Abstract
The inspiration for this paper comes from a successful experiment conducted with students in the "Mobile Robots" course in the fifth year of the integrated Master's program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots course is "Localization of Mobile Robots using the Extended Kalman Filter in a LEGO NXT," which gives the students the opportunity to study the concepts of localization. This experiment comes within the framework of teaching localization concepts in mobile robotics and focuses primarily on explaining the Kalman filter concept. It involves a specific tool developed by the authors and based on LEGO NXT technology. The work presented here could be a helpful guide for teaching concepts related to localization in mobile robotics to ensure adequate understanding of the concept and of the use of the extended Kalman filter (EKF). The LegoFeup robot described here was built using a LEGO Mindstorms NXT and tested both in simulation and in real scenarios. Based on the results obtained, the authors concluded that the developed tool is effective in motivating students. The implementation of the tool, the structure of the Mobile Robots course, and the criteria for student assessment are described in this paper.

Abstract
In this article, we describe the analysis, design and implementation of a control architecture for a mobile platform to autonomously carry out transportation, surveillance and inspection tasks in semi-structured industrial environments. Based on a hierarchical structure composed by the Organization, Coordination and Functional levels organized linguistically and structured according to the Principle of Increasing Precision with Decreasing Intelligence, this control architecture is permits the real-time parallel execution of tasks.

Abstract

Abstract
In this paper we describe the algorithms used in the external tracking system of the Isurus AUV. By listening to the acoustic signals exchanged between the vehicle and the beacons of the acoustic navigation network, the tracking system is able to obtain distance measurements from the vehicle to each beacon, that are then used to compute the vehicle horizontal position. Several error sources make these measurements inadequate to be used for computing the vehicle position by a simple triangulation technique. The tracking algorithms described here are able to reject highly erroneous measurements, producing position estimates with a satisfactory degree of accuracy. Copyright © 2004 IFAC

Abstract
This paper presents the recent developments of the project Multiple Autonomous Underwater Vehicles for Coastal Oceanography, of the University of Porto. The project envisages the development of a highly operational and low-cost system for scientific and environmental data collection in the Portuguese coastal waters. The project activities suggested the development of a Generalized Vehicle (GV) Control Architecture for multiple vehicles. Reconfigurability, reliability and operational effectiveness, are at the basis of this control architecture, which encompasses human intervention besides the cooperation among AUVs and other system devices. A simulation environment was developed in SHIFT, to support the design and testing of the GV control architecture. SHIFT is a new specification language for describing dynamic networks of hybrid automata, which constitute the most adequate formalism for this problem domain.

Abstract
In this paper, we present a low-cost versatile device developed to support the navigation of autonomous underwater vehicles. Unlike the usual transponders extensively used for LBL acoustic navigation of a single vehicle, this device allows the navigation of multiple vehicles and the remote tracking of their positions, without any extra acoustic signals being transmitted. The navigation beacon has a radio buoy connected to an underwater reconfigurable multi-frequency transponder. A remote tracking station receives data from the buoy and monitors the position of the vehicles in real time. We describe the mechanical, electronic and software modules involved, as well as the tracking algorithm, and we also present experimental data from an operational mission.