Abstract
The present article explores the impact of educational robotics on fostering computational thinking and problem-solving skills in elementary school students through a problem-based learning approach. This study involved the creation of a framework which includes a robot and two eBooks designed for students and teachers. The eBooks serve as a guide to the construction and programming of a small Arduino-based robot. Through integration with gamification elements, the model features a narrative with three characters to boost a student's engagement and motivation. Through iteration of heuristic evaluations and practical tests, we refined the initial theoretical framework. An empirical study was conducted in two phases involving 350 students. The first empirical test involved a small group of 21 students, similar to end users, from five European schools. With a 100% completion rate for the tasks, 73.47% of these tasks were solved optimally. Later, we conducted a larger validation study which involved 329 students in a Portuguese school. This second phase of the study was conducted during the 2022-2023 and 2023-2024 school years with three study groups. The results led to a 91.13% success rate in problem-solving activities, and 56.99% of those students achieved optimal solutions. Advanced statistical techniques, including ANOVA, were applied to account for group differences and ensure the robustness of the findings. This study demonstrates that the proposed model which integrates educational robotics with problem-based learning effectively promotes computational thinking and problem-solving skills, which are essential for the 21st century. These findings support the inclusion of robotics into primary school curricula and provide a validated framework for educators.

Abstract
This research contributes to the field of Human-Robot Collaboration (HRC) within dynamic and unstructured environments by extending the previously proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) architecture to handle the uncertainty and irregularity inherent in real-world sensor data. Recognising the challenges posed by low-cost sensors, which are highly susceptible to environmental conditions and often fail to provide regular periodic readings, this paper introduces additional pre-processing blocks. These include two indirect Kalman filters and an additional LSTM network, which together enhance the input variables for the fuzzification process. The enhanced FS-LSTM approach is evaluated using real-world data, demonstrating its effectiveness in extracting meaningful information and accurately recognising human activities. This work underscores the potential of robotics in addressing global challenges, particularly in labour-intensive and hazardous tasks. By improving the integration of humans and robots in unstructured environments, this research contributes to the broader exploration of robotics in new societal applications, fostering connections and collaborations across diverse fields.

Abstract
Forest fires in Portugal are a recurring tragedy, especially during the summer, leaving a devastating trail affecting the environment and local communities. In addition to the loss of vast forest areas, these disasters harm wildlife, pollute the air, and compromise soil and water quality, contributing to environmental degradation and increasing the risk of soil erosion and landslides. Furthermore, fires have significant economic impacts, affecting communities that depend on the forest for subsistence, tourism, and agricultural activities. To address this issue, an innovative Web Service has been developed that uses artificial intelligence algorithms to calculate real-time fire risk. This service integrates up-to-date weather data with historical fire patterns, providing an accurate and timely assessment of fire potential in specific areas. The machine learning model behind the service was trained with historical fire data from mainland Portugal between 2017 and 2023, allowing for a more accurate and predictive analysis of fire risk. The Web Service facilitates proactive emergency prevention and decision-making response by integrating real-time weather information with historical fire data. Authorities can use the information provided by the service to implement preventive policies to help elderly people. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract

Abstract
Human-Robot Collaboration (HRC) in advanced industrial scenarios has emerged as a transformative force. Modern robots, infused with artificial intelligence (AI), can enhance human capabilities, offering a wide spectrum of opportunities in agriculture, forestry, construction and many other domains. However, the complex nature of HRC demands realistic simulators to bridge the gap between theory and practice. This paper introduces the FEROX Simulator, purpose-built for robot-assisted wild berry collection. We briefly delve into the simulator's capabilities to showcase its potential as a platform to develop HRC systems. Our research underscores the need for simulators designed for challenging HRC contexts and aims to inspire further advancements in this domain.

Abstract
In the present paper, we propose a control approach to perform docking of an autonomous surface vehicle (ASV) while avoiding surrounding obstacles. This control architecture is composed of two sequential controllers. The first outputs a feasible trajectory between the vessel's initial and target state while avoiding obstacles. This trajectory also minimizes the vehicle velocity while performing the maneuvers to increase the safety of onboard passengers. The second controller performs trajectory tracking while accounting for the actuator's physical limits (extreme actuation values and the rate of change). The method's performance is tested on simulation, as it enables a reliable ground truth method to validate the control architecture proposed.