Abstract
The advancement of technology has led to a growing demand for autonomy across various sectors. A key aspect of achieving autonomous navigation through intricate environments is path planning, initially confined to 2D spaces but rapidly evolving to address the complexities of 3D environments. Despite the widespread adoption of RRT-based planners, their inherent lack of optimality has encouraged researchers to find refinements. This paper transposes an existing algorithm developed for 2D environments to 3D, leveraging a heuristic to optimize the generated paths in terms of path length, memory consumed, and execution time. Along with this scalability to 3D scenarios, a modification was introduced that trades off some execution time for a substantial improvement in path length. The results obtained from a series of simulated experimental tests prove the efficacy of the proposed method in 3D environments, demonstrating reduced memory consumption and execution time compared to conventional approaches.

Abstract
Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. The wide range of AMR's applications and the characteristics of multiple industrial environments (indoor and outdoor) have led to the development of a flexible and robust robot software architecture that allows the fusion of different data sensors in real time. In this way, and in terms of localization, AMRs have greater precision when it comes to uncontrolled and unstructured environments. These complex environments feature a variety of dynamic and unpredictable elements, such as variable layouts, limited visibility, unstructured spaces, and uncertain terrain. This paper presents a multi-localization system for industrial mobile robots in complex and dynamic industrial scenarios, based on different localization technologies and methods that can interact together and simultaneously.

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
Autonomous Mobile Robots (AMR) are seeing an increased introduction in distinct areas of daily life. Recently, their use has expanded to intralogistics, where forklift type AMR are applied in many situations handling pallets and loading/unloading them into trucks. One of the these vehicles requirements, is that they are able to correctly identify the location and status of pallets, so that the forklifts AMR can insert the forks in the right place. Recently, some commercial sensors have appeared in the market for this purpose. Given these considerations, this paper presents a comparison of the performance of two different approaches for pallet detection: using a commercial off-the-shelf (COTS) sensor and a custom developed application based on Artificial Intelligence algorithms applied to an RGB-D camera, where both the RGB and depth data are used to estimate the position of the pallet pockets.

Abstract
This article presents the prototyping of an educational manipulator robot, based on the EEZYbotARM Mk2 robot, tailored for first-year master's students in the field of robotics. The project encompasses the assembly of the robot arm, computation of both forward and inverse kinematics, and analysis of two path-planning movement algorithms. These features are consolidated into an Arduino library to streamline the process for students to generate instructions for the robot. The EEZYbotARM Mk2 features a three-degree-of-freedom revolute arm with a gripper that remains parallel to its base at all times, enhancing its suitability for educational applications such as pick-and-place tasks. The article provides detailed descriptions of the materials and methods employed, along with proposed challenges for student engagement.

Abstract
In this paper it is presented a Hardware-in-theloop (HIL) mobile robot programming approach, to be applied in a robotics educational context. The motivation to apply this approach is the fact that students can program the robots without access to the robot hardware, but still maintain some important closed loop control critical features, such as a realistic lag time and the possibility for a larger number of students to program at the same time. Therefore, the developed software is applied to the real hardware without any change. The HIL approach was applied to provide a simulation close to reality, once the processing occurs in the real robot processor and the actuation and sensorization inside the simulation, adding to the advantage to test the firmware avoiding damage in the physical robot.