Abstract

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
A new generation of manufacturing systems is emerging through the adoption of new policies to overcome future crises highlighted by constant social, environmental, and economic concerns. The rise of so-called smart manufacturing is noticeable. However, new risks to humankind are being introduced, and, more than ever, science and technology are required to guarantee the future sustainability and resilience of our manufacturing systems. This research presents a Digital Twin approach resorting to simulation models with embedded intelligence to transform efficient manufacturing systems and react to complex and unpredictable circumstances. The methodology covers production scheduling incorporating flexible robots, internal logistics supervision contemplating planning and control of mobile robots, and capacity management. The method demonstrates the potential of integrating Additive Manufacturing technologies to quickly react to production needs. The developed strategy was enforced and assessed in an industrial experiment, exhibiting its robustness and promising application. The attained results were very encouraging, highlighting its potential extension to more complex industrial systems.

Abstract
As the need for enhanced material performance continues to escalate in several sectors, addressing complex parameters such as economic feasibility, ease of manufacturing, and production volume, rises the need for multidomain decision-making tools. In order to explore and streamline this process, this study employed the novel Material Design-for-eXcellence methodology to investigate polymer material selection in aeronautical and power transformer components, using additive manufacturing. The study assessed the X's selected (mechanical, thermal, physical, cost, dielectric, and environmental) by assigning weights to these factors, and identifying the optimal materials for each application. In the aeronautical context, PEI+GF30 was chosen as the best solution, attaining an overall effectiveness of 79 %, primarily due to its exceptional mechanical characteristics. The use of a thermoplastic can lead to lighter components while ensuring the same technical performance, enabling longer flight duration. Conversely, in the energy sector for power transformers, PSU obtained a 78 % score, largely attributable to its outstanding dielectric properties. The application of additive methods on transformers' insulating parts leads to optimized channels for the mineral oil, enhancing its thermal and dielectric performance. The obtained results underscored the importance of tailored material selection approaches, adjusted to specific application requirements. The importance of comprehending and adapting to diverse contexts for effective material design and implementation is also highlighted.

Abstract
Pickup and delivery problems are frequently encountered problems in transport companies. This paper presents a variant of the homogeneous vehicle, single-to-single Pickup and Delivery Problem with Time Windows, where several vehicles must fulfill transport requests from pickup nodes to delivery nodes, called missions, with associated service level agreements (SLA). A mathematical programming model is proposed to tackle this variant, focused on optimizing the allocation and sequencing of missions to be executed by autonomous vehicles. Numerical experiments are performed comparing instances with missions with long and short SLAs. The results show that the model takes longer to find the optimal solution when the missions have short SLAs and increased difficulty in meeting them if the number of vehicles is limited.

Abstract
Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.