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.

Abstract
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Abstract
Order picking has driven an increase in the number of logistics researchers. Robotics can help reduce the operational cost of such a process, eliminating the need for a human operator to perform trivial and dangerous tasks such as moving around the warehouse. However, for a mobile robot to perform such tasks, certain problems, such as defining the best path, must be solved. Among the most prominent techniques applied in the calculation of the trajectories of these robotic agents are potential fields and the A* algorithm. However, these techniques have limitations. This study aims to demonstrate a new approach based on the behavior of oceanic relief to map an environment that simulates a logistics warehouse, considering distance, safety, and efficiency in trajectory planning. In this manner, we seek to solve some of the limitations of traditional algorithms. We propose a new mapping technique for mobile robots, followed by a new trajectory planning approach.

Abstract
Simulators have been increasingly used on development and tests on several areas. They allow to speed up the development without damage and no extra costs. On realistic simulators, where kinematics play an important role, the modelling process should be imported for each component to be accurately simulated. Some robots are not yet modelled, as for example the Monera. This paper presents a model of a small vibration robot (Monera) that is acquired in a developed test-bed. A localisation ground truth is used to acquire the position of the Monera with actuating it. Linear and angular speeds acquired from real experiments allow to validate the proposed methodology.

Abstract
This survey analyses and compares ten different robots capable of hybrid locomotion in an attempt to elucidate the readers on several aspects of importance when designing and implementing a legged-wheeled vehicle. With this purpose in mind, the robots are compared based on their goals, kinematic configurations, joint specifications and overall performance. In this text, their variety and versatility is presented, justifying their use in real-world scenarios.