Abstract

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
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
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.

Abstract
Nowadays, the industrial market is characterised by high levels of competition, with customers increasingly demanding in terms of quality, delivery times, costs, etc.. However, with increasing demand and the need to increase productivity, many companies in recent years have dedicated themselves to decentralising their factories, thus moving to distributed production. Today's manufacturing systems are distributed in the sense that there are several jobs that have to be carry out on machines located in different factories. This paper proposes a multi-objective distributed job shop scheduling model with unrelated parallel machines and sequence-dependent setup times. The transport time of raw materials to carry out a given job to a factory is also taken into account. Small instances of the problem were solved using NSGA-III with the aim of simultaneously minimising two objectives: the makespan and average completion time. Preliminary results show the validity of this approach.

Abstract
Job shop scheduling problems are common in the engineering field. In spite of some approaches consider just the most important objective to optimize, several other conflicting criteria are also important. Multi-objective optimization algorithms can be used to solve these problems optimizing, simultaneously, two or more objectives. However, when the number of objectives increases, the problems become more challenging. This paper presents the results of the optimization of a set of job shop scheduling with unrelated parallel machines and sequence-dependent setup times, using the NSGA-III. Several instances with different sizes in terms of number of jobs and machines are considered. The goal is to assign jobs to machines in order to simultaneously minimize the maximum job completion time (makespan), the average job completion time and the standard deviation of the job completion time. These results are analysed and confirm the validity and highlight the advantages of this approach.