming system. Therefore the work plan will be mapped into a directed graph in a mathematically defined way. Now it is possible to use algorithms to find the shortest path and a Hamiltonian path inside this directed graph regarding to given requirements. Thus, the work plan will be re-ordered and scheduled. Finally the corresponding NC paths will be generated and distributed to the machinery. Thence in this in-process paper the requirements, the investigation and selection of suitable knowledge structuring concepts, mathematically basics and the work flow in such a system will be pointed out. Finally a preliminary prototype will be introduced.

ADAPTIVE RESOURCES CONSUMPTION IN A DYNAMIC AND UNCERTAIN ENVIRONMENT - An Autonomous Rover Control Technique using Progressive Processing

Simon Le Gloannec, Abdel Illah Mouaddib and François Charpillet

This paper address the problem of an autonomous rover that have limited consumable resources to accomplish a mission. The robot has to cope with limited resources : it must decide the resource among to spent at each mission step. The resource consumption is also uncertain. Progressive processing is a meta level reasoning model particulary adapted for this kind of mission. Previous works have shown how to obtain an optimal resource consumption policy using a Markov decision process (MDP). Here, we make the assumption that the mission can dynamically change during execution time. Therefore, the agent must adapt to the current situation, in order to save resources for the most interesting future tasks. Because of the dynamic environment, the agent cannot calculate a new optimal policy online. However, it is possible to compute an approximate value function with which the robot will behave as good as if it knew the optimal policy.

FLEXIBLE ROBOT-BASED INLINE QUALITY MONITORING USING PICTURE-GIVING SENSORS

Chen-Ko Sung, Andreas Jacubasch and Thomas Müller

As part of the ROBOSENS project, the IITB developed and tested a new four-step concept for multiple sensor quality monitoring. The robot-based system uses an array of test-specific short-range and wide-range sensors which make the inspection process more flexible and problem-specific. To test this innovative inline quality monitoring concept and to adapt it to customized tasks, a development and demonstration platform was created. It consists of an industrial robot with various sensor ports - a so-called “sensor magazine” - with various task-specific, interchangeable sensors and a flexible transport system.

AN EFFICIENT INFORMATION EXCHANGE STRATEGY IN A DISTRIBUTED COMPUTING SYSTEM - Application to the CARP

Kamel Belkhelladi, Pierre Chauvet and Arnaud Schaal

Distributed computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and they have been implemented on parallel computers to speed up the computation. In this paper, we introduce a multi-agent model conceived as a conceptual and practical framework for distributed genetic algorithms used both to reduce execution time and get closer to optimal solutions. Instead of using expensive parallel computing facilities, our distributed model is implemented on easily available networked PCs. Furthermore, distributed genetic algorithms with multiple subpopulations are difficult to configure because they are controlled by many parameters that affect their efficiency and accuracy. Among other things, one must decide the number and the size of the subpopulations (demes), the rate of migration, the frequency of migrations, and the destination of the migrants. Moreover, we develop an efficient information exchange strategy based on the different dynamic migration window methods defined in (Kim, 2002) and the selective migration model defined in (Eldos, 2006). To evaluate the proposed approach, different kinds of experiments have been conducted on an extended set of Capacitated Arc Routing Problem(CARP). Obtained results are useful for optimization practitioners and show the efficiency of our approach.

A MARINE FAULTS TOLERANT CONTROL SYSTEM BASED ON INTELLIGENT MULTI-AGENTS

Tianhao Tang and Gang Yao

This paper presents a hybrid intelligent multi-agent method for marine faults tolerant control (FTC). A new FTC schema, implemented by different kinds of agent, is discussed as well as the structure and functions of those agents, which have been encapsulated with intelligent algorithms to carry out different aspects in FTC. These agents could, having a purpose of trying to earn payoff as much as possible in a mission, communicate and form a coalition via negotiation when they find cooperation would bring them more benefits. Simulation experiments and its results are shown at last to demonstrate the efficiency of the proposed system.

EXPONENTIAL OBSERVER FOR A CLASS OF NONLINEAR DISTRIBUTED PARAMETER SYSTEMS WITH APPLICATION TO A NONISOTHERMAL TUBULAR REACTOR

Nadia Barje, Mohammed Achhab and Vincent Wertz

This paper present sufficient conditions to guarantee the existence of an exponential state estimator for a class of infinite dimensional non-linear systems driven in a real Hilbert state description. The theory is applied to a nonisothermal plug flow tubular reactor, governed by hyperbolic first order partial differential equations. For this application performance issues of the exponential state estimator design are illustrated in a simulation study.

NEURAL NETWORK AND GENETIC ALGORITHMS FOR COMPOSITION ESTIMATION AND CONTROL OF A HIGH PURITY DISTILLATION COLUMN

J. Fernandez de Cañete, P. del Saz-Orozco and S. Gonzalez-Perez

Many industrial processes are difficult to control because the product quality cannot be measured rapidly and reliably. One solution to this problem is neural network based control, which uses an inferential estimator (software sensor) to infer primary process outputs from secondary measurements, and control these outputs. This paper proposes the use of adaptive neural networks applied both to the prediction of product composition from temperature measurements, and to the dual control of distillate and bottom composition for a continuous high purity distillation column. Genetic algorithms are used to automatically choice of the optimum control law based on the neural network model of the plant. The results obtained have shown the proposed method gives better or equal performances over other methods such fuzzy, or adaptive control

RECONFIGURATION OF EMBEDDED SYSTEMS

Mohamed Khalgui, Martin Hirsch, Dirk Missal and Hans-Michael Hanisch

This paper deals with automatic reconfiguration of control systems following the component-based standard IEC61499. First of all, we propose a new reconfiguration semantic allowing to improve the system performance even if there is no hardware fault. In addition, we propose to characterize the possible reconfiguration forms in order to cover all the possible execution scenarios. To apply an automatic reconfiguration, we define thereafter an agent-based architecture that we propose to model with nested state machines to control the design complexity.

OBDD COMPRESSION OF NUMERICAL CONTROLLERS

Giuseppe Della Penna, Nadia Lauri, Daniele Magazzeni and Benedetto Intrigila

In the last years, the use of control systems has become very common, especially in the embedded systems contained in a growing number of everyday products. Therefore, the problem of the automatic synthesis of control systems is extremely important. However, most of the current techniques for the automatic generation of controllers, such as cell-to-cell mapping, dynamic programming, set oriented approach or model checking, typically generate numerical controllers that cannot be embedded in limited hardware devices due to their size. A possible solution to this problem is to compress the controller. However, most of the common lossless compression algorithms, such as LZ77, would decrease the controller performances due to their decompression overhead. In this paper we propose a new, completely automatic OBDD-based compression technique that is capable of reducing the size of any numerical controller up to a space savings of 90% without any noticeable decrease in the controller performances.

THE PARALLELIZATION OF MONTE-CARLO PLANNING - Parallelization of MC-Planning

S. Gelly, J. B. Hoock, A. Rimmel, O. Teytaud and Y. Kalemkarian

Since their impressive successes in various areas of large-scale parallelization, recent techniques like UCT and other Monte-Carlo planning variants have been extensively studied. We here propose and compare various forms of parallelization of bandit-based tree-search, in particular for our computer-go algorithm XYZ.

PATH PLANNING FOR MULTIPLE FEATURES BASED LOCALIZATION

Francis Celeste, Frederic Dambreville and Jean-Pierre Le Cadre

In surveillance or exploration mission in a known environment, the localization of the dedicated sensor is of main importance. In this paper, we discuss the path planning problem for the localization algorithm which correlates range and bearing measurements and a map composed of several features. The sensor motion is designed from an in information measure deduced from the Fisher Information Matrix. It is shown that a closed form expression of the cost can be obtained. The optimal features location can be neatly geometrically interpreted. An integral cost which includes the sensor perception is then formulated based on this information metric. It is used in a dynamic programming framework to tackle the path optimization problem.

AN ONLINE BANDWIDTH SCHEDULING ALGORITHM FOR DISTRIBUTED CONTROL SYSTEMS WITH MULTIRATE CONTROL LOOPS

Saroja Kanchi and Juan Pimentel

In this paper, we present an online scheduling algorithm for communication in a distributed control system. The packet size of the communication varies for each execution of the loop within certain bounds. We consider systems with closed loops that restart immediately after the completion of an execution. Our algorithm is based on priority of the loop and size of the communication packet. We demonstrate through simulation that our algorithm generates a feasible schedule that minimizes average control delay over all the loops. Our simulations demonstrate that this online schedule reduces average delay significantly compared to a-priori schedules for distributed control systems. We demonstrate that bandwidth utilization is more efficient in case of online scheduling.

COMBINATION OF BREEDING SWARM OPTIMIZATION AND BACKPROPAGATION ALGORITHM FOR TRAINING RECURRENT FUZZY NEURAL NETWORK

Soheil Bahrampour, Sahand Ghorbanpour and Amin Ramezani

The usage of recurrent fuzzy neural network has been increased recently. These networks can approximate the behaviour of the dynamical systems because of their feedback structure. The Backpropagation of error has usually been used for training this network. In this paper, a novel approach for learning the parameters of RFNN is proposed using combination of the backpropagation and breeding particle swarm optimization. A comparison of this approach with previous methods is also made to demonstrate the effectiveness of this algorithm. Particle swarm is a derivative free, globally optimizing approach that makes the training of the network easier. These can solve the problems of gradient based method, which are instability, local minima and complexity of taking derivation.

TWO-SIDED ASSEMBLY LINE - Estimation of Final Results

Waldemar Grzechca

The paper considers simple assembly line balancing problem and two-sided assembly line structure. In the last four decades a large variety of heuristic and exact solutions procedures have been proposed to balance one-sided assembly line in the literature. Some heuristic were given to balance two-sided lines, too. Some measures of solution quality have appeared in line balancing literature: balance delay (BD), line efficiency (LE), line time (LT) and smoothness index (SI). These measures are very important for estimation the balance solution quality. Author of this paper modified and discussed the line time and smoothness for two-sided assembly line. Some problems, which appeared during evaluations, are mentioned.

A REAL TIME EXPERT SYSTEM FOR FAULTS IDENTIFICATION IN ROTARY RAILCAR DUMPERS

Osevaldo S. Farias, Jorge H. M. Santos, João V. F. Neto, Sofiane Labidi, Thiago Drumond, José Pinheiro de Moura and Simone C. F. Neves

This paper describes the development of a real-time Expert System applied to the ore extraction Industrial branch, specifically used to assist the decision making and fault identification on rotary railcar dumpers of the operational productive system located at Ponta da Madeira Dock Terminal, built and operated by Companhia Vale do Rio Doce (CVRD) in São Luis-MA. The Expert System is built on JESS (Java Expert System Shell) platform and provides support to engineers and operators during the ore unloading as soon as supplying on-line information about faults triggered by device sensors of the rotary railcar. The system’s conception involves the application of CommonKADS methodology, knowledge engineering and artificial intelligence techniques at the symbolic level for representing and organizing the knowledge domain in which the system is applied.

AUTOMATIC PARAMETERIZATION FOR EXPEDITIOUS MODELLING OF VIRTUAL URBAN ENVIRONMENTS - A New Hybrid Metaheuristic

Filipe Cruz, António Coelho and Luis Paulo Reis

Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the parameter values that feed the modeller. By gathering small amounts of information about certain key urban areas, it becomes possible to feed a system that automatically compares and adjusts the input parameter values to find optimal solutions of parameter combinations that resemble the real life model. These correctly parameterized rules can then be reapplied to generate virtual models of real areas with similar characteristics to the referenced area. Based on several nature inspired metaheuristic algorithms such as genetic algorithms, simulated annealing and harmony search, this paper presents a new hybrid metaheuristic algorithm aiming to find the optimal solution of a multi-parameter real-based optimization problem. Results achieved in a simple test-case are also presented showing the potential of the new hybrid metaheuristic algorithm when compared with standard optimization algorithms.

WISA - A Modular and Hybrid Expert System for Machine and Plant Diagnosis

Mario Thron, Thomas Bangemann and Nico Suchold

Expert systems are well known tools for diagnosis purposes in medicine and industry. One problem is the hard efford, to create the knowledge base. This article describes an expert system for industrial diagnosis and shows an efficient approach for the creation of the rule base, which is based on the reusage of knowledge modules. These knowledge modules are representants for assets like devices, machines and plants. The article encourages manufacturers of such assets to provide diagnosis knowledge bases by using a proposed multi-paradigm rule definition language called HLD (Hybrid Logic Description). Rule based knowledge may be expressed by using various methodologies, which differ in expressiveness but also in runtime performance. The HLD allowes rules to be defined as propositional logic with or without the use of certainty factors, as Fuzzy Logic or as probabilistic rules as in Bayesian Networks. The most effective rule type may be choosen to describe causal dependencies between symptoms and faillures. An evaluation prototype implementation provides separate terminals for experts and operators to communicate with the HLD interpreter via Internet-based communication systems.

AN EVOLUTIONARY ALGORITHM FOR UNICAST/ MULTICAST TRAFFIC ENGINEERING

Miguel Rocha, Pedro Sousa, Paulo Cortez and Miguel Rio

A number of Traffic Engineering (TE) approaches have been recently proposed to improve the performance of network routing protocols, both developed over MPLS and intra-domain protocols such as OSPF. In this work, a TE approach is proposed for routing optimization in scenarios where unicast and multicast demands are simultaneously present. Evolutionary Algorithms are used as the optimization engine with overall network congestion as the objective function. The optimization aim is to reach a set of (near-)optimal weights to configure the OSPF protocol. The results show that the method is able to obtain networks with low congestion, even under scenarios with heavy unicast/multicast demands.

COLLISION AVOIDANCE SYSTEM PRORETA - Strategies Trajectory Control and Test Drives

R. Isermann, U. Stählin and M. Schorn

Methods and experimental results of a collision avoidance driver assistance system are described with automatic object detection, trajectory prediction, and path following with controlled braking and steering. The objects are detected by a fusion of LIDAR scanning and video camera pictures resulting in the location, size and speed of objects in front of the car. A desired trajectory is calculated depending on the distance, the width of a swerving action and difference speed. For the trajectory control different control methods were designed and tested experimentally like velocity depend linear feedback and feedforward control, nonlinear asymptotic output tracking and nonlinear flatness based control using extended one-track models with vehicle state estimation for the sideslip angle and cornering stiffness. Automatic braking is realized with an electrohydraulic brake (EHB) and automatic steering with an active front steering (AFS). The various control systems are compared by simulations and real test drives showing the behaviour of a VW Golf with automatic braking or/and automatic swerving to a free track, such avoiding hitting a suddenly appearing obstacle. The research project PRORETA was a four-years-cooperation between Continental Automotive Systems and Darmstadt University of Technology.

EVALUATION OF NEURAL PDF CONTROL STRATEGY APPLIED TO A NONLINEAR MODEL OF A PUMPED – STORAGE HYDROELECTRIC POWER STATION

G. A. Munoz-Hernandez, C. A. Gracios-Marin, A. Diaz-Sanchez, S. P. Mansoor and D. I. Jones

In this paper, a neural Pseudoderivative control (PDF) is applied to a nonlinear mathematical model of the Dinorwig pumped - storage hydroelectric power station. The response of the system with this auto-tuning controller is compared with the classic controller, currently implemented on the system. The results show how the application of PDF control to a hydroelectric pumped-storage station improves the dynamic response of the power plant, even when multivariable effects are taken into account.

THE BEES ALGORITHM AND MECHANICAL DESIGN OPTIMISATION

D. T. Pham, M. Castellani, M. Sholedol and A. Ghanbarzadeh

The Bees Algorithm is a search procedure inspired by the way honey-bees forage for food. A standard mechanical design problem, the design of a welded beam structure, was used to benchmark the Bees Algorithm against other optimisation techniques. The paper presents the results obtained showing the robust performance of the Bees Algorithm.

A NOVEL PARTICLE SWARM OPTIMIZATION APPROACH FOR MULTIOBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEM

Souad Mekni, Besma Fayech Char and Mekki Ksouri

Because of the intractable nature of the problem of flexible job shop scheduling and its importance in both fields of production management and combinatorial optimization, it is desirable to employ efficient metaheuristics in order to obtain a better solution quality for the problem. In this paper, a novel approach based on the vector evaluated particle swarm optimization and the weighted average ranking is presented to solve flexible job shop scheduling problem (FJSP) with three objectives (i) minimize the makespan, (ii) minimize the total workload of machines and (iii) minimize the workload of critical machine. To convert the continuous position values to the discrete job sequences, we used the heuristic rule the Smallest Position Value (SPV). Experimental results in this work are very enouraging since that relevent solutions were provided in a reasonable computational time.

RFID BASED LOCATION IN CLOSED ROOMS - Implementation of a Location Algorithm using a Passive UHF-RFID System

Christoph Schönegger, Burkhard Stadlmann and Michael E. Wernle

This paper presents a new concept for determining the location of an RFID-tag without any additional hardware. For this positioning system standard RFID components within the UHF range are used. The measurement is based on a location algorithm which makes use of the RSSI value of the UHF reader. The RSSI value is the return signal strength indicator and, as it is shown in the paper in hand, this signal correlates to the distance between the RFID tag and the antenna of the reader. This positioning system is especially useful indoors, where other positioning systems may not work. For this reason it could prove very useful in various logistics applications. The maximum distance from antenna to the tag is approximately between 0.5 m and 3 m. To this end a special algorithm is used to obtain stable calculation results. A minimum of two antennas is needed to get a two-dimensional location.

PARALLEL MACHINE EARLINESS-TARDINESS SCHEDULING - Comparison of Two Metaheuristic Approaches

Marcin Bazyluk, Leszek Koszalka and Keith J. Burnham

This paper considers the problem of parallel machine scheduling with the earliness and tardiness penalties (PMSP\_E/T) in which a set of sequence-independent jobs is to be scheduled on a set of given machines to minimize a sum of the weighted earliness and tardiness values. The weights and due dates of the jobs are distinct positive numbers. The machines are diverse - each has a different execution speed of the respective jobs, thus the problem becomes more complex. To handle this, it two heuristics are employed, namely: the genetic algorithm with the MCUOX crossover operator and the tabu search. The performances of the both approaches are evaluated and their dependency on the shape of the investigated instances examined. The results indicate the significant predominance of the genetic approach for the larger-sized instances.

SENSOR AND ACTUATOR FAULT ANALYSIS IN ACTIVE SUSPENSION IN VIEW OF FAULT-TOLERANT CONTROL

Claudio Urrea and Marcela Jamett

This paper shows the first step of a fault tolerant control system (FTCS) to control active suspension on a full-car suspension model. In this paper, the elimination of the inevitable pitch and roll actions of a spring suspension between each axle and the body of a vehicle is studied. An actuator (linear motor) producing an electromagnetic force and a pneumatic force acting simultaneously on the same output element is used. This linear motor acts as a force generator that compensates instantly for the disturbing effects of the road surface. Simulation results to illustrate the system’s performance in front of the occurrence of sensor and actuator faults are shown.

HUNTER – HYBRID UNIFIED TRACKING ENVIRONMENT - Real-time Identification and Tracking System using RFID Technology

A. G. Foina and F. J. Ramirez Fernandez

This article presents the results of the use of RFID technology for trucks’ cargo real-time tracking. RFID tags were settled at trucks’ dump-carts and readers were spread throughout warehouses entrances, at the truck weighting scale and through unload platforms. The unload inspectors used robust PDA with camera, along with Wi-Fi access points installed in warehouses, to confirm the truck information and take a snapshot for future audits. A wireless broadband link was used to connect two weighting scale that are distant from the unloading area. All technologies communicate with a web-based middleware that manages all different devices. The system design is flexible enough to be used in very different applications like product process control, automated manufactory lines control, supply chain applications and others.

GENETIC-ALGORITHM SEEDING OF IDIOTYPIC NETWORKS FOR MOBILE-ROBOT NAVIGATION

Amanda M. Whitbrook, Uwe Aickelin and Jonathan M. Garibaldi

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, pre-engineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

ROBOT GOES BACK HOME DESPITE ALL THE PEOPLE

Paloma de la Puente, Diego Rodriguez-Losada, Luis Pedraza and Fernando Matia

We have developed a navigation system for a mobile robot that enables it to autonomously return to a start point after completing a route. It works efficiently even in complex, low structured and populated indoor environments. A point-based map of the environment is built as the robot explores new areas; it is employed for localization and obstacle avoidance. Points corresponding to dynamical objects are removed from the map so that they do not affect navigation in a wrong way. The algorithms and results we deem more relevant are explained in the paper.

IDENTIFICATION OF THE DYNAMIC PRAMETERS OF THE C5 PARALLEL ROBOT

B. Achili, B. Daachi, Y. Amirat and A. Ali-Cherif

This paper deals with the experimental identification of the dynamic parameters of the C5 parallel robot. The inverse dynamic model of the robot is formulated under the form of linear equation with respect to the dynamic parameters. Moreover, a heuristic procedure for finding the exciting trajectory has been conducted. This trajectory is based on Fourier series whose coefficients are determined by using a heuristic method. The least squares method has been applied to solve an over-determined linear system which is obtained by sampling the dynamic model along the exciting trajectory. The experimental results show the effectiveness of the identification procedure.

ROBUST CONTROL OF THE C5 PARALLEL ROBOT

B. Achili, B. Daachi, A. Aliu-Cherif and Y. Amirat

This paper deals with the dynamic control of a parallel robot with C5 joints. Computed torque control and robust control have been studied and implemented. For this purpose, we have used the inverse dynamic model whose parameters have been experimentally identified. The closed loop stability has been studied using the Lyapunov principle. The addition of a robustness term based on sliding mode technique ensures good tracking performances. The experimental results show the effectiveness of the robust control.

ROBOT NAVIGATION MODALITIES

Ray Jarvis

Whilst navigation (robotic or otherwise) consists simply of traversing from a starting point to a goal, there are a plethora of conditions, states of knowledge and functional intentions which dictate how best to execute this process in a manageable, reliable, safe and efficient way. This position paper addresses the broad issues of how a continuum of choices from pure manual or teleoperation control through to fully autonomous operation can be laid out and then selected from, taking into account the variety of factors listed above and the richness of live sensory data available to describe the operational environment and the location of the robot vehicle within it.

SHOE GRINDING CELL USING VIRTUAL MECHANISM APPROACH

Bojan Nemec and Leon Zlajpah

The paper describes the automation of the shoe grinding process using an industrial robot. One of the major problems of flexible automation using industrial robots is how to avoid joint limitations, singular configuration and obstacles. This problem can be solved using kinematically redundant robots. Due to the circular shape of the grinding disc, the robot becomes kinematically redundant. This task redundancy was efficiently handled using virtual mechanism approach, where the tool is described as a serial mechanism.

ROBOTIC WHEELCHAIR CONTROL CONSIDERING USER COMFORT - Modeling and Experimental Evaluation

Razvan Solea and Urbano Nunes

This paper analyzes the comfort of wheelchair users when a sliding mode trajectory-tracking controller is used. The transmission of the horizontal (fore-and-aft) vibration to the head-neck complex (HNC) in the seated human body may cause unacceptable discomfort and motion sickness. A double-inverted pendulum model with two degrees of freedom is considered as a model for the HNC. The user comfort is examined not only in the time domain (using the fourth power vibration dose value), but also in the frequency domain (using the cross-spectral density method). For measuring the acceleration of the wheelchair, along the trajectory, an inertial measurement unit was used.

DYNAMICAL MODELS FOR OMNI-DIRECTIONAL ROBOTS WITH 3 AND 4 WHEELS

Hélder P. Oliveira, Armando J. Sousa, A. Paulo Moreira and Paulo J. Costa

Omni-directional robots are becoming more and more common in recent robotic applications. They offer improved ease of maneuverability and effectiveness at the expense of increased complexity. Frequent applications include but are not limited to robotic competitions and service robotics. The goal of this work is find a precise dynamical model to predict the robot behavior. During this work, models were found for two real world omni-directional robot configurations and its parameters estimated using a prototype that can have 3 or 4 wheels. Results of experimental runs are presented in order to validate the presented work.

REAL TIME GRASPING OF FREELY PLACED CYLINDRICAL OBJECTS

Mario Richtsfeld, Wolfgang Ponweiser and Markus Vincze

In the near future, service robots will support people with different handicaps to improve the quality of their life. One of the required key technologies is to setup the grasping ability of the robot. This includes an autonomous object detection and grasp motion planning to fulfil the task of providing objects from any position on a table to the user. This paper presents a complete system, which consists of a fixed working station equipped with a laser-range scanner, a seven degrees of freedom arm manipulator and an arm prothesis as gripper. The contribution of this work is to use only one sensor system based on a laser-range scanning head to solve this challenge. The presented work is tested at a live demo presentation in front of more than 1000 college students in about 50 trials. The goal is that the user can select any defined object on the table and the robot arm delivers it to a target position or to the disabled person.

DIAGNOSIS OF DISCRETE EVENT SYSTEMS WITH PETRI NETS AND CODING THEORY

Dimitri Lefebvre

Event sequences estimation is an important issue for fault diagnosis of DES, so far as fault events cannot be directly measured. This work is about event sequences estimation with Petri net models. Events are assumed to be represented with transitions and firing sequences are estimated from measurements of the marking variation. Estimation with and without measurement errors are discussed in n – dimensional vector space over alphabet Z3 = {-1, 0, 1}. Sufficient conditions and estimation algorithms are provided. Performance is evaluated and the efficiency of the approach is illustrated on two examples from manufacturing engineering.

MODELING AND SIMULATION OF A NEW PARALLEL ROBOT USED IN MINIMALLY INVASIVE SURGERY

Doina Pisla, Calin Vaida, Nicolae Plitea, Jürgen Hesselbach, Annika Raatz, Marc Simnofske, Arne Burisch and Liviu Vlad

Surgery is one of the fields where robots have been introduced due to their positioning accuracy which exceed the human capabilities. Parallel robots offer higher stiffness and smaller mobile mass than serial ones, thus allowing faster and more precise manipulations that fit medical applications. In the paper is presented the modeling and simulation of a new parallel robot used in minimally invasive surgery. The parallel architecture has been chosen for its superiority in precision, repeatability, stiffness, higher speeds and occupied volume. The robot kinematics, singular position identification and workspace generation are illustrated. Using the developed virtual model of the parallel robot, some simulation tests are presented. The latest obtained results demonstrate that the computing time necessary for generating the virtual model is relatively small.

DYNAMIC MODELING OF A 6-DOF PARALLEL STRUCTURE DESTINATED TO HELICOPTER FLIGHT SIMULATION

Nicolae Plitea, Adrian Pisla, Doina Pisla and Bogdan Prodan

The dynamic analysis is the basic element of the mechanical design and control of parallel mechanisms. The parallel robots dynamics requires a great deal of computing as regards the formulation of the generally nonlinear equations of motion and their solution. In this paper a solution for solving the dynamical model of a 6-DOF parallel structure destined to helicopter flight simulation is presented. The obtained dynamical algorithms, based on the kinematical ones, offer the possibility of a complex study for this type of parallel structure in order to evaluate the dynamic capabilities and to generate the control algorithms.

RACBOT-RT: ROBUST DIGITAL CONTROL FOR DIFFERENTIAL SOCCER-PLAYER ROBOTS

João Monteiro and Rui Rocha

Robot soccer is a popular testbed to study challenging problems of mobile robotics. It is recognized in the robot soccer domain that robust trajectory control and high responsiveness to motion commands are key aspects to successfully deal with the game dynamic. With this aim, the paper presents a digital controller developed to small-sized robot soccer players. A special emphasis has been given to design a controller as much generic as possible, which can be applied to any mobile robot with differential kinematics. The theoretical framework is based on Lyapunov equations for pose stability convergence. The controller was implemented as a software module running on the robot, which responds to motion commands through the decomposition of the trajectory into a set of virtual reference positions with respect to the world reference coordinates system, which are further followed robustly by the robot, even in the presence of unwanted motion disturbances. Experimental results obtained with a mobile robot moving on the game field demonstrate the quality of the proposed solution and validate the implemented controller.

CONTRIBUTION CONCERNING ROBOT ACCURACY USING NUMERICAL MODELING

Daniela Ghelase, Luiza Daschievici and Irina Ghelase