Abstract
Since creating universally capable robots is challenging for a single manufacturer, a diverse fleet of robots from various manufacturers is utilized. However, these heterogeneous fleets encounter communication and interoperability issues. As a result, there is a growing need for a standardized interface that is capable of communicating, controlling and managing a diverse fleet without these interoperability issues. This paper presents a translation software module capable of controlling an autonomous mobile robot and communicating with a ROS-based robot fleet manager using the VDA5050 Standard and exchanging information via the MQTT communication protocol, aiming at flexibility and control across different robot brands. The effectiveness of the software in controlling a mobile robot via the VDA5050 standard was demonstrated by the results. It accurately analysed data from the Robot Fleet Manager, converted it into VDA 5050 JSON messages and skilfully translated it back into ROS messages. The robot's behavior remained consistent before and after the VDA5050 implementation.

Abstract
The need for interoperability between robots of different brands and navigation typologies, graph-based and free navigation, is increasing and this has led to the development of a new approach to empower a graph and ROS-based robot fleet manager for the management of free navigation mobile robots. For this implementation and validation, in real tests, the OMRON LD-90 was the mobile robot platform chosen, which has the particularity of not allowing the execution of a waypoints sequence. A software module was developed to exchange data between a non-ROS-based mobile robot and a specific ROS-based robot fleet manager. This is an approach applicable to any free navigation Autonomous Mobile Robot (AMR) with the necessary adaptations regarding the information provided by the different robot brands.

Abstract
With the increasing adoption of mobile robots for transporting components across several locations in industries, congestion problems appear if the movement of these robots is not correctly planned. This paper introduces a fleet management system where a central agent coordinates, plans, and supervises the fleet, mitigating the risk of deadlocks and addressing issues related to delays, deviations between the planned paths and reality, and delays in communication. The system uses the TEA* graph-based path planning algorithm to plan the paths of each agent. In conjunction with the TEA* algorithm, the concepts of supervision and graph-based environment representation are introduced. The system is based on ROS framework and allows each robot to maintain its autonomy, particularly in control and localization, while aligning its path with the plan from the central agent. The effectiveness of the proposed fleet manager is demonstrated in a real scenario where robots operate on a shop floor, showing its successful implementation.

Abstract
The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Abstract
Angle assessment is crucial in rehabilitation and significantly influences physiotherapists' decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5 degrees and 17 degrees, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easy-to-implement solution.

Abstract
The energy storage batteries, employed in solar systems installed on lampposts, are usually placed in devices such as switchboards fixed at an elevation near the top of the column. However, this storage solution becomes inefficient, because it is not possible to guarantee the control of the working temperature of the batteries, due to the low thermal insulation capacity of these storage devices. In this sense, an underground compartment made of concrete, steel plate and rock wool were created, embedded in the foundation of the lamppost, with the purpose of using geothermal energy to maintain an adequate temperature inside the compartment. To verify the temperature inside the battery storage compartment, a thermal analysis was performed, where heat transfer by conduction, convection and radiation was considered. Analyses were performed in steady state, and later, transient state, considering the initial temperatures of the thermal study in the previous steady state. With a storage volume of 1m3 and the base of the compartment at a depth of 2m, it was verified that it is possible to use geothermal energy to cool or heat, depending on the season, a system through geothermal energy. Considering a typical day in July, with room temperature of 35oC, a reduction of approximately 8oC was obtained inside the storage compartment, compared to the ambient temperature.