Abstract
The increase of the use of Autonomous Vehicles in different types of environments leads to an improvement of the Localization and Navigation algorithms. The goal is to increase the levels of efficiency, security and robustness of the system, minimizing the tasks completion time. The application of cleaning robots in domestic environments have several advantages however some improvements should be performed in order to develop a robust system. Also in large spaces one robot doesn't achieve the desired performance in terms of robustness to faults and efficiency in the cleaning process. Considering a fleet of autonomous robots, this process could be improved. The purpose of our paper is the presentation of an architecture for management a fleet of cleaning robots, considering a complete coverage path planning for large and structured environments. Compartments are found in a grid-like decomposition and an area coverage strategy are evolved (optimized) by using Genetic Algorithms. The Task allocation module is based on Auctions strategy, thus obtaining cooperation under dynamic constraints in complex environments. The case study optimizes the number of robots involved in the cooperative cleaning of a full building in the campus, based on its real architectural plans.

Abstract
Innovation has a cost. It is often the case that museums trying to innovate, simply cannot afford what it takes to build truly memorable exhibitions. Lack of human resources and high-tech equipment makes it hard to create quality contents that could be shown to the general public. Nevertheless, universities' museums are usually embedded in an environment that has the potential to provide all the tools and human resources required. The only thing needed is to establish a proper strategy and an interaction facilitator platform - U.OpenLab - which enables creating, building and sharing knowledge about the museums' collections and the academic population. This will make it easier to distribute the aforementioned knowledge to the general public, in a truly sustainable, systematic, integrated and articulated manner. In this paper we are going to present the University of Porto (U.Porto) OpenLab prototype that is being built as the stepping stone of this project, providing students with learning in a project environment.

Abstract
Problem-Based Learning (PBL) has been used in several domains for almost two decades as a more efficient way to develop student's new skills. This approach lends itself well to teaching Industrial Communication Systems, allowing the students to acquire skills that enable them to solve a large set of concrete problems in an industrial context. Based on the authors experience in the Integrated Master in Electrical and Computer Engineering at the University of Porto in Portugal, we have developed a new hardware and software based platform for teaching industrial communications that is affordable and portable and amenable to PBL. This platform is the basis of a new course developed as a module in the MEDIS European project (MEDIS: A Methodology for the Formation of Highly Qualified Engineers at Masters Level in the Design and Development of Advanced Industrial Informatics Systems). This paper describes the course itself, the tools used and includes a brief discussion on the feedback received from early adopters among MEDIS participating universities.

Abstract
Self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to algorithms accuracy, robustness and computational efficiency. In this paper we present the comparison of two of the most used map-matching algorithm, which are the Iterative Closest Point and the Perfect Match. This category of algorithms are normally applied in localization based on natural landmarks. They were compared using an extensive collection of metrics, such as accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to outliers in the robots sensors data. The test results were performed in both simulated and real world environments.

Abstract
The Robot@Factory competition was recently included in Robotica, the main Portuguese Robotics Competition. This robot competition takes place in an emulated factory plant, where automatic guided vehicles (AGVs) must cooperate to perform tasks. To accomplish their goals, the AGVs must deal with localization, navigation, scheduling, and cooperation problems that must be solved autonomously. This robot competition can play an important role in education due to its inherent multidisciplinary approach, which can motivate students to bridge different technological areas. It can also play an important role in research and development, because it is expected that its outcomes will later be transferred to real-world problems in manufacturing or service robots. By presenting a scaled-down factory shop floor, this competition creates a benchmark that can be used to compare different approaches to the challenges that arise in this kind of environment. The ability to alter the environment, in some restricted areas, can usually promote the test and evaluation of different localization mechanisms, which is not possible in other competitions. This paper presents one of the possible approaches to build a robot capable of entering this competition. It can be used as a reference to current and new teams.

Abstract
Usually the Industrial Automatic Guide Vehicles (AGVs) have two kind of lasers. One for navigation on the top and others for obstacle detection (security lasers). Recently, security lasers extended its output data with obstacle distance (contours) and reflectivity, that allows the development of a novel localization system based on a security laser. This paper addresses a localization system that avoids a dedicated laser scanner reducing the implementations cost and robot size. Also, performs a tracking system with precision and robustness that can operate AVGs in an industrial environment. Artificial beacons detection algorithm combined with a Kalman filter and outliers rejection method increase the robustness and precision of the developed system. A comparison between the presented approach and a commercial localization system for industry is presented. Finally, the proposed algorithms were tested in an industrial application under realistic working conditions.