Abstract
To meet the growing demand of robotics applications in industry high efforts are placed on research and education for robotics fundamentals, as a more intuitive and easier access to robotics must be ensured. Based on this, the aim of this work is to make a contribution to investigate new approaches for teaching robotics fundamentals using immersive technologies. In this work an existing digital twin for a desktop robot was extended by adding a feature for the positioning of way-points for path planning in an existing Augmented Reality (AR) application as well as update the robot controller with the inverse kinematics for the corresponding motion implementation. The performance of the new functionality was evaluated by means of a pick-and-place application and validated via a test series with an associated questionnaire. The conducted experiments have been carried out in a comparative study between the AR based digital twin and an ABB industrial robot where a clear benefit of the proposed systems for entry into robotics as well as for teaching was identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations' efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.

Abstract
This paper presents an approach to address the challenges of manual inspection using multirotor Unmanned Aerial Vehicles (UAV) to detect olive tree flies (Bactrocera oleae). The study employs computer vision techniques based on the You Only Look Once (YOLO) algorithm to detect insects trapped in yellow chromotropic traps. Therefore, this research evaluates the performance of the YOLOv7 algorithm in detecting and quantify olive tree flies using images obtained from two different digital cameras in a controlled environment at different distances and angles. The findings could potentially contribute to the automation of insect pest inspection by UAV-based robotic systems and highlight potential avenues for future advances in this field. In view of the experiments conducted indoors, it was found that the Arducam IMX477 camera acquires images with greater clarity compared to the TelloCam, making it possible to correctly highlight the set of Bactrocera oleae in different prediction models. The presented results in this research demonstrate that with the introduction of data augmentation and auto label techniques on the set of images of Bactrocera oleae, it was possible to arrive at a prediction model whose average detection was 256 Bactrocera oleae in relation to the corresponding ground truth value to 270 Bactrocera oleae.

Abstract

Abstract
This article presents a new firmware update paradigm for optimising the procedure in microcontrollers. The aim is to allow updating during program execution, without interruptions or restarts, replacing only specific code segments. The proposed method uses static and absolute addresses to locate and isolate the code segment to be updated. The work focuses on Microchip's PIC18F27K42 microcontroller and includes an example of updating functionality without affecting ongoing applications. This approach is ideal for band limited channels, reducing the amount of data transmitted during the update process. It also allows incremental changes to the program code, preserving network capacity, and reduces the costs associated with data transfer, especially in firmware update scenarios using cellular networks. This ability to update the normal operation of the device, avoiding service interruption and minimising downtime, is of remarkable value.

Abstract
STEM education advances at the same rate as the need for new and more evolved tools. This article introduces the latest version of the Kid Grígora educational robot, based on the work of Barradas et al. (2019). Targeted for students aged 8 to 18, the robot serves as an interdisciplinary teaching tool, integrated into STEM curricula. The upgraded version corrects what we’ve learned from a real test with 177 students from a Portuguese school and adds other features that allow this new robot to be used in even more educational STEM and problem-solving scenarios. We focused on the creation of a second beta version of the prototype, named Stemie, and its heuristic evaluation by three experts. After all the issues and suggestions from the experts have been resolved and implemented, the new version is ready for usability evaluation. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.