Abstract
Welding is a challenging, risky, and time-consuming profession. Recently, there has been a documented shortage of trained welders, and as a result, the market is pushing for an increase in the rate at which new professionals are trained. To address this growing demand, training institutions are exploring alternative methods to train future professionals. The emergence of virtual reality technologies has led to initiatives to explore their potential for welding training. Multiple studies have suggested that virtual reality training delivers comparable, or even superior, results when compared to more conventional approaches, with shorter training times and reduced costs in consumables. This paper conducts a comprehensive review of the current state of the field of welding simulators. This involves exploring the different types of welding simulators available and evaluating their effectiveness and efficiency in meeting the learning objectives of welding training. The aim is to identify gaps in the literature, suggest future research directions, and promote the development of more effective and efficient welding simulators in the future. The research also seeks to develop a categorical system for evaluating and comparing welding simulators. This system will enable a more systematic and objective analysis of the features and characteristics of each simulator, identifying the essential characteristics that should be included in each level of classification.

Abstract
The footwear industry is known for its longstanding traditional production methods that require intense manual labor. Roughing, for example, is regarded as one of the significant and critical operations in shoe manufacturing and consists of using abrasive tools to remove a thin layer of the shoe's surface, creating a slightly roughened texture that provides a better surface area for adhesion. As such, workers are typically subjected to hazardous substances (i.e., dust, chromium), repetitive strain injuries, and ergonomic challenges. Although robots can automate repetitive tasks and perform with high precision and consistency, the footwear industry is usually reluctant to employ industrial robots due to the need for restructuring. This paper addresses the challenge of re-designing the lateral roughing of uppers to allow robot-assisted manufacturing with minimal modifications in the manufacturing process. The proposed innovative system employs a robotic manipulator to perform roughing based on data collected from preceding manufacturing steps. Workers marking the mesh line of each sole-upper pair can simultaneously teach the manipulator path for that same pair, using a programming-by-demonstration approach. Multiple paths were collected by outlining a piece of footwear, converted into robot instructions, and deployed on a simulated and real industrial manipulator. The key findings of this research showcase the capability of the proposed solution to replicate collected paths accurately, indicating potential applications not only in roughing processes but also in similar tasks like primer and adhesive application.

Abstract
Emerging from a rich heritage, the shoe manufacturing industry stands as one of the world’s most enduring and tradition-bound sectors. While renowned for their high-quality craftsmanship, countries like Portugal and Italy share the spotlight with those who focus on mass production methods. Regardless of their manufacturing model, both must adapt to the evolving competitive landscape by embracing innovative manufacturing techniques. Robotics has emerged as a transformative force within the shoe industry, offering a path towards enhanced working conditions for employees while simultaneously reducing reliance on manual labor and bolstering productivity. The main focus of this paper is the comprehensive literature review, which examines the advancements made by researchers in various stages of shoe production, including roughing, gluing, finishing, and lasting. This article sheds light on the industry’s response to modernization and efficiency imperatives, providing a thorough understanding of robotics in shoe manufacturing automation. A case study on the real implementation and simulation of a robotic cell for sole roughing is also presented. The results revealed that the robotic cell maintains the production cadence.

Abstract
Numerous pose estimation methodologies demonstrate a decrement in accuracy or efficiency metrics when subjected to highly cluttered scenarios. Currently, companies expect high-efficiency robotic systems to close the gap between humans and machines, especially in logistic operations, which is highlighted by the requirement to execute operations, such as navigation, perception and picking. To mitigate this issue, the majority of strategies augment the quantity of detected and matched features. However, in this paper, it is proposed a system which adopts an inverse strategy, for instance, it reduces the types of features detected to enhance efficiency. Upon detecting 2D polygons, this solution perceives objects, identifies their corners and edges, and establishes a relationship between the features extracted from the perceived object and the known object model. Subsequently, this relationship is used to devise a weighting system capable of predicting an optimal final pose estimation. Moreover, it has been demonstrated that this solution applies to different objects in real scenarios, such as intralogistic, and industrial, provided there is prior knowledge of the object's shape and measurements. Lastly, the proposed method was evaluated and found to achieve an average overlap rate of 89.77% and an average process time of 0.0398 seconds per object pose estimation.

Abstract
The use of collaborative robots in industries is growing rapidly. To ensure the successful implementation of these devices, it is essential to consider the user experience (UX) during their design process. This study is aimed at testing the UX goals that emerge when users interact with a collaborative robot during the programming and collaborating phases. A framework on UX goals will be tested, in the geographical context of Portugal. For that, an experimental setup was introduced in the form of a laboratory case study in which the human-robot collaboration (HRC) was evaluated by the combination of both quantitative (applying the User Experience Questionnaire [UEQ]) and qualitative (semistructured interviews) metrics. The sample was constituted by 19 university students. The quantitative approach showed positive overall ratings for the programming phase UX, with attractiveness having the highest average value (M=2.21; SD=0.59) and dependability the lowest (M=1.64; SD=0.65). For the collaboration phase, all UX ratings were positive, with attractiveness having the highest average value (M=2.46; SD=0.78) and efficiency the lowest (M=1.93; SD=0.77). Only perspicuity showed significant differences between the two phases (t18=-4.335, p=0.002). The qualitative approach, at the light of the framework used, showed that efficiency, inspiration, and usability are the most mentioned UX goals emerging from the content analysis. These findings enhance manufacturing workers' well-being by improving cobot design in organizations.

Abstract
Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Crossdocking is a logistics strategy used by several companies from varied economic sectors, applied in warehouses and distribution centres. In this context, it is the objective of the “CrossLog – Automatic Mixed-Palletizing for Crossdocking Logistics Centers” Project, to investigate and study an automated and collaborative crossdocking system, capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In its scope, this paper describes the concept and architecture envisioned for the crossdocking system developed in the scope of the CrossLog Project. One of its main distinguishing characteristics is the use of Autonomous Mobile Robots for performing much of the operations traditionally performed by human operators in today’s logistics centres.