Abstract
Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Abstract
Industry 4.0 has brought innovative concepts and technologies that have greatly improved the development of more intelligent, flexible and reconfigurable systems. Two of these concepts, Cyber-Physical Systems (CPSs) and Digital Twins (DTs), have gained significant attention from various stakeholders, e.g., researchers, industry practitioners, and governmental organizations. Both are vital to support the digitalisation of products, machines, and systems, and they focus on the integration of physical and cyber processes, where one affects the other through feedback loops. Having this in mind, this paper aims to better understand how CPS and DT are correlated, particularly exploring their similarities and differences, their positioning within the Industry 4.0 paradigm, and their convergence to develop Industry 4.0 solutions. Some research challenges to develop Industry 4.0 solutions by integrating these concepts are also discussed.

Abstract

Abstract
The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS's growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system's capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers. © 2024 IEEE.

Abstract
Autonomous Mobile Robots (AMR) are seeing an increased introduction in distinct areas of daily life. Recently, their use has expanded to intralogistics, where forklift type AMR are applied in many situations handling pallets and loading/unloading them into trucks. One of the these vehicles requirements, is that they are able to correctly identify the location and status of pallets, so that the forklifts AMR can insert the forks in the right place. Recently, some commercial sensors have appeared in the market for this purpose. Given these considerations, this paper presents a comparison of the performance of two different approaches for pallet detection: using a commercial off-the-shelf (COTS) sensor and a custom developed application based on Artificial Intelligence algorithms applied to an RGB-D camera, where both the RGB and depth data are used to estimate the position of the pallet pockets.

Abstract
Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. The wide range of AMR's applications and the characteristics of multiple industrial environments (indoor and outdoor) have led to the development of a flexible and robust robot software architecture that allows the fusion of different data sensors in real time. In this way, and in terms of localization, AMRs have greater precision when it comes to uncontrolled and unstructured environments. These complex environments feature a variety of dynamic and unpredictable elements, such as variable layouts, limited visibility, unstructured spaces, and uncertain terrain. This paper presents a multi-localization system for industrial mobile robots in complex and dynamic industrial scenarios, based on different localization technologies and methods that can interact together and simultaneously.