Abstract
Object localization and tracking is core to many practical applications, like human-computer interaction, security and surveillance, robot competitions and Industry 4.0. Such task may be computationally demanding, especially for traditional embedded systems, that usually have tight processing and storage constraints. This calls for the investigation of alternatives, including emergent heterogeneous embedded systems, like the Parallella line of single-board-computers (SBCs). The work presented in this paper explores the use of a Parallella board with a 16-core Epiphany co-processor, to perform real-time tracking of objects in frames captured by a Kinect sensor, based on color segmentation. We addressed several processing strategies, trying to assess which one performed better. We also ran the same code (where applicable) in several models of the Raspberry Pi platform, for comparison. We conclude that effectively exploring the Epiphany co-processor is not trivial, requiring considerable programming effort and suitable applications (CPU-demanding and highly parallelizable), to the extent that simpler development approaches, on more recent SBCs may be more effective. © 2017.

Abstract
Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

Abstract
Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of localisation solutions such as LiDAR, Radio-frequency and acoustic among others. The well-known line follower has been a solution used for a long time ago and still remains its application, especially in competitions for young researchers that should be captivated to the scientific and technological areas. This paper describes two methodologies to estimate the position of a robot placed on a gradient line and compares them. The Least Squares and the Machine Learning methods are used and the results applied to a real robot allow to validate the proposed approach.

Abstract
The experimental component is an essential method in Engineering education. Sometimes the availability of laboratories and components is compromised, and the COVID19 pandemic worsened the situation. Resorting to an accurate simulation seems to help this process by allowing students to develop the work, program, test, and validate it. Moreover, it lowers the development time and cost of the prototyping stages of a robotics project. As a multidisciplinary area, robotics requires simulation environments with essential characteristics, such as dynamics, connection to hardware (embedded systems), and other applications. Thus, this paper presents the Simulation environment of SimTwo, emphasizing previous publications with models of sensors, actuators, and simulation scenes. The simulator can be used for free, and the source code is available to the community. Proposed scenes and examples can inspire the development of other simulation scenes to be used in electrical and mechanical Engineering projects.

Abstract
In a robotics scope, an excellent way to test and improve knowledge is through competitions. In other words, it is possible to follow the results in practice, compare them with the development of other teams and improve the current solutions. The Robot At Factory Lite proposal simulates an Industry 4.0 warehouse scenario, applying education through Science, Technology, Engineering, and Mathematics (STEM) methodology, where the participants have to work on a solution to overcome its challenges. Thus, this article presents an initial electromechanical proposal, which is the basis for developing robots for this competition. The presented main concepts aim to inform the possibilities of using the robot's parts and components. Thus, an idea can be sketched in the participants' minds, inspiring them to use their imagination and knowledge through the presentation of this model.

Abstract
Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.