Browsing by Author "Santos, Tatiana M.B."
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- Control tunning approach and digital filter application for competitive line follower robotPublication . Amorim, Johann S.J.C.C.; Fernandes, Jefferson H.O.; Canella, André L.C.; Santos, Tatiana M.B.; Lima, José; Pinto, Milena F.This research describes the development of a control strategy to optimize a competitive line follower robot for standard races. The innovative approach stems from the WolfBotz team at CEFET/RJ, presenting a thorough exploration of mathematical foundations, hardware design, control analysis, and how to implement this system in a microcontroller. This research complements a previous work that shows all the regulations used in Brazilian competitions and describes the controllers used in the system, such as angular and linear control. This research emphasizes all the changes between the two versions of Line Follower robots. The emphasis on mathematical foundations and integrating digital signal processing techniques like digital filters set the stage for robust sensor data interpretation. The tuning and optimization of dual controllers for track stability and linear velocity regulation represent a significant innovation, augmenting the robot’s overall performance.
- Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic EnvironmentsPublication . Castro, Gabriel G.R.; Santos, Tatiana M.B.; Andrade, Fabio A.A.; Lima, José; Haddad, Diego B.; Honório, Leonardo de M.; Pinto, Milena F.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.
- The impact of educational robots as learning tools in specific technical classes in undergraduate educationPublication . Santos, Tatiana M.B.; Amorim, Johann S.J.C.C.; Carneiro, Mirella M. de O.; Campos, Victor F.; Manhães, Aline; Lima, José; Pinto, Milena F.The use of mobile robots in the classroom has gained increasing attention in recent years due to their potential to enhance student engagement and facilitate personalized learning. This research presents the insertion of mobile robots as a hands-on learning experience in Control and Servomechanisms II and Signal Processing II classes. This work also addresses the challenges and limitations of using mobile robots in the classroom, including technical difficulties. The students were evaluated during the code implementation in the practical exercises. Besides, a form was provided to them in order to assess the impact of these robots as part of the pedagogical practice. From the students’ positive feedback, it was possible to conclude that the mobile robots were well-accepted. Besides, the robots enhanced Control Systems classes and improved students’ learning outcomes.