Abstract
Reliable operation of production lines without unscheduled disruptions is of paramount importance for ensuring the proper operation of automated working cells involving robotic systems. This article addresses the issue of preventing disruptions to an automotive production line that can arise from incorrect placement of aluminum car parts by a human operator in a feeding container with 4 indexing pins for each part. The detection of the misplaced parts is critical for avoiding collisions between the containers and a high pressure washing machine and also to avoid collisions between the parts and a robotic arm that is feeding parts to a air leakage inspection machine. The proposed inspection system relies on a 3D sensor for scanning the parts inside a container and then estimates the 6 DoF pose of the container followed by an analysis of the overlap percentage between each part reference point cloud and the 3D sensor data. When the overlap percentage is below a given threshold, the part is considered as misplaced and the operator is alerted to fix the part placement in the container. The deployment of the inspection system on an automotive production line for 22 weeks has shown promising results by avoiding 18 hours of disruptions, since it detected 407 containers having misplaced parts in 4524 inspections, from which 12 were false negatives, while no false positives were reported, which allowed the elimination of disruptions to the production line at the cost of manual reinspection of 0.27% of false negative containers by the operator.

Abstract
Robot Learning is one of the most important areas in Robotics and its relevance has only been increasing. The Robot Operating System (ROS) has been one of the most used architectures in Robotics but learning it is not a simple task. Additionally, ROS 1 is reaching its end-of-life and a lot of users are yet to make the transition to ROS 2. Reinforcement Learning (RL) and Robotics are rarely taught together, creating greater demand for tools to teach all these components. This paper aims to develop a learning kit that can be used to teach Robot Learning to students with different levels of expertise in Robotics. This kit works with the Flatland simulator using open-source free software, namely the OpenAI Gym and Stable-Baselines3 packages, and contains tutorials that introduce the user to the simulation environment as well as how to use RL to train the robot to perform different tasks. User tests were conducted to better understand how the kit performs, showing very positive feedback, with most participants agreeing that the kit provided a productive learning experience.

Abstract
Climate change, limited natural resources, and the increase in the world's population impose society to produce food more sustainably, with lower energy and water consumption. The use of robots in agriculture is one of the most promising solutions to change the paradigm of agricultural practices. Agricultural robots should be seen as a way to make jobs easier and lighter, and also a way for people who do not have agricultural skills to produce their food. The PixelCropRobot is a low-cost, open-source robot that can perform the processes of monitoring and watering plants in small gardens. This work proposes a mission supervisor for PixelCropRobot, and general agricultural robots, and presents a prototype of user interface to this mission supervision. The communication between the mission supervisor and the other components of the system is done using ROS2 and MQTT, and mission file standardized. The mission supervisor receives a prescription map, with information about the respective mission, and decomposes them into simple tasks. An A* algorithm then defines the priority of each mission that depends on factors like water requirements, and distance travelled. This concept of mission supervisor was deployed into the PixelCropRobot and was validated in real conditions, showing a enormous potential to be extended to other agricultural robots.

Abstract
This paper explores the use of Robotics and decentralized Multi-Agent Reinforcement Learning (MARL) for side-by-side navigation in Intelligent Wheelchairs (IW). Evolving from a previous work approach using traditional single-agent methodologies, it adopts a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to provide control input and enable a pair of IW to be deployed as decentralized computing agents in real-world environments, discarding the need to rely on communication between each other. In this study, the Flatland 2D simulator, in conjunction with the Robot Operating System (ROS), is used as a realistic environment to train and test the navigation algorithm. An overhaul of the reward function is introduced, which now provides individual rewards for each agent and revised reward incentives. Additionally, the logic for identifying side-by-side navigation was improved, to encourage dynamic alignment control. The preliminary results outline a promising research direction, with the IWs learning to navigate in various realistic hallways testing scenarios. The outcome also suggests that while the MADDPG approach holds potential over single-agent techniques for the decentralized IW robotics application, further investigation are needed for real-world deployment.

Abstract
Faced with the current unsustainability and recognizing the importance of engineering (and technology) in the Capitalocene, it is important to develop educational approaches that facilitate the awareness and training of engineering students to the sustainable future’s construction. The main objective of the study is the evaluation of the educational approach developed (educational board game). It was used an action-research methodology and a quasi-experimental method. These results show that the developed game can be an important contribution in the engineers training to change the role of engineering to an ethical and responsible construction of a sustainable and fair future. © 2024 Taylor & Francis Group, LLC.

Abstract
Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.