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.

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
The 10th edition of the Technological Ecosystems for Enhancing Multiculturality (TEEM 2022) brings together researchers and postgraduate students interested in combining different aspects of the technology applied to knowledge society development, with particular attention to educational and learning issues. This volume includes contributions related to communication, educational assessment, sustainable development, educational innovation, mechatronics, and learning analytics. Besides, the doctoral consortium papers close the proceedings book from a transversal perspective. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
The aspect of energy constraint and simulation of battery behavior in robotic simulators has been partially neglected by most of the available simulation software and is offered unlimited energy instead. This lack does not reflect the importance of batteries in robots, as the battery is one of the most crucial elements. With the implementation of an adequate battery simulation, it is possible to perform a study on the energy requirements of the robot through these simulators. Thus, this paper describes a Lithium-ion battery model implemented on SimTwo robotic simulator software, in which various physical parameters such as internal resistance and capacity are modeled to mimic real-world battery behavior. The experiments and comparisons with a real robot have assessed the viability of this model. This battery simulation is intended as an additional tool for the roboticists, scientific community, researchers, and engineers to implement energy constraints in the early stages of robot design, architecture, or control.

Abstract
The robot presented in this paper was developed with the main focus on participating in robotic competitions. Therefore, the subsystems here presented were developed taking into account performance criteria instead of simplicity. Nonetheless, this paper also presents background knowledge in some basic concepts regarding robot localization, navigation, color identification and control, all of which are key for a more competitive robot.

Abstract
The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.