Abstract
Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in areal setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.

Abstract
Semantic segmentation is a relevant process for creating the rich semantic maps required for indoor navigation by autonomous robots. While foundation models like Segment Anything Model (SAM) have significantly advanced the field by enabling object segmentation without prior references, selecting an efficient variant for real-time robotics applications remains a challenge due to the trade-off between performance and accuracy. This paper evaluates three such variants — FastSAM, MobileSAM, and SAM 2 — comparing their speed and accuracy to determine their suitability for semantic mapping tasks. The models were assessed within the Robot@VirtualHome dataset across 30 distinct scenes, with performance quantified using Frames Per Second (FPS), Precision, Recall, and an Over-Segmentation metric, which quantifies the fragmentation of an object into multiple masks, preventing high quality semantic segmentation. The results reveal distinct performance profiles: FastSAM achieves the highest speed but exhibits poor precision and significant mask fragmentation. Conversely, SAM 2 provides the highest precision but is computationally intensive for real-time applications. MobileSAM emerges as the most balanced model, delivering high recall, good precision, and viable processing speed, with minimal over-segmentation. We conclude that MobileSAM offers the most effective trade-off between segmentation quality and efficiency, making it a good candidate for indoor semantic mapping in robotics.

Abstract
This paper compares five PID controller architectures for robotic manipulator position control, addressing the challenge of maintaining performance under varying inertial loads while providing accessible implementations for research and education. The five PID controller architectures for a three degrees-of-freedom SCARA manipulator position control are a basic Proportional-Derivative (PD), PD with Feed-Forward (FF), Parallel PD-PI-FF, Cascade PD-PI-FF, and Cascade PD-PI-FF with dead zone (DZ) compensation. The controllers were evaluated under varying inertial loads to assess robustness, extending beyond previous work's idealized conditions. Results show advanced configurations reduced errors by up to 64% compared to the baseline PD, with Parallel-FF achieving optimal dynamic performance and Cascade-FF-DZ excelling in steady-state control. The Feed-Forward addition enhanced tracking performance, while DZ compensation effectively eliminated limit cycles. The work provides open-source implementations and simulation environments, supporting research reproducibility and educational applications in robotics control.

Abstract
Integrating physical robots in an educational context often entails acquiring expensive equipment that often operates using proprietary software. Both conditions restrict the students from exploring and fully understanding the internal operation of robots. In response to these limitations, a three-degree-of-freedom robotic manipulator, based on the “EEZYbotARM MK2” open-source design by Carlo Franciscone, is being repurposed and integrated within the SimTwo simulation environment to operate within a hardware-in-the-loop architecture. To accomplish this objective, first, an open-source Arduino-based library was developed aiming at the robot’s online and offline programming akin to industrial robots. The firmware is able to communicate with the SimTwo software in which the digital twin’s robot is living. The dynamic behavior of the robot’s digital twin must be properly parametrized and aligned with the physical robot’s dynamics. This article describes the modeling of the robot joint’s actuator and its closed-loop controller formulation. The obtained results show that the dynamic behavior of the robot joint digital twin closely matches both open and closed-loop, the one of its physical counterpart.

Abstract
Force sensors are essential elements of actuator systems, providing measurement and force control in different domains. This literature review discusses its applications in the industry, academic research, and educational domains. In an industrial setup, force sensors enhance efficiency, safety, and reliability within automation systems, predominantly robotic arms and assembly lines. In the academic environment, using such sensors fosters innovation within robotics and biomechanical studies, allowing for testing theoretical models and new methodologies. In education, force sensors help students understand basic concepts about mechanics and robotics from practical work. Understanding this diverse application allows one to design effective actuator systems, promoting technological advances and improved learning experiences. With this literary review, the aim is to gain an understanding of the state of the art in force sensor actuators applied in various areas, such as academia, education, and industry.

Abstract
Robotic competitions have been popularly applied in the educational context, proving to be an excellent method for fostering student engagement and interest in science, technology, engineering, and math (STEM). In this context, this paper presents the application of mobile robots in a classroom competition, in order to encourage students to enhance mobile robotics concepts learning in a dynamic and collaborative environment. The mobile robot prototyping is presented, and the methodology, including the Hardware-in-the-loop approach applied in the classrooms, is also described, together with the competition rules and challenges proposed for the students. The results indicated an improvement in students’ motivation, teamwork, communication, and the development of technical skills, computational thinking, and problem-solving.