Abstract
Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1 degrees-3 degrees degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise.

Abstract
Autonomous robots play a pivotal role in improving productivity and reducing operational costs. They excel at both precision and speed in repetitive jobs and can cooperate with humans in complex tasks within dynamic environments. Self-localization is critical to any robot that must navigate or manipulate the environment. To solve this problem, a modular localization system suitable for mobile manipulators was developed. By using LIDAR data the proposed system is capable of achieving less than a centimeter in translation error and less than a degree in rotation error while requiring only 5 to 25 milliseconds of processing time. The system was tested in two different robot platforms at different velocities and in several cluttered and dynamic environments. It demonstrated high accuracy while performing pose tracking and high reliability when estimating the initial pose using feature matching. No artificial landmarks are required and it is able to adjust its operation rate in order to use very few hardware resources when a mobile robot is not moving.

Abstract
The sports community needs technological aid to extract accurate statistics and performance data from both practice sessions and games. To obtain such information, players must be tracked over time and their movements processed so that individual actions and team plays are simultaneously analyzed. In order to perform this analysis in an automated, formal and accurate way, the authors developed a cost conscientious processing system fed by two overhead cameras (roughly one video stream for each half-field). Players are detected by vest colors, and Fuzzy Logic is used to allow for a given color to be shared by different teams. Color models for the background and the teams are dynamic over time to make up for changes in natural lighting conditions and consequent color changes. Player tracking is further enhanced using Kalman Filtering. Some examples of the analysis, made possible by the proposed system, are shown. Results are based on videos collected during the Portuguese Handball SuperCup competition for the year 2011.

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
In the growing field of Robotics, one of the many possible paths to explore is the social aspect that it can influence upon the present society. The combination of the goal-oriented development of robots with the interactivity used in games while employing mixed reality is a promising route to take in regard to designing user-friendly robots and improving problem solving featured in artificial intelligence software. In this paper, we present a competitive team-based game using Pololu's 3Pi robots moving in a projected map, capable of human interaction via game controllers. The game engine was developed utilizing the framework Qt Creator with C++ and OpenCV for the image processing tasks. The technical framework uses the ROS framework for communications that may be, in the future, used to connect different modules. Various parameters of the implementation are tested, such as position tracking errors.