Abstract
Self-localization of a robot is one of the most important requirements in mobile robotics. There are several approaches to providing localization data. The Ultra Wide Band Time of Flight provides position information but lacks the angle. Odometry data can be combined by using a data fusion algorithm. This paper addresses the application of data fusion algorithms based on odometry and Ultra Wide Band Time of Flight positioning using a Kalman filter that allows performing the data fusion task which outputs the position and orientation of the robot. The proposed solution, validated in a real developed platform can be applied in service and industrial robots.

Abstract
This paper presents the development of an acquisition system and data logger from an existing set of three continuous stirred-tank reactors in series. The reactors are currently used in chemical engineering educational laboratories to perform kinetic and tracer experiments. In this sense, to accomplish the store data process, the volumetric flow rate and the concentration of tracer, reactants and/or products of the reaction must be acquired as a function of time. In the original experimental setup, only the signal conditioning system was operational, while the acquisition, visualization, and control systems were obsolete and damaged. Thus, a new system composed of an interface and real-time acquisition data is proposed alongside preserving the main reactor structure. A graphical user interface and the automation of the various actuators were developed based on worldwide usage and low cost, respectively. This system, based on a common 8-bit microcontroller and an application developed in Lazarus, allows the storage of the acquired data in a time-series database. In this way, students can analyze the results later or in real time. Moreover, remote access allows controlling the reactor and getting data by the Internet of Things (IoT) resources. Additionally, the proposed system using IoT allows data to be shared with the community as a dataset. © 2023 IEEE.

Abstract
There is an increasing need for autonomous mobile robots (AMRs) in industrial environments. The capability of autonomous movement and transportation of items in industrial environments provides a significant increase in productivity and efficiency. This need, coupled with the possibility of controlling groups of heterogeneous robots, simultaneously addresses a wide range of tasks with different characteristics in the same environment, further increasing productivity and efficiency. This paper will present an implementation of a system capable of coordinating a fleet of heterogeneous robots with robustness. The implemented system must be able to plan a safe and efficient path for these different robots. To achieve this task, the TEA* (Time Enhanced A*) graph search algorithm will be used to coordinate the paths of the robots, along with a graph decomposition module that will be used to improve the efficiency and safety of this system. The project was implemented using the ROS framework and the Stage simulator. Results validate the proposed approach since the system was able to coordinate a fleet of robots in various different tests efficiently and safely, given the heterogeneity of the robots.

Abstract
This work presents a methodology for characterizing ultrasonic and LASER sensors aimed at detecting obstacles within the context of electrical inspections by multirotor Unmanned Aerial Vehicles (UAVs). A set of four ultrasonic and LASER sensor models is evaluated against eight target components, typically found in high-voltage towers. The results show that ultrasonic sensor arrays displaced 25. apart reduce the chances of problems related to crosstalk and angular uncertainty. Within the LASER sensor suite, solar exposure directly affects the detection behavior among lower power sensors. Based on the results obtained, a set of sensors capable of detecting multiple obstacles belonging to a high-voltage tower was identified. In this reasoning, it becomes possible to model sensor architectures for multirotor UAVs to detect multiple obstacles and advance in the state of the art in obstacle avoidance systems by UAVs in inspections of high-voltage towers.

Abstract
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms' ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology's performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

Abstract
The exponential growth in energy demand is leading to massive energy consumption from fossil resources causing a negative effects for the environment. It is essential to promote sustainable solutions based on renewable energies infrastructures such as microgrids integrated to the existing network or as stand alone solution. Moreover, the major focus of today is being able to integrate a higher percentages of renewable electricity into the energy mix. The variability of wind and solar energy requires knowing the relevant long-term patterns for developing better procedures and capabilities to facilitate integration to the network. Precise prediction is essential for an adequate use of these renewable sources. This article proposes machine learning approaches compared to an hybrid method, based on the combination of machine learning with optimisation approaches. The results show the improvement in the accuracy of the machine learning models results once the optimisation approach is used. © 2023 - IOS Press. All rights reserved.