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
Studies dealing with route optimization have received considerable attention in recent years due to the increased demand for transportation services. For decades, scholars have developed robust algorithms designed to solve various Vehicle Routing Problems (VRP). In most cases, the focus is to present an algorithm that can overcome the shortest distances reported in other studies. On the other hand, execution time is also an important parameter that may limit the feasibility of the utilization in real scenarios for some applications. For this reason, in this work, a Guided Local Search (GLS) metaheuristic available in open-source OR-Tools will be tested to solve the Augerat instances of Capacitated Vehicle Routing Problems (CVRP). The stop criterion used here is the execution time, going from 1 s (standard) to 10 s, with a last run of 360 s. The numerical results demonstrate that increasing the execution time returns significant improvement in distance optimization. However, the optimization found considering high execution times can be expensive in terms of time, and not feasible for situations demanding faster algorithms, such as in Dynamic Vehicle Routing Problems (DVRP). Nonetheless, the GLS has proven to be a versatile algorithm for use where distance optimization is the main priority (high execution times) and in cases where faster algorithms are required (low execution times).

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.