Abstract
Assessing the different factors that contribute to accidents in the workplace is essential to ensure the safety and well-being of employees. Given the importance of risk identification in hazard prediction, this work proposes a comparative study between different feature selection techniques (.2 test and Forward Feature Selection) combined with learning algorithms (Support VectorMachine, Random Forest, and Naive Bayes), both applied to a database of a leading company in the retail sector, in Portugal. The goal is to conclude which factors of each database have the most significant impact on the occurrence of accidents. Initial databases include accident records, ergonomic workplace analysis, hazard intervention and risk assessment, climate databases, and holiday records. Each method was evaluated based on its accuracy in the forecast of the occurrence of the accident. The results showed that the Forward Feature Selection-Random Forest pair performed better among the assessed combinations, considering the case study database. In addition, data from accident records and ergonomic workplace analysis have the largest number of features with the most significant predictive impact on accident prediction. Future studies will be carried out to evaluate factors from other databases that may have meaningful information for predicting accidents.

Abstract
Municipal Solid Waste Management Systems (MSWMS) worldwide are currently facing pressure due to the rapid growth of the population in cities. One of the biggest challenges in this system is the inefficient expenditure of time and fuel in waste collection. In this regard, cities/municipalities in charge of MSWMS could take advantage of information and communication technologies to improve the overall quality of their infrastructure. One particular strategy that has been explored and is showing interesting results is using a Wireless Sensors Network (WSN) to monitor waste levels in real-time and help decision-making regarding the need for collection. The WSN is equipped with sensing devices that should be carefully chosen considering the real scenario in which they will work. Therefore, in this work, three sets of sensors were studied to evaluate which is the best to be used in the future WSN assembled in Braganca, Portugal. Sets tested were HC-SR04 (S1), HC-SR04 + DHT11 (S2), and US-100 (S3). Tests considered for this work were air temperature and several distances. In the first, the performance of each set to measure a fixed target (metal and plastic box) was evaluated under different temperatures (1.7-37 degrees C). From these results, two best sets were further used to assess distance measurement at a fixed temperature. This test revealed low absolute errors measuring the distances of interest in this work, ranging from 0.18% to 1.27%.

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.

Abstract
Robotic competitions are an excellent way to promote innovative solutions for the current industries' challenges and entrepreneurial spirit, acquire technical and transversal skills through active teaching, and promote this area to the public. In other words, since robotics is a multidisciplinary field, its competitions address several knowledge topics, especially in the STEM (Science, Technology, Engineering, and Mathematics) category, that are shared among the students and researchers, driving further technology and science. A new competition encompassed in the Portuguese Robotics Open was created according to the Industry 4.0 concept in the production chain. In this competition, RobotAtFactory 4.0, a shop floor, is used to mimic a fully automated industrial logistics warehouse and the challenges it brings. Autonomous Mobile Robots (AMRs) must be used to operate without supervision and perform the tasks that the warehouse requests. There are different types of boxes which dictate their partial and definitive destinations. In this reasoning, AMRs should identify each and transport them to their destinations. This paper describes an approach to the indoor localization system for the competition based on the Extended Kalman Filter (EKF) and ArUco markers. Different innovation methods for the obtained observations were tested and compared in the EKF. A real robot was designed and assembled to act as a test bed for the localization system's validation. Thus, the approach was validated in the real scenario using a factory floor with the official specifications provided by the competition organization.

Abstract

Abstract
Three-dimensional scanning is a task of great importance for our modern society and has brought significant advances in the precision of material inventory. These sensors map the material surface continuously, allowing real-time inventory monitoring. Most technologies are expensive because this process is complex, and even inexpensive ones are considerate smart investments for the average user. Therefore, this work presents the simulation of a low-cost time-of-flight based 3D scanning system that performs the volume estimation of an object-filled indoor space after a voxelization process. The system consists of a 2D LIDAR scanner that performs an azimuthal scan of 180. through its rotating platform and a motor that rotates the platform in angle inclination.