Abstract
In this paper it is presented the stochastic modeling of a time of flight sensor, to be applied in a mobile robotics application. The sensor was configured to provide data at a frequency 30 Hz, obtaining a tradeoff between reactiveness and accuracy. The sensor data was acquired using a microcontroller development board, being the sensor moved with a manipulator, in order to assure repeatability and accuracy in the data acquisition process. The sensor was modeled having in mind the targets color, ranging from black to white for the working range, its variance, standard deviation, offset, means and errors measures were estimated.

Abstract
Robotics competitions encourage the development of solutions to new challenges that emerge in sync with the rise of Industry 4.0. In this context, robotic simulators are employed to facilitate the development of these solutions by disseminating knowledge in robotics, Education 4.0, and STEM. The RobotAtFactory 4.0 competition arises to promote improvements in industrial challenges related to autonomous robots. The official organization provides the simulation scene of the competition through the open-source SimTwo simulator. This paper aims to integrate the SiwTwo simulator with the Robot Operating System (ROS) middleware by developing a framework. This integration facilitates the design of robotic systems since ROS has a vast repository of packages that address common problems in robotics. Thus, competitors can use this framework to develop their solutions through ROS, allowing the simulated and real systems to be integrated.

Abstract
Nowadays, the automation of factory floors is necessary for extensive manufacturing processes to meet the ever-increasing competitiveness of current markets. The technological advances applied to the digital platforms have led many businesses to automate their manufacturing processes, introducing robotic manipulators collaborating with human operators to achieve new productivity, manufacturing quality, and safety levels. However, regardless of the amount of optimization implemented, some quality problems may be introduced in production lines with many products being designed and produced. This project proposes a solution for feature extraction that can be applied to automatic shape- and position-detection using a 2-dimension (2D) industrial laser to extract 3-dimension (3D) data where the movement of the item adds the third dimension through the laser's beam. The main goal is data acquisition and analysis. This analysis will later lead to the generation of trajectories for a robotic manipulator. The results of this application proved reliable given their small measurement error values of a maximum of 2 mm.

Abstract
Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code's validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.

Abstract
Forests are remote areas with uneven terrain, so it is costly to map the range of signals that enable the implementation of systems based on wireless and long-distance communication. Even so, the interest in Internet of Things (IoT) functionalities for forest monitoring systems has increasingly attracted the attention of several researchers. This work demonstrates the development of a platform that uses the GPS technology of mobile devices to map the signals of a LoRaWAN Gateway. Therefore, the proposed system is based on concatenating two messages to optimize the LoRaWAN transmission using the Global Position System (GPS) data from a mobile device. With the proposed approach, it is possible to guarantee the data transmission when finding the ideal places to fix nodes regarding the coverage of LoRaWAN because the Gateway bandwidth will not be fulfilled. The tests indicate that different changes in the relief and large bodies drastically affect the signal provided by the Gateway. This work demonstrates that mapping the Gateway's signal is essential to attach modules in the forest, agriculture zones, or even smart cities.

Abstract
The urge for robust and reliable localization systems for autonomous mobile robots (AMR) is increasing since the demand for these automated systems is rising in service, industry, and other areas of the economy. The localization of AMRs is one of the crucial challenges, and several approaches exist to solve this. The most well-known localization systems are based on LiDAR data due to their reliability, accuracy, and robustness. One standard method is to match the reference map information with the actual readings from LiDAR or camera sensors, allowing localization to be performed. However, this approach has difficulties handling anything that does not belong to the original map since it affects the matching algorithm's performance. Therefore, they should be considered outliers. In this paper, a deep learning-based object detection algorithm is not only used for detection but also to classify them as outliers from the localization's perspective. This is an innovative approach to improve the localization results in a realmobile platform. Results are encouraging, and the proposed methodology is being tested in a real robot.