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The combined action of several sensing systems, so that they are able to compensate the technical flaws of each other, is common in robotics. Monte Carlo Localization (MCL) is a popular technique used to estimate the pose of a mobile robot, which allows the fusion of heterogeneous sensor data. Several sensor fusion schemes have been proposed which include sensors like GPS to improve the performance of this algorithm. In this paper, an Adaptive MCL algorithm is used to combine data from wheel odometry, an inertial measurement unit, a global positioning system and laser scanning. A particle weighting model which integrates GPS measurements is proposed, and its performance is compared with a particle generation approach. Experiments were conducted on a real robotic car within an urban environment.

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In optimization problems, neural networks have been proved as an efficient method. Because of this, students of Engineering should learn this tool properly in subjects as ''Optimal Control'' or ''Intelligent Control.'' In this paper, a planning for neural networks teaching is proposed. As tool, Evenet-2000, a Java-based toolkit which allows to design and train neural networks with arbitrary architecture, is used. ß