Abstract
The integration of different robot automation technologies, with the aim for reusing available production solutions, is a major obstacle for deployment of low-cost components into productive (high-performance) systems. Technologies demanding high processing power, like machine vision or voice recognition systems, are normally easy to program but require proprietary languages and platforms, which constitutes an important problem during communications and setup. Instead of the current need for trained specialist, in particular flexible manufacturing in SMEs call for solutions that are easy easy to use and (re)configure. One attempt in that direction is the service-oriented architecture (SOA) approach, which here is accomplished by the use of Universal Plug-and-Play (UPnP) technologies and confronted with real robot application demand represented by an experimental manufacturing cell. Contributions include the way of building software applications to program manufacturing cells whose building blocks are represented by UPnP devices. Such devices encapsulate both manufacturing equipment and interaction methods. The latter is exemplified by a speech recognition system, for which a tool for automatic generation of UPnP devices based on the information contained in speech recognition XML grammars is presented. Experiences form experiments confirms the desired efficiency and simplicity when setting up advanced manufacturing equipment. © 2007 IFAC.

Abstract
This paper introduces Petri net based models of robotic tasks, which can be used to analyse and synthesise task plans, taking into account a Petri net model that abstracts the relevant features from the robot environment as well. Logical analysis concerning deadlocks and resource conservation can be performed over the ordinary version of the model. A task plan modeled by a Petri net can be extracted from the generalised stochastic version of the model, representing the optimal plan given a probabilistic measure of uncertainty associated to the effects of its composing actions. The Petri net representing the model is suitable for being ran directly within the code, as well as for plan monitoring during execution time. Simulation results illustrating the methodology are presented for a robotic soccer scenario.

Abstract
This article reports the study of fractional dynamics during the evolution of a Particle Swarm Optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle. The dynamics is represented through the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence of the PSO parameters influence upon the global dynamics is also analyzed.

Abstract
Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Abstract
This paper studies the fractional dynamics during the evolution of a Particle Swarm Optimization (PSO). Some swarm particles of the initial population are randomly changed for stimulating the system response. After the result is compared with a reference situation. The perturbation effect in the PSO evolution is observed in the perspective of the time behavior of the fitness of the best individual position visited by the replaced particles. The dynamics is investigated through the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence of the PSO parameters upon the global dynamics is also analyzed by performing several experiments for distinct values.

Abstract
The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports.