Abstract
The manufacturing domain faces a challenge in making timely decisions due to the large amounts of data generated by digital technologies such as Internet-of-Things, Artificial Intelligence (AI), Digital Twin, and Big Data. By integrating recommendation systems is possible to support the decision-makers in handling large amounts of data by delivering personalised, accurate, and quality recommendations. One example is the SimQL recommendation model that incorporates AI algorithms with trust and similarity measures to enhance recommendation quality. This paper aims to analyse the sensitivity of the SimQL model's parameters, such as dataset conditions, trust and learning factors, and their impact on the final recommendation quality. A fuzzy logic approach is employed to evaluate the model and identify optimal operating conditions for the recommendation system. By implementing the findings of this study, manufacturers can improve the acceptance and adoption of the SimQL trustworthy recommendation system in this field.

Abstract
Robotic competitions stand as platforms to propel the forefront of robotics research while nurturing STEM education, serving as hubs of both applied research and scientific innovation. In Portugal, the Portuguese Robotics Open (FNR) is an event with several robotic competitions, including the Robot@Factory 4.0 competition. This competition presents an example of deploying autonomous robots on a factory shop floor. Although the literature has works proposing frameworks for the original version of the Robot@Factory competition, none of them proposes a system framework for the Robot@Factory 4.0 version that presents the hardware, firmware, and software to complete the competition and achieve autonomous navigation. This paper proposes a complete robotic framework for the Robot@Factory 4.0 competition that is modular and open-access, enabling future participants to use and improve it in future editions. This work is the culmination of all the knowledge acquired by winning the 2022 and 2023 editions of the competition.

Abstract
The extraction of geometric information from the environment may be of interest to localisation and mapping algorithms. Existent literature on extracting geometric features from 2D laser data focuses mainly on detecting lines. Regarding corners, most methodologies use the intersection of line segment features. This paper presents a feature extraction algorithm for corner-like points in the 2D laser scan. The proposed methodol-ogy defines arrival and departure neighbourhoods around each scan point and performs local line fitting evaluated in multiple distance-based scales. Then, a set of indicators based on line fitting error, the angle between arrival and departure lines, and consecutive observation of the same keypoint across different scales determine the existence of a corner-like feature. The experiments evaluated the corner-like features regarding their relative position and observability, achieving standard deviations on the relative position lower than the sensor noise and visibility ratios higher than 75% with very low false positives rates.

Abstract
Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.

Abstract
Current industrial environments have multiple robots working alongside humans, thus providing an operator the ability to perceive the robot's workspace correctly and to anticipate its intentions and movements through the visualization of the robot's digital twin is of utmost importance for safe and productive human-robot collaboration scenarios. Much has been studied regarding single human-single robot collaborative scenarios, but few address multi-user multi-robot scenarios. To this end, this paper presents a multi-robot multi-operator architecture, where the users' awareness is enhanced through an augmented reality head-mounted display. A multi-robot, multi-user collaborative scenario is presented in a laboratory environment with two industrial robots. Besides being able to interact with both robots in the system, each user becomes more aware of the robot's workspace and its pre-defined trajectories. Furthermore, it presents how fiducial markers can help to establish the relation between the different coordinate frames.

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.