Abstract
PurposeThe purpose of the article is to develop an innovative pedagogic tool: an escape room board game to be played in-class, targeting an introduction to an ethics course for engineering students. The design is student-centred and aims to increase students' appreciation, commitment and motivation to learning ethics, a challenging endeavour for many technological students.Design/methodology/approachThe methodology included the design, development and in-class application of the mentioned game. After application, perception data from students were collected with pre- and post-action questionnaire, using a quasi-experimental method.FindingsThe results allow to conclude that the developed game persuaded students be in class in an active way. The game mobilizes body and mind to the learning process with many associated advantages to foster students' motivation, curiosity, interest, commitment and the need for individual reflection after information search.Research limitations/implicationsThe main limitation of the game is its applicability to large classes (it has been successfully tested with a maximum of 65 students playing simultaneously in the same room).Originality/valueThe originalities and contributions include the presented game that helped to captivate students to ethics area, a serious problem felt by educators and researchers in this area. This study will be useful to educators of ethics in engineering and will motivate to design tools for a similar pedagogical approach, even more so in areas where students are not especially motivated. The developed tool is available from the authors at no expense.

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
Autonomous Mobile Robots (AMRs) have significantly transformed task management in factories, warehouses, and urban environments. These robots enhance operational efficiency, reduce labor costs, and automate various tasks. However, navigating dynamic environments with moving obstacles, such as human workers, vehicles, and machinery, remains challenging. Traditional navigation systems, which rely on static maps and predefined routes, struggle to adapt to these dynamic settings. This research addresses these limitations by developing a dynamic navigation system that improves AMR performance in industrial and urban scenarios. The system enhances the A* algorithm to account for the current positions and predicted trajectories of moving obstacles, allowing the AMR to navigate safely and efficiently. Advanced sensor technologies, such as LiDAR and stereo cameras, are utilized for real-time environmental perception. The system integrates trajectory prediction and an Artificial Potential Field (APF) method for emergency collision avoidance. The solution is implemented using the Gazebo simulator and the Robot Operating System (ROS2), ensuring real-time operation and adaptive path planning. This research aims to significantly improve AMR safety, efficiency, and adaptability in dynamic environments.

Abstract
Robotics has emerged as a cornerstone of modern society, significantly impacting diverse sectors including industry, healthcare, and defense. Among its varied applications, one of the most crucial fields is the control of rigid-structure robotic manipulators. However, conventional robotic arms are typically highly specialized and rigid in design, which limits their adaptability to different tasks and environments. One promising solution to this challenge is the development of modular robotic manipulators. This work proposes a cost-effective approach for implementing a n-Degrees-of-Freedom (DoF) manipulator. It introduces a design consisting of 3D printable links that allow for flexible assembly into custom configurations. A reconfigurable software architecture is presented, enabling automated generation of description and configuration files. This facilitates visualization, planning, and control of various custom configurations. The solution leverages the open-source Robot Operating System (ROS) as a digital twin for the modular setups. Additionally, it explores the development of hardware modules accompanying each link, facilitating independent joint control irrespective of motor type. Communication with ROS software is achieved via a CAN-based OpenCyphal network.

Abstract
Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often experience limitations due to weight shifts during gait transitions. These weight shifts can cause torque peaks that exceed the capacity of the jointmotors (overdrive torque), which lead to an increased risk of mechanical failure. Through the optimization of gait parameters, it is possible to reduce these risks while maximizing performance. This paper presents the use of multi-objective optimization algorithms for gait optimization in a simulated quadruped mammal robot within the Pybullet physics engine. The main focus of the study was to compare the performance of NSGA-II, NSGA-III and U-NSGA-III in minimizing overdrive torque while maximizing travel distance. The results showed that the three algorithms solve this problem, although the NSGA-III consistently yields better results in comparison to the other versions of the NSGA algorithm.

Abstract
Several approaches have been developed over time aiming to improve the localization aspects, especially in mobile robotics. Besides the more traditional techniques, mainly based on analytical models, artificial intelligence has emerged as an interesting alternative. The current study proposes to explore the machine learning model structure optimization for pose estimation, using the RobotAtFactory 4.0 competition as the main context. Using a Bayesian Optimization-based framework, the parameters of a Multi-Layer Perceptron (MLP) model, trained to estimate the components of the 2D pose (x, y, and theta) of the robot were optimized in four different scenarios of the same context. The results obtained showed a quality improvement of up to 60% on the estimation when compared with the modes without any optimization. Another aspect observed was the different optimizations found for each model, even in the same scenario. An additional interesting result was the possibility of the reuse of optimization between scenarios, presenting an interesting approach to reduce time and computational resources.