Abstract
This paper presents a method for position correction in collaborative robots, applied to a case study in an industrial environment. The case study is aligned with the GreenAuto project and aims to optimize industrial processes through the integration of various hardware elements. The case study focuses on tightening a specific number of nuts onto bolts located on a partition plate, referred to as “Cloison”, which is mounted on commercial vans produced by Stellantis, to secure the plate. The main challenge lies in deviations that may occur in the plate during its assembly process, leading to uncertainties in its fastening to the vehicles. To address this and optimize the process, a collaborative robot was integrated with a 3D vision system and a screwdriving system. By using the 3D vision system, it is possible to determine the bolts’ positions and adjust them within the robot’s frame of reference, enabling the screwdriving system to tighten the nuts accurately. Thus, the proposed method aims to integrate these different systems to tighten the nuts effectively, regardless of the deviations that may arise in the plate during assembly. © 2025 by the authors.

Abstract
This study demonstrates the application of a tomography-like (TL) method to monitor grape maturation dynamics over two growing seasons (2021-2022) in the Douro Wine Region. Using a Vis-NIR point-of-measurement sensor, which employs visible and near-infrared light to penetrate grape tissues non-destructively and provide spectral data to predict internal composition, this approach captures non-destructive measurements of key physicochemical properties, including soluble solids content (SSC), weight-to-volume ratio, chlorophyll and anthocyanin levels across internal grape tissues-skin, pulp, and seeds-over six post-veraison stages. The collected data were used to generate detailed metabolic maps of maturation, integrating topographical factors such as altitude and NDVI-based (normalised difference vegetation index) vigour assessments, which revealed significant (p < 0.05) variations in SSC, chlorophyll, and anthocyanin levels across vineyard zones. The metabolic maps generated from the TL method enable high-throughput data to reveal the impact of environmental variability on grape maturation across distinct vineyard areas. Predictive models using random forest (RF) and self-learning artificial intelligence (SL-AI) algorithms showed RF's robustness, achieving stable predictions with R-2 >= 0.86 and MAPE <= 33.83 %. To illustrate the TL method's practical value, three hypothetical decision models were developed for targeted winemaking objectives based on SSC, chlorophyll in the pulp, and anthocyanin in the skin and seeds. These models underscore the TL method's ability to support site-specific management (SSM) by providing actionable agricultural practices (e.g. harvest) into vineyard management, guiding winemakers to implement tailored interventions based on metabolic profiles rather than only cultivar characteristics. This precision viticulture (PV) approach enhances wine quality and production efficiency by aligning vineyard practices with specific wine quality goals.

Abstract
The present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures. Research and development of new sensors for this purpose are currently a challenge. Present procedures and diagnosis techniques depend on visual characteristics and symptoms to be initiated and applied, compromising an early intervention. Also, these methods were designed to confirm the presence of pathogens, which did not have the required high throughput and speed to support real-time agronomic decisions in field extensions. Proximal sensor-based systems are a reasonable tool for an efficient and economic disease assessment. This work focused on identifying the application of optical and spectroscopic sensors as a tool for disease diagnosis. Biophoton emission, fluorescence spectroscopy, laser-induced breakdown spectroscopy, multi- and hyperspectral spectroscopy (HS), nuclear magnetic resonance spectroscopy, Raman spectroscopy, RGB imaging, thermography, volatile organic compounds assessment, and X-ray fluorescence were described due to their relevant potential. Nevertheless, some techniques revealed a low technology readiness level (TRL). The main conclusions identify HS, single and multi-spatial point observation, as the most applied methods for early plant disease diagnosis studies (88%), combined with distinct feature selection (FeS), dimensionality reduction (DR), and modeling techniques. Vegetation indices (28%) and principal component analysis (19%) were the most popular FeS and DR approaches, highlighting the most relevant wavelengths contributing to disease diagnosis. In modeling, classification was the most applied technique (80%), used mainly for binary and multi-class health status identification. Regression was used in the remaining (21%) scientific works screened. The data was collected primarily in laboratory conditions (62%), and a few works were performed in field conditions (21%). Regarding the study's etiological agent responsible for causing the disease, fungi (53%) and viruses (23%) were the most analyzed group of pathogens found in the literature. Overall, proximal sensors are suitable for early plant disease diagnosis before and after symptom appearance, presenting classification accuracies mostly superior to 71% and regression coefficients superior to 61%. Nevertheless, additional research regarding the study of specific host-pathogen interactions is necessary.

Abstract
Continuous Integration and Continuous Deployment are known methodologies for software development that increase the overall quality of the development process. Several robotic software repositories make use of CI/CD tools as an aid to development. However, very few CI pipelines take advantage of using cloud computing to run simulations. Here, a CI pipeline is proposed that takes advantage of such features, applied to the development of ATOM, a ROS-based application capable of carrying out the calibration of generalized robotic systems. The proposed pipeline uses GitHub Actions as a CI/CD engine, AWS RoboMaker as a service for running simulations on the cloud and Rigel as a tool to both containerize ATOM and execute the tests. In addition, a static analysis and unit testing component is implemented with the use of Codacy. The creation of the pipeline was successful, and it was concluded that it constitutes a valuable tool for the development of ATOM and a blueprint for the creation of similar pipelines for other robotic systems.

Abstract
Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository. © 2025 IEEE.

Abstract
Robotics education plays a crucial role in developing STEM skills. However, university-level courses often emphasize theoretical learning, which can lead to decreased student engagement and motivation. In this paper, we tackle the challenge of providing hands-on robotics experience in higher education by adapting a mobile robot originally designed for competitions to be used in laboratory classes. Our approach integrates real-world robot operation into coursework, bridging the gap between simulation and physical implementation while maintaining accessibility. The robot's software is developed using ROS, and its effectiveness is assessed through student surveys. The results indicate that the platform increases student engagement and interest in robotics topics. Furthermore, feedback from teachers is also collected and confirmed that the platform boosts students' confidence and understanding of robotics. © 2025 IEEE.