Abstract
The purpose of the research is to identify the most common social representations of chemistry, green chemistry, robotics and sustainability among teenagers as they were engaging in a project that links robotics and green chemistry. Subjects were students from a school in the north of Portugal enrolled in year 9 (n = 163, 73 males and 86 females, 4 missing values, mean age around 15 years-old). Data was collected through a questionnaire that consisted of four open-ended, free association questions. Participants were asked to express their ideas and thoughts on each one of the four mentioned stimuli and asked to draw a robot. Written responses and drawings of robots were submitted to a content analysis. Preliminary results showed that the ten most frequent words associated with chemistry explain around 49% of the semantic field (average of 4.5 words per participant) and largely consist of conceptual elements, e.g., atoms (38), protons (22) and ions (22), experiments (114), laboratory (62) and explosions (19). The ten most frequent words associated with green chemistry explain around 42% of the semantic field of the representation. Participants - which never have learnt about green chemistry at school curriculum - filled their representations (2.8 words per participant) with elements derived from environment (e.g., nature, environment, trees,...). Only 92 participants defined sustainability, many of them associating the concept with earth's natural resources and future. The ten most frequent words associated with robotics (4.1 words per participant) explain around 53% of the semantic field which consists of robots (129), technology (50), electricity (39), metal (33), energy (20), circuits (18) and computers (17): electronics rather than informatics contribute for the representation. Most of the drawings of robots were anthropomorphic resembling more C-3PO than R2-D2 from Star Wars movies. Human elements, such as eyes, mouth and hands are largely present while movement is assured by means of feet or wheels. The significance of the study is that there is a decalage between science and technology developments and teenager audience understanding. From the point of view of the theory of social representations, this gap is not only expected but also understandable. Popular culture although still inspire the representation of chemistry is not as relevant as it would be if participants have not attended physics and chemistry classes for three years. On the other hand, since green chemistry and robotics are not included in the ordinary curriculum until year 9, the concepts are at loose and their meaning must be grounded elsewhere. Despite the impressive and rapid changes that characterize the technology research, its market and advertising, old metaphors and symbols still contribute to build the representations of teenagers about robotics while green chemistry - a relatively new approach - has its poor semantic field rooted in chemistry and environment. School, thus, must act as a medium to help students to build a coherent worldview that is capable of copying with contemporary challenges, strongly affected by science and technology namely regarding sustainability. An ongoing project is designed to introduce green chemistry experiments that involve slow reactions via a programmable robotic arm in the school laboratory to conduct. The planned activities include monitoring experiences by students at home by means of a live webcam.

Abstract
Interest in robotics field as a teaching tool to promote the STEM areas - Science, Technology, Engineering and Mathematics has grown in the past years. The search for costless solutions to promote robotics is a major challenge and the use of real robots always increases associated costs. An alternative to this is the use of a simulator. The construction of a simulator related with the Portuguese Autonomous Driving Competition using Gazebo as 3D simulator and Robotics Operating System (ROS) as a middleware connection to promote, attract, and enthusiasm university students to the mobile robotics challenges is presented. The proposed simulator focus on the autonomous driving competition task, such as semaphore recognition, localization, and motion control. An evaluation of the simulator is also performed, leading to an absolute error of 5.11% and a relative error of 2.76% on best case scenarios relating to the odometry tests and an accuracy of 99.37% regarding to the semaphore recognition tests performed.

Abstract
This article intends to debate the institutional modes of first-year adjustment to higher education. Specifically, the aim is to analyze and consider the need to include social and academic integration activities in the curricular programs. The presented contributions are based on the investigations over the case study course that was studied using non-experimental and descriptive approach. The “Projeto FEUP” (Faculty of Engineering of the University of Porto) course is analyzed as it addresses the mentioned integration concerns explicitly by providing tutoring by selected older students (relating to social integration) and adequate teamwork challenges (relating to academic integration). The presented case study course is given to about 1,000 students across nine engineering degrees at the Faculty of Engineering of University of Porto, in Portugal. The article includes some details about not infrequent practices such as “hazing” of newcomer students and strategies for circumventing associated disadvantages. The results shown in the article indicate that the students involved tend to see it as a significant academic integration device, regarding mainly academic work and expectations. © The Author(s) 2015.

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
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.