Abstract
This paper presents a collaborative mobile manipulator assembly station, which uses force control to surpass the positional uncertainties arising from unstructured work environments and positional errors of the mobile platform. For this purpose, the use case of an internal combustion engine for the automotive industry was considered. Several force control heuristics relying on blind searches using oscillations and/or environment exploration were developed and implemented. Particular attention was given to the orientation errors of the mobile platform, as it was proved that they have a significant impact on the assembly task. The proposed heuristics showed great potential for the use case at hand. Particularly, when the orientation error of the platform is limited to +/- 2 degrees, the oscillation method complemented by environment exploration was able to surpass a maximum translation error of 32.3mm, whereas the environment exploration complemented by orientation correction was able to surpass an error of 73.3mm. Moreover, a generalization strategy was proposed, intending to expand the scope of the developed heuristics to other assembly applications.

Abstract
A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV's route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*-Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.

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
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
The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.