Abstract
NaSSIE - Navigation and Sensoring Skills in Engineering is a platform developed with the intent of facilitating the acquisition of some skills by Engineering Students, which is a core part of the process of controlling a mobile robot. In this paper, the chosen hardware and consequent physical construction of the prototype as well as vehicle’s associated software will be presented. As a use case, this platform was tested during the Robotic Day 2017 in Czech Republic. Preliminary results will also be presented of this year’s preparation for the Micromouse competition. © 2019, Springer Nature Switzerland AG.

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster.

Abstract
Robotics competitions are increasing in complexity and number challenging the researchers, roboticists and enthusiastic to address the robot applications. One of the well-known competition is the micromouse where the fastest mobile robot to solve a maze is the winner. There are several topics addressed in this competition such as robot prototyping, control, electronics, path planning, optimization, among others. A simulation can be used to speed-up the development and testing algorithms but faces the gap between the reality in the dynamics behaviour. In this paper, an open source realistic simulator tool is presented where the dynamics of the robot, the slippage of the wheels, the friction and the 3D visualization can be found. The complete simulator with the robot model and an example is available that allow the users to test, implement and change all the environment. The presented results validate the proposed simulator. © 2019, Springer Nature Switzerland AG.

Abstract
In this paper it is presented a line follower educational mobile robot performance robustness increase. The Robotic Day line Follower Competition was used as a Benchmark to test the proposed approach. The applied robot is based on an Arduino, which is applied in the low level control, while the high level control loop is carried out by an RPI running an object pascal application. The described robot was prototyped in order to have a competitive participation in the Robotic Day Line Follower 2017 competition, and improved for performance robustness increase in order to participate in the 2018 competition. It was prototyped with an RPI, taking advantage of its capabilities, allowing the use of higher performance sensors, when compared with the most common standard approaches based on a single 8 bit RISC micro-controller, having as disadvantage the inevitable robot size increase, which compromises in certain situations the robot maneuverability and increases the power consumption. The robot is equipped with DC Motors, the chosen line follower sensor is the picamera and for the obstacle detection, a time of flight sensor was applied.

Abstract
Since DC Motors are common components in engineering projects that involve process control, it is necessary for any student in this area to understand their concepts, construction and applications. This paper focuses on a series of Laboratory Experiments that were carried out in an Entry Level Unit of the Integrated Master Degree in Electrical and Computers Engineering of the Faculty of Engineering of the University of Porto, named Project FEUP. In this class, mandatory for all students, they learn to how use these motors, from basic concepts to the estimation modeling. The paper presents the developed kits that students use, the simplified model and examples of the experiments performed in some classes.

Abstract
In this paper the proposal of a low cost high performance educational mobile robot is described. The robot is based on an Arduino, applied in the low level control, while the high level control loop is carried out by an RPI running an object pascal application. The described robot was prototyped in order to have a competitive participation in the Robotic Day Line Follower 2017 competition, taking advantage of the RPI capabilities. The RPI allows the use of higher performance sensors, when compared with the most common standard approaches based on a single 8 bit RISC micro-controller, having as disadvantage the inevitable robot size increase, which compromises in certain situations the robot maneuverability and increases the power consumption. The robot is equipped with DC Motors, the chosen line follower sensor is the picamera and for the obstacle detection sonar sensors are used. © CLAWAR Association.