Proceedings of the Brazilian Symposium on Robotics and Latin American Robotics Symposium (SBR/LARS)
It's Your Fault! The Influence of Roboldo, a Social Robot, on Human Guilt and Shame in an Interactive Game
This research investigates the ethical consequences of human interaction with Roboldo, a cost-effective emotional robot assembled using low-cost hardware. Grounded in theories such as Computers as Social Actors and Robots as Persuasive Agents, the study examines the influence of blame attribution for mistakes on participants' sense of guilt and shame, specifically within a memory card game. Participants filled out questionnaires pre- and post-interaction with Roboldo, evaluating their initial perceptions of the robot and their levels of guilt and shame under two distinct conditions: one where Roboldo faults the participant for its errors, and another where it faults its developer. The findings reveal no significant differences in levels of guilt and shame between conditions. Additionally, no substantial changes were observed in overall perceptions of the robot's friendliness or effectiveness. These results underscore the complexity of emotional responses in human-robot interactions and emphasize the importance of ethical considerations in robot design.
Integration of BERT Models in NAO Robot for Library Assistance
Uuman-Robot Interaction has evolved significantly with advancements in artificial intelligence (AI) and natural language processing (NLP). This paper investigates the BERT Model and pre-trained sub-models to develop a tool that enable more natural dialogues among social robots and humans. To this end, the NAO robot from SoftBank Robotics was designed to function as a library assistant for the student community. The proposed system integrates the Google Speech Recognition API for voice recognition, transcribing the user's speech into text. Moreover using BERT model and pre-trained sub-models for natural language understanding, the system interprets the user's intent and responds appropriately. Using its Speech Synthesis system, the NAO robot interacts with users through audio, simulating a real conversation. Several experiments were conducted for demonstrating that this setup facilitates user communication, serving as an efficient and accessible intermediary for providing library information.
What is the Comfort Distance for a Human and a Collaborative Industrial Robot Interaction?
Humans interact with each other considering a complex mechanism of analysis defined by the fusion of cultural factors with a set of non-verbal factors such as kinesics, haptics and visual cues. This behavior is termed proxemics, with interpersonal distance being its most expressive signal. When interacting with robotic agents, proxemic behavior must also be considered while respecting the human need to maintain a comfortable distance. This study explores the hypothesis that the comfort distance decreases as there is a common interest activity underway. To validate this hypothesis, a human-robot interaction experiment was conducted where the common interest is achieved through a card game. In addition to the quantitative data, such as the robot's distance, the experiment also generates qualitative data through subjects' responses to a questionnaire about their impressions of the interaction. The analysis of the experiment results is of particular interest in industrial plants where the use of collaborative mobile robots (cobots) represents a growing demand in the sector.
RoboSign: Improving Interaction Between Service-Robot and Hearing Impairments People
Service robots assist humans in routine activities such as domestic tasks. Their interaction with humans, e.g., during communication, is commonly done through voice recognition functionalities. However, voice interaction limits robot usage, making it inaccessible to hearing-impaired people. This paper presents RoboSign, an interaction solution between a service robot and people with hearing impairments through command recognition in Brazilian Sign language (Libras). RoboSign includes a simplified dataset from scratch for a computer with low computational power, training a recurrent neural network model using MediaPipe Landmark Detector and Long ShortTerm Memory (LSTM) to perform signal recognition. The model was validated during training with unknown data, reaching 99% average accuracy, and evaluated in real-time using a camera, reaching 85% average accuracy. These results demonstrate that it is possible to perform human-robot interaction through Libras recognition with considerable accuracy without high computational cost due to simplified data collection and the LSTM algorithm for network training.
LLM Text Generation for Service Robot Context
Service robots are designed to perform useful activities for humans or other machines, such as cleaning a house, providing guidance, or cooking. To perform these activities, robots must be increasingly autonomous and capable of executing physical and cognitive tasks. Such skills involve human-robot interaction, where humans give a specific command, and the robot performs the task. In this scenario, voice recognition and detection tools can ensure communication through natural language. However, obtaining comprehensive and contextualized answers to specific questions or topics becomes challenging for the robot, which relies on static and pre-existing information to generate rigid and repetitive responses. This limitation hinders the creation of a more natural and intuitive dialogue between humans and machines. This paper proposes the LLP4ServiceRobot solution, which uses fine-tuned large language models to generate more dynamic texts and responses. The development methodology involves selecting large language models, fine-tuning them, and training them for text generation and question-answering tasks. We validated our model using two performance metrics: the Fl-Score, for the question-answering model; and ROUGE, for the text generation model. Results showed that the question-answering model achieved an F'l-Score of 52%, while the text generation model achieved a ROUGE-l score of 67%. Additionally, an experiment was conducted to evaluate the Question Answering (QA) model. Results showed that the model could correctly answer 60 % of the questions in a test dataset. The proposed model will be used in the service robot BILL, part of the RoboCup@Home league research at ACSO - the Center for Research in Computer Architecture, Intelligent Systems, and Robotics at the State University of Bahia - UNEB.
Using Speech and Text in Emotions Recognition
This study explores the intricacies of human communication with a focus on emotion recognition in speech, emphasizing the significance of voice recognition. To Address Brazilian Portuguese's acoustic and linguistic features, this study adopts an innovative approach by integrating a convolutional neural network and the bidirectional encoder representations from the transformers model. The methodology involves a systematic literature review and the development of a real-time system, resulting in an accuracy rate of 87.5%. Voice is portrayed as a vital communication channel, conveying information and emotional nuances. Cultural and linguistic influences accentuate the complexity of emotion detection in speech, posing specific challenges in the Brazilian context. The need for more data for Brazilian Portuguese presents a barrier to developing culturally sensitive systems. The social relevance of the project is underscored by its potential to enhance human-computer interaction, promote emotional well-being, and contribute to the contest Brazilian natural language processing community.
Dynamic Safety Zones for Industrial Robots: A Fuzzy Logic and Computer Vision Approach
This research focuses on integrating a robotic manipulator in shared workspaces within the framework of Indus-try 4.0 for Human-Robot Collaboration (HRC). The primary objective is to implement a safe strategy by employing a fuzzy-speed controller to enhance the mobile robot's movements near machinery. This strategy utilizes two cameras: one mounted on the ceiling of the industrial environment and another on the robot itself, called an eye-in-hand camera. The You Only Look Once Version 3 (YOLOv3) Convolutional Neural Network (CNN) detects the obstacles in the environment and identifies the target objects. Simulations were conducted using Gazebo software along with a Robot Operating System (ROS) to determine the effectiveness of the proposed approach in ensuring safe robot movements while accurately reaching target positions by dynamically adjusting velocity within the defined danger zone.
Recognizing Human Actions: A Deep Learning Model for UAV Piloting
This project aims to explore action recognition through a deep learning model generated by Convolutional Neural Networks, establishing the foundation for human-robot interaction in a scenario where Unmanned Aerial Vehicles (UAV) are controlled exclusively by visual commands. The model analyzes images captured by an onboard camera using and classifies them into nine categories. Each category issues a specific command based on human actions performed by individuals properly equipped with personal protective equipment. The results demonstrate the feasibility of the proposed approach, opening room for improvements aiming its use in more complex scenarios.
Analysis of the Effect of Marine Currents on an Aerial-Underwater Vehicle
Aerial-underwater vehicles present control challenges in both aerial and underwater environments, so obtaining a model for simulating the current effects experienced by these vehicles in both environments provides an important theoretical basis for analyzing dynamic behavior in closed-loop control. This article proposes the analysis of an aerial-underwater hybrid vehicle while adding the effects of ocean currents as perturbations. In this regard, it is intended to explore two numerical simulation scenarios: in the first scenario, the vehicle performs a mission in an underwater environment without the presence of ocean currents, and in the second scenario, with the inclusion of the effect of ocean currents under the same mission conditions, to illustrate the difference in the vehicle's behavior.
Deep Reinforcement Learning Applied for Threat Engagement by Loyal Wingmen Drones
This study explores using Loyal Wingmen drones in counter- UAV systems to detect and neutralize multiple aerial threats cooperatively. We employ Curriculum Learning and Bayesian Optimization to enhance the Proximal Policy Optimization algorithm, which trains a Deep Neural Network to perform high-level decision-making. The effectiveness of this network is assessed in simulations that pit it against an increasing number of Loitering Munitions, with a comparative analysis of a Behavior Tree agent. A key finding is the effective deployment of simulated LiDAR technology for dynamic multi-target detection, which surpasses traditional systems limited to observing a fixed number of entities.
Trajectory of Humanoid Robots with the Unscented Kalman Filter
In this paper, the integration of the Unscented Kalman Filter (UKF) with gait control for humanoid robot navigation was explored, specifically for the NAO robot. The UKF improves the accuracy of state estimation by processing nonlinear transformations, while gait control adjusts the robot's trajectory using real-time feedback from landmarks detected through computer vision. Experimental results demonstrate that this approach significantly reduces trajectory deviations, leading to improved navigation accuracy and system robustness. This method has potential applications in autonomous robotic systems.
Direct Drive System Applied to a Small Size League Robot
This paper compares a new transmission system and a new set of powertrains for robots in the Small Size League category of the RoboCup Soccer competition. The goal is to improve their speed and movement, leading to better performance and engine efficiency. Additionally, the study aims to optimize the robot's lower internal space to facilitate future improvements and integration of new technologies. The best types of powertrains in various arrangements, shapes, sizes, and the optimal motor/wheel system are studied, presenting a direct drive system that suits the league's evolution.
Attitude Control and Simulation of a low Orbit Nanosatellite
Nanosatellites have been objects of research and application in recent years due to the accessibility promoted by the standardization of components and structures. The various applications of this type of device require well-determined and controlled behavior. In this work, a nanosatellite simulation platform provided by Matlab/Simulink was extended and applied to validate closed-loop attitude control techniques using a set of reaction wheels as actuators for low-orbit operation. The attitude of a small satellite was controlled from an unfavorable initial condition, in a movement known as detumbling. The implemented PID controller showed good results in regulating the satellite's orientation angles in low orbit, correcting and maintaining its pointing towards the Earth, without saturating the set of reaction wheels.
Teach and Repeat for Path Planning Using Bézier Curves
In the service robots field, a mobile robot must be able to navigate and repeat previously defined paths. This paper aims to develop a technique capable of teaching and repeating (T&R) a path taken by a mobile robot. We evaluated using a differential robot. The developed system is integrated with the ROS framework and uses the mobile base, which is built with self-balancing scooter components. When changes need to be made in the environment, a user must generate a new path by teleoperating the robot, and the system can repeat new paths autonomously. The proposed approach consists of using Bezier curves to represent the paths taken by the user. The proposed method implements a T &R technique to navigate a mobile robot. Results show the worst maximum average of percentage error (MPE) using this method was only 1.361 % in adapting the Bezier curve to the points where the robot was taught, satisfactorily representing the path. In the repeating process, the worst MPE was 9.983%.
Autoencoder-based Super-Resolution Approach for Aerial Robot Navigation
Images are increasingly used in the navigation of autonomous aerial vehicles. However, processing images locally in aerial robots can be very computationally and energy-intensive. In this sense, it is increasingly common to use remote data processing for applications involving mobile robots. This paper addresses the problem of super-resolution as a strategy for remote processing of aerial images, considering the proposed algorithm as a technique that reconstructs high-resolution images from low-resolution images captured by the aerial vehicle. To this end, we propose an approach called AESR, based on Autoencoder architecture for generating high-definition images, integrating skip-connections and self-attention modules to improve the learning process. The results indicate the high performance of our approach, validating the learning process based on autoencoder. Moreover, the approach achieved high accuracy even in challenging images representing different environments. The experiments demonstrate the robustness and accuracy of our approach, overcoming state-of-the-art techniques, through the use of the quality metrics PSNR, SSIM, LPIPS, DISTS, NIQE, and BRISQUE.
Comparison of Wheel Odometry and Visual Odometry for Low-Cost Vehicle Navigation
This paper presents a comparative study of Wheel Odometry (WO), which relies on measurements from wheel en-coders, and Visual Odometry (VO), which uses images captured by a monocular camera to estimate the vehicle movement, for the purpose of low-cost vehicle navigation. The analysis reveal the strengths and limitations of each method, offering insights into their suitability for different applications. The ultimate goal of the paper is to guide the selection of odometry techniques in designing low-cost, efficient vehicle navigation systems. Results from simulated experiments suggest that, while VO can offer higher accuracy in complex environments, WO remains a viable and cost-effective option for simpler navigation tasks, especially when the associated wheel radius errors are correlated, i.e., similar for both wheels, and do not exceed ±0.26%.
Minimum Time Footstep Planning for Simulated Robot Soccer Kicks Using Model Predictive Control
This work contributes with a footstep planner that applies Model Predictive Control techniques, along with non-linear and mixed-integer numerical optimization, to generate footstep plans for the NAO humanoid robot to kick a soccer ball, within the context of the Robocup Soccer Simulation 3D league. The resulting planner can easily be adapted to other Robocup leagues and applications that require footstep planning. The planner accounts for several constraints related to the employed physical platform, as well as obstacles that may be present in the environment, and minimizes the amount of footsteps present. Two different approaches to find the plans with the least amount of footsteps were proposed, namely the Increasing Horizon Method (IHM) and Constraint Relaxation Method (CRM), and the resulting plans and execution times for different parameters were studied. The footstep plans generated using both methods display interesting emergent behaviors, such as rotating a foot to extend the next footstep's reach, or placing footsteps in a way that, at the end of the plan, the robot faces a completely different direction than at the start.
Deep Deterministic Policy Gradient and Computer Vision for Autonomous Wheelchair Navigation in Human-Shared Environments
We present an innovative approach to autonomous navigation for motorized wheelchairs in shared indoor spaces with human presence, combining Deep Reinforcement Learning and Computer Vision. The primary aim of this study is to enhance the well-being of individuals with disabilities who rely on such assistance for their mobility needs. Our results demonstrate that the Deep Deterministic Policy Gradient (DDPG) algorithm combined with computer vision techniques outperforms the Deep Q-Network (DQN) in both efficiency and stability. Throughout all analyzed stages, DDPG achieved higher average success rates: 98% (Stage 01), 89% (Stage 02), 86% (Stage 03), and 86% (Stage 04), showcasing generalization capabilities and consistently superior performance in diverse settings. In contrast, DQN struggled to avoid collisions, resulting in significantly lower average success rates: 3% (Stage 02), 14% (Stage 03), and 29% (Stage 04). These findings highlight the promising potential of our proposed solution and contribute to the advancement of research in this field.
Improved Defensive Strategy in RoboCup Small Size League using Deep Reinforcement Learning
The Small Size League (SSL) is one of the old-est leagues in RoboCup, an international scientific initiative whose objective is to advance the state-of-the-art in robotics. The matches are highly dynamic and competitive, an excellent environment to exercise and extend the latest decision-making techniques. In this work, we focus on the planning problem of the defense strategy in the SSL context. Our contribution lies in proposing a method for learning such behaviors using Deep Reinforcement Learning (DRL) algorithms. Through this technique, we intend to apply a more automated process than manually adjusting the heuristics used to handcraft such skills in code. For this purpose, the RoboCup Small Size League standard simulator grSim was adapted in order to implement a synchronous mode. Furthermore, we integrated this new simulator with ITAndroids team's code base and with the DRL algorithms provided by the Stable Baselines 3 library. With this setup, we successfully trained an agent using Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), the state-of-the-art for continuous control reinforcement learning algorithms. Moreover, we demonstrate that the resulting policies outperform the hand-coded counterparts, showing potential to improve the team's overall defense strategy.
Reinforcement Learning Applied to Very Small Size Soccer Decision-Making, Trajectory Planning and Control In Penalty Kicks
In this work, we propose a Reinforcement Learning (RL) training methodology using Proximal Policy Optimization (PPO) and Curriculum Learning (CL) to make simulated Very Small Size Soccer robots learn to convert penalty kicks against two types of opponents: the “line follow” goalkeeper, which defends the goal by following the goal line, and the offensive goalkeeper, which uses the univector field navigation technique to attack the ball. The RL agents trained using this methodology demonstrated sophisticated penalty kick skills with high conversion rates. The best-evaluated RL agent achieved a conversion rate of over 90% against the “line follow” goalkeeper and of over 71% against the offensive goalkeeper. Comparisons with the univector agent showed that the RL agents significantly outperformed it, with conversion rates 78.71% higher against the “line follow” goalkeeper and 1997.66% higher against the offensive goalkeeper.
A Platform for Manufacturing and Operation of Robot Systems
The industry, since its beginnings, has developed itself through technological leaps aiming to improve its efficiency and productivity. Industry 4.0 is the most recent paradigm that promises to revolutionize production through the integration of information technology in all of its processes. It integrates manufacturing, operations, and maintenance in a single ecosystem comprised of a diverse range of IoT devices, artificial intelligence, and a huge amount of data. This paper elaborates on the implementation of Industry 4.0 concepts into the manufacturing and operation processes of a robotics competition team. The concepts were implemented through a web platform that integrates all of the group's manufacturing and operation processes in a single database. This aggregation is achieved by identifying each of the team's items using Unique Universal Identifiers (UUID). This identification makes it possible to register exactly which item is being used in each robot, when the robot was manufactured, the last time it had to go through maintenance, who has participated in each of those processes, etc. In addition, the web platform, being populated with the Bill of Materials and documentation of each project, is able to help the manufacturing process by supplying the student with a structured step-by-step guide and documentation on how to build each of the projects designed by the team. The Industry 4.0 platform, in that way, can both integrate and register everything that happens in the workshop and improve the processes by guiding the student through each step of the manufacturing process. Finally, for documentation and experimental evaluation, the processes that use the platform were modeled using Business Process Model and Notation (BPMN).
Quality Analytics for Latin American and Brazilian Robotics Symposiums
We bring historical findings related to the most important Latin American and Brazilian Robotics Symposiums including their quality analytics regarding all papers published since their creation. For this quality analysis, we first provide a little of history and then we apply a methodology based on a systematic mapping of the literature using Scopus (from Elsevier) and Web of Science. The quality analysis includes variables as citations metrics, and also other historical variables as participation per countries, per states, per universities, and contributed areas among others. We use metadata and tools available in the most commonly used digital libraries, for a fair comparison. To the end, with basis on the results of our methodological approach, we point out that the community has followed the right path for establishing a high quality event, mainly regarding the decision of publication of the proceedings at IEEE Xplore, and recently in the Brazilian Computer Society Library (Sol-SBC), all together. Also, our analysis corroborate to verify the modi operandi that have been used in the events execution, including joining in the Workshop of Robotics in Education (WRE) and the competitions (RCB, OBR, MNR, CBR, LARC). Besides happening jointly, each event keeps their original event structure and characteristics.
Implementation of Firmware Based on Real-Time Operating System for Small Size League Robot
This article presents the implementation of a real-time operating system (RTOS) on ITAndroids Small Size League (SSL) category robots, a significant advancement for the team's participation in the Robocup SSL. The transition to using the STM32H753BI microcontroller and FreeRTOS on the second-generation Mainboard enhances temporal predictability and de-terminism, crucial for real-time systems. This development allows more efficient task management, including motor control, kicker charging, telemetry transmission, and fault handling, leading to improved robot performance and reliability during matches. The detailed system description and task implementation showcase how the Mainboard efficiently handles various functions such as motor control in a constant rate, responsive kicking mechanisms, and real-time telemetry collection and transmission. The work contributes by proposing a real-time operating system for a robot designed to participate in the RoboCup Small Size League (SSL). Despite the implementation being dependent on our team's design decisions, the concepts presented here can be adapted by other teams.
Design and Construction of a Low-Cost Combat Robot
This paper presents the design and construction of a low-cost combat robot named Artemis, developed for participation in the Robocore Experience 2024 competition. The project emphasizes the integration of cost-effective components and innovative methodologies to create a competitive robot. The ESP32 microcontroller was selected for its advantages in energy efficiency and connectivity, surpassing traditional platforms like Arduino. The construction process involved several phases, including planning, development, testing, correction, and performance evaluation, utilizing tools such as CAD software for design and 3D printing for fabrication. Key components included a DC motor, L298N H-bridge, and a LiPo battery, all chosen to optimize performance while adhering to budget constraints. The robot's design evolved through iterative testing and adjustments, ultimately leading to a successful competition debut. This work highlights the importance of resourcefulness and collaboration in robotics, showcasing how educational institutions can foster innovation in engineering projects.
Automating the Evaluation of the Scalability, Flexibility, and Robustness of Collective Behaviors for Robot Swarms
Robot swarms are large self-organizing groups of robots that coordinate to collectively perform missions that escape their individual capabilities. In a robot swarm, the individuals operate using local perception and communication, without relying on a leader or external infrastructure for coordination. These characteristics help robot swarms achieve desirable properties, such as scalability, flexibility, and robustness. In this paper, we use recently proposed experimental protocols to evaluate these properties in various typical collective behaviors for robot swarms. We focus on demonstrating that the joint application of defined protocols can help streamline and automate evaluation and reporting processes in swarm robotics research. In our experiments, instances of robot control software for typical collective behaviors are systematically simulated and evaluated across different environmental configurations and swarm sizes. We show how automated tools can facilitate the evaluation of swarm robotics properties.
Computer Vision for Space Robotics: CubeSat Detection and Tracking with OpenCV
Development of robotic manipulators for space operations is crucial to meet demands for small tasks, reducing astronaut risk via autonomous systems. Computer vision techniques provide the space robot with information about the target object for maintenance or repositioning, such as a CubeSat. To fulfill these requirements, this paper presents the development of a monocular vision system with the aim to provide visual detection, tracking and distance estimation of small satellites like CubeSats. The system, built using OpenCV, is designed to identify, track, and estimate the distance of a 1 U CubeSat within a controlled test environment simulating spatial conditions. By leveraging a combination of background subtraction, edge detection, and geometric analysis, available in OpenCV, the system effectively isolates the CubeSat from dynamic backgrounds, maintains robust tracking, and provides accurate distance estimations. Extensive testing across various scenarios demonstrated that the system performs particularly well in object tracking. Despite the challenges posed by varying lighting conditions and the nanosatellite's small size at greater distances, the system proved to be reliable, albeit limited. These findings underscore the potential of the system for applications in autonomous navigation and satellite servicing.
Tatiana De Figueiredo Pereira Alves Taveira Pazelli
An Expanded Latent Space Autoencoder for Land Cover Classification of Remote Sensing Images from EuroSAT
Image classification has been instrumental in the interpretation and labeling of images in the field of remote sensing, computer vision, and in robotics applications. Machine learning and artificial intelligence algorithms, particularly artificial neural networks, are extensively utilized for this purpose. In this work we propose the Expanded Latent Space Autoencoder (ELSA) with a case use application to classify land cover data. The main idea on the ELSA network structure is to utilize the latent spaces of multiple internal autoencoders in order to create an expanded latent space. This expanded latent space extracts more information from the input data, and serves as input features for a more simpler classifier network. In order to evaluate the proposed network's ability to extract features and classify complex and multispectral images we employed it to the EuroSAT dataset. The results demonstrate a remarkable capacity for feature extraction using the ELSA network, with lower complexity, trained with a reduced number of images. The classifier network achieved a final accuracy of 98.7%, matching or exceeding the performance of more complex state-of-the-art models.
Enhancing Casting Manufacturing Quality Control with Vision Transformers
Ensuring quality control is a crucial task in modern manufacturing processes. Today, industries must guarantee the quality of their products to stay competitive. Automated visual inspection is one of the most effective ways to promote quality control, enabling the efficient inspection of 100% of the production volume. This paper deals with the quality control problem of casting discs for submersible pump impellers, ensuring quality at the casting stage. We propose an approach to detect defects in casting discs throughout the manufacturing process. Our methodology uses Vision Transformers (ViT), which employ self-attention mechanisms to capture spatial relationships between image patches, allowing the model to learn hierarchical visual representations. Both real and simulated experiments were conducted to validate the proposed approach. The results indicate an accuracy of 99.22% using the proposed ViT inspection. Moreover, the approach maintained high accuracy even in noisy images, with accuracies of 95.45% and 98.28% for Gaussian and Salt and Pepper noise, respectively, in the worst-case scenario. The experiments demonstrate the robustness and reliability of the approach, optimizing the manufacturing process.
Mapping Solution for Programmable Intelligent Spaces Based on Computer Vision
This paper presents a mapping approach to be applied in intelligent spaces based on computer vision. By using cameras installed in the environment, the three-dimensional pose of mobile robots can be recovered through visual odometry, that provides a global and more accurate localization without cumulative errors. This enables the creation of high-quality maps that better represent the environment, ensuring invariance concerning the robot's reference frame and effectively addressing the kidnapped robot problem without compromising the map's integrity. The result is a robust mapping system capable of supporting more complex navigation tasks.
Integrating Visual and Encoder Data for Enhanced Navigation in Mecanum-Wheeled Robots via Extended Kalman Filter
This paper presents a methodology for fusing data from the IMU data acquired from the Intel RealSense 415i camera and the wheel encoder of a mecanum robot to estimate its velocity and acceleration. The proposed approach utilizes an Extended Kalman Filter (EKF) to integrate these information to enhance its navigation capabilities. the proposed approach also utilizes the capabilities of the companion computer onboard the robot to process the visual data for Simultaneous Localization and Mapping (SLAM). The paper discusses the implementation details, performance evaluation, and experimental validation using a real robot. From the results, the positional error using EKF was about 56 mm, exhibiting superior performance compared to the raw sensor data streams.
Multi-Drone 3D Reconstruction of Architectural and Structural Entities
In this paper, we analyze the results of 3D reconstructions of buildings and monuments using a multi-drone sys-tem with predefined parameters. The drones follow an elliptical flight path with the same average speed in opposite directions and different altitudes for data acquisition of RGB videos. We set the drone cameras at a specific angle to avoid occlusion areas. Then, for 3D reconstructions, we used Convolutional Neural Networks techniques, specifically NeRF and Gaussian Splatting, in two different case studies: a simulation environment and a field test. As a result, we obtained detailed and visually accurate 3D models.
The Contributions of Programming and Robotics Courses on Women's STEM Inclusion
This research investigates the impact of women's involvement in STEM education through the introductory programming and robotics course offered by the Computing Department at CEFET-MG. The course aims to foster logical reasoning, creativity, and computational thinking among students in the final years of public elementary schools. The study gathered data on the participation of women as students, monitors, and researchers from 2016 to 2024. These data were organized to create graphs supporting quantitative and qualitative analyses. The results show that including women researchers in the course, which began in 2022, has positively affected young women's participation. The lack of women in the department's faculty until 2022 highlights the importance of recent efforts to promote gender diversity within the course and the wider academic environment, promoting positive practices for advancing gender equality in fields where disparities persist.
Anderson Ribeiro De Oliveira Santos Silva
With the increasing developments observed in Edu-cational Robotics field, many robotics projects have been created helping students and professionals in this topic to acquire knowledge and develop their work. There are several educational robotics kits already marketed worldwide, however, with only a few of them suitable for classroom use, i.e., fulfilling a substantial degree of alignment with the country's educational documents such as the Brazil's National Common Curricular Base (BNCC). In this direction and still inside Educational Robotics, the use of music is one such topic that has been little developed and that is addressed in this work. Hence, we wish to express in this work the understanding of how Music is used inside educational robotics activities and to give further directions on its possible use. Towards this goal, this work proposes an educational robotics approach named EcoMusic, created based on the BNCC skills for early childhood, elementary, and high school education. The project consists on developing the basics for having a musical robot using electronic components and recycled materials. In addition to addressing musicalization, it involves concepts of programming, electrical engineering, and mechanics, which are pillars of educational robotics. Our experiments deomsntrate the viability of the developed tools.
On Strengthening the Glass Ceiling of Training for Robotics Competitions: Methods, Issues, and Directions
This study presents case studies of workshops de-signed to train teams for participation in robotics competitions, specifically the Brazilian Robotics Olympiad (OBR) and the First Lego League (FLL). The methodologies implemented are detailed, including team selection processes, the introduction of programming tools like Scratch, and the use of simulated competitions. The findings highlight the importance of rigorous planning and the involvement of experienced mentors, which significantly improved students' technical skills, teamwork, and problem-solving abilities. Additionally, key challenges such as resource limitations and motivational factors that impact the effectiveness of educational robotics initiatives are identified. Directions that can motivate the holding of these competitions in schools are pointed out, provided they are combined with methods and techniques that have a strong theoretical basis in educational practice, aiming at the formation of the student as a social being.
Musicalization of a Line-Following Robot for Educational Robotics Competitions
Educational Robotics competitions take place all over the world, with Robocup being the ultimate destination for most of these competitions. This is not different in Brazil. There are competitions for various educational levels, such as the Brazilian Robotics Competition (CBR) aimed at university students and the Brazilian Robotics Olympiad (OBR), intended for elementary and high school students. One of the categories in these competitions is the rescue category, where autonomous robots can be used to overcome challenges along a course, as well as rescue victims symbolized by objects. Various types of robots can be used in this category, utilizing wheels, chassis, claws, and sensors to assist in path detection, such as color, line, and proximity sensors. This category also has an extra challenge, where teams can present innovations in their own projects. In this sense, the project that performs best along the course and has the best innovation in the extra challenge earns points and can win the competition. One of the robots used in these competitions is from the project One Robot per Student (URA), of the Federal University of Rio Grande do Norte. The robot was designed both for use in competitions and for the training of teachers and teams. The use of music in Educational Robotics is a common approach and is known to be beneficial for education, but it is not as present in robotics competitions. Thus, as a matter of innovation, this work proposes to incorporate music into an autonomous robotics vehicle, in this case, the URA robot, making it a differentiating factor in educational robotics competitions. To achieve this, a buzzer actuator is attached to the URA vehicle's structure to produce sounds that signals the start and end of the vehicle's course in the rescue category challenge. These sounds are inserted into the programming through the frequencies of the melodies used in the URA vehicle's programming. The results of this work consider the effectiveness in competition environments within the rescue category in terms of functionality and performance.
Development of an Autonomous Robot for Competitive and Educational Purposes in the Sumo Category
This article explores the design, construction, and performance of a robot designed to compete in the Mini Autonomous Sumo category. In this challenge, autonomous robots weighing up to 500 grams battle in a circular arena, being programmed to detect and push the opponent out of the combat area without human intervention. The nature of this competition requires the integration of various fields of knowledge, from the selection of components to the development of codes. By highlighting the challenges faced, the solutions adopted and the lessons learned, the article seeks to provide innovation in the area of competitive, educational, and interdisciplinary robotics.
The underrepresentation of women in robotics re-flects broader gender disparities in STEM fields, notably in engineering courses. This complex issue stems from various factors and often requires targeted initiatives to address it. Therefore, this paper outlines the outcomes of an action research project named WIE FIGHT, which aims to foster women's interest in robotics within STEM education by forming an all-female team to develop a competitive combat robot. This publication presents an overview of the project's evolution, from participant training to developing the first robot prototype. The survey results reveal that participants had a positive influence, as evidenced by improved knowledge and enthusiasm for engaging with robots in both learning and practical applications.
Chameleon Robot: An Innovative Approach to Teaching Colors
This project aims to design and build a chameleon robot capable of exploring various environments and changing its color according to the RGB color model to blend into its surroundings. With this innovative approach, we intend to introduce educational robotics into classrooms, offering students a unique experience in learning about colors and their compositions. This is our second project, improved based on the experiences and lessons from the first one. The interdisciplinary project will integrate concepts from design, physics, and arts, with the goal of stimulating student's interest and providing a more comprehensive and practical understanding of colors. By exploring the possibilities of robotics, we aim to make learning more engaging and captivating, encouraging an innovative approach to teaching.
The Use of the Ultrasonic Sensor as a Potential Teaching Tool in the Teaching-Learning Process of Simple Harmonic Motion in High School
The following study highlights the increasing necessity of experimental classes with a significant focus on robotics, especially in the study of vibrations and simple harmonic motion (SHM). The students' perception that abstract subjects become more comprehensible with the use of empirical resources and robotic solutions is evident. However, acquiring measurement equipment is often unfeasible due to its high cost. To surpass this obstacle, the utilization of an Arduino prototyping platform and ultrasound sensors was proposed, integrating robotics into the teaching of SHM. The results indicated that the prototype developed with robotic components has the potential to generate greater dynamism and appeal in the classes, beyond its easy setup, presentation, and low cost. This study contributes to the promotion of more practical and accessible teaching approaches, highlighting robotics as an essential tool to overcome the barriers related to access to experimental equipment.
Connecting Theory to Practice: Mathematics, Physics, and Mobile Robotics in Our Daily Lives
This article presents an outreach project focused on developing educational videos to teach mathematical and physical fundamentals applied to robotics and intelligent automation. Initially, the videos demonstrate the practical application of these concepts in the Laboratory of Autonomous Intelligent Robots (LAB-AIR), with plans to expand the content to include tutorials and undergraduate topics. The videos will be made available on platforms such as YouTube, TikTok, Instagram, and LinkedIn to democratize knowledge and engage new students and researchers with the Federal University of Espírito Santo and LAB-AIR. The project stands out for aligning research in robotics and automation with the university's mission of promoting knowledge and fostering scientific dissemination and education.
A Low-Cost Multimedia Robotic Platform for Dance Performances: D4NC3R Robot
The integration of technology creates opportunities for the use of robotics in artistic performances and works. However, due to high costs, access to this type of technology is limited. The goal of this article is to report on the development of the D4NC3R Robot, a low-cost multimedia robotics platform designed by the 10 Dimensães project, with the goal of democratizing access to robotics in art.
The Pedagogical Use of the Online Digital Weather Station in Teaching Mathematics to First-Year Elementary School Students
This article describes the didactic-pedagogical process of the use of the online digital weather station as a didactic-pedagogical tool in teaching mathematics to students in the 1st years A and B of elementary school at Escola Municipal Cedro Alto in the 2023 school year. The objective of this article is to socialize the effort and dedication to carry out the activities necessary to develop skills by the National Common Curricular Base, and a search to introduce Computational Thinking and Digital Culture into children's lives. The pedagogical use of the weather station demonstrated positive results, with students actively engaging in learning and developing essential skills. This experience highlighted the potential for expanding similar initiatives to enhance innovative education in other knowledge areas.
Robotics as an Auxiliary Tool in the Construction of Playful Didactic Resources for Children's Literature at the Emília Floriani de Oliveira Early Childhood Education Center
This work aims to present innovative pedagogical proposals that seek to contemplate literature and robotics resources for the didactic part of early childhood education as a practice that aims to develop cognitive and socio-emotional skills. Therefore, it can be stated that the use of these resources in everyday school life helps in the development of children, favoring their understanding of the world. To offer opportunities that stimulate imagination, creativity, games, make-believe, and the exploration of different materials and technological resources, the Peter Pan and Neverland Project was developed using James Matthew Barrie's work: Peter Pan as a basis. The project was applied, through didactic resources created with the support of the team of educational robotics trainers of the Municipal Department of Education of Brusque, with young children from the Emília Floriani de Oliveira Early Childhood Education Center, of the municipal network of Brusque, Santa Catarina, by activity hour teachers, guaranteeing the learning rights as determined by the National Common Curricular Base.
Robotics Club Students Create Interactive Model of Ilse Teske Sculpture Park
This article describes the creation of an interactive model of the Ilse Teske Sculpture Park by students from 6th to 9th grade at Professor Georgina de Carvalho Ramos da Luz Elementary School. The project was born from an activity developed by the Educational Robotics Club, which aimed to integrate technology and education. Using Arduino robotics kits, the students applied concepts of computational thinking, digital culture, and programming to build an interactive replica of the park. The project also sought to promote the cultural heritage of Brusque, Santa Catarina, encourage creativity and innovation among students, and foster educational tourism in the city. There were some challenges, but the students persisted and completed the model. They successfully presented it at the 1st Education, Technology, Innovation, and Science Exhibition, where they won second place in the Tinkercad and Arduino categories.
Evaluation of the Impact of Educational Robotics on School Engagement: Educators' Perspectives
This study explores the impact of educational robotics on student engagement in a nonrormal learning environment, specifically within a non-governmental organization serving socioeconomically vulnerable children aged 7 to 13. Utilizing action research methodology, the study was conducted through a series of weekly workshops focused on unplugged computing and robotics activities. Data were collected using questionnaires completed by six teachers, who provided information on the academic and behavioral changes observed in 12 participating students. The findings reveal a positive impact on student engagement, problem solving skills, and intrinsic motivation, as well as improvements in classroom behavior and academic performance. Qualitative analysis highlights specific examples of improved student autonomy, responsibility, and collaboration as a result of the program. The study highlights the importance of integrating unppluged activies into nonformal educational practices as a means to improve engagement and provide access to learning opportunities for underserved communities.
Effectiveness Analysis of Claw Designs on the URA Platform for the First Stage of the Brazilian Robotics Olympics
The usage of robotics as a supporting tool to educational activities in Brazil has gained traction with the popularization of both consumer-grade 3D printers and the promotion of events such as the Brazilian Robotics Olympiad (OBR). To compete in such contest, students need to design and make their own robots, which should be able to solving rescue tasks that most often require picking up a ball on some irregular arena ground. This paper analyzes different claw designs, both active (a design that uses a motor) and passive (a design that does not use a motor) in order to evaluate the abilities and capabilities of each design, on the One Robot per Student (URA) platform. We then propose a passive design with comparable effectiveness to the active design in order to reduce the complexity of robot construction. Experiments show that the approach reduces failure points and facilitates the process of teaching a lay-student on building a robot capable of surpassing the challenges proposed by the rescue line challenge from OBR at its fundamental level (one).
The Brazilian Robotics Olympiad Extension Program
In an environment conducive to the dissemination of educational robotics and its results in the development of technical skills and soft skills of primary and secondary school students in Brazil, the organizing team of the Brazilian Robotics Olympiad, supported by RoboCup Brazil, developed an extension program to reach communities with low participation in competitions. Throughout Brazil, over the last 10 years, educational robotics courses have been developed and applied by university professors and state OBR representatives at schools, and for teachers and students for whom the possibility of working with technology, or even using it as a teaching and learning tool, was quite nonexistent. In recent years, centered on the creative learning approach, hands-on activities have been structured through instructional cards and challenges, which cover everything from electronic assembly to programming typical algorithms for solving RobocupJunior Rescue Line tasks. The program went through several phases, focusing on students, teacher training, the production of educational kits and, from now on, it seeks to develop course disseminators in schools and regions.
Utilizing Mobile Robots to Teach Dengue and Chikungunya Prevention in Brazilian Schools
This paper introduces a novel approach to combating the spread of Dengue and Chikungunya, two prevalent arboviruses in tropical regions, especially in Brazil. The research presents a low-cost differential topology robot designed as an educational tool to raise awareness about mosquito breeding grounds among students and teachers in public schools across the Federal District, Brazil. Developed at the University of Brasilia, the robot is deployed in collaboration with the Federal District Health Department to support prevention campaigns in schools. The paper evaluates the effectiveness of this innovative tool in enhancing community engagement and improving health outcomes. The findings suggest that integrating robotics into health education can significantly elevate awareness and strengthen prevention strategies against arboviral diseases.
The Integration of Education Eobotics in High School in the Light of the BNCC: Potentialities and Challenges – Experience Report
This work is an experience report on the integration of educational robotics into the school curriculum, highlighting the importance of adapting education to rapid technological advances. The inclusion of robotics in classes, using the Arduino platform, meets the competencies of the National Common Curriculum Base (BNCC), such as digital culture and computational thinking. Results obtained from the project implementation show that students improved their problem-solving skills and became more engaged in learning. Practical activities promoted an interactive learning environment, fostering creativity and collaboration. The study reports on the implementation of practical classes in a private school in City X, where students develop real projects, promoting playful and continuous learning. The activities are carried out weekly using a dynamic laboratory that stimulates interaction and creativity, preparing students for the digital future.
Educational Robotics as a Methodological Strategy for Improving School Performance in Mathematics
In order to achieve the objective of developing computational thinking and logical reasoning among students at Escola de Ensino Fundamental Dr. Carlos Moritz, located in the city of Brusque, Santa Catarina, pedagogical tools were used, ranging from unplugged computing to the use of Chromebooks, interactive screen, Arduino kits and block programming platforms such as Scratch, Code.org and Robogarden. These tools brought programming language and educational robotics to the classroom. Furthermore, during school hours, a robotics club was offered to develop mechanical, electrical, and programming skills. In these Robotics classes, the school's dollhouse was transformed into a smart, sustainable house with a lighting sensor, solar panel, rainwater capture, and smart irrigation as a pilot proj ect at the end of 2021, which served as a starting point for developing new methodological strategies, in 2022, to improve student performance in the Mathematics curricular component. Furthermore, the school advanced in the use of technology in the classroom by creating a task force with teachers so that everyone felt comfortable using technology in the classroom.
ASV AeroCat: Small Platform for Teaching and Research in Robotics
Autonomous surface vehicles (ASVs) play a crucial role in various applications such as bathymetric mapping, pollution monitoring, surveillance, and oceanographic studies. However, despite their undeniable importance, there is a scarcity of vessels specifically designed for robotics education. This market gap presents a significant opportunity, as integrating robotics concepts into ASVs could enhance the educational experience, addressing demand in the field of hands-on learning in marine robotics. This paper introduces the ASV AeroCat, a vessel developed with robotics education and research in mind. With reduced dimensions and weight, AeroCat features a differential-aerial propulsion system, making it ideal for operation in shallow water environments. Throughout the paper, details of the design and potential educational applications of the AeroCat will be discussed.
A Practical Approach to Teaching Differential Robot Control Using a Goal-to-Goal Interface
This paper presents an interface to assist students in performing robot controllers. This application is designed to stabilize the movement of a goal-to-goal robot. The primary objective is to enhance the learning experience by offering an easy application that showcases the robot's behavior based on its kinematic model. The paper outlines the various stages involved in developing and testing this simple interface, including a study of the kinematics of differential robots. It discusses the discrete-time equations for goal-to-goal behavior, the data required to obtain the controller, the application of the controller in a discrete microcontroller, and the development of code to simulate the robot's behavior. The results show the developed interface and the possible outcomes through the use of this application in control classes.
Improving Control Theory Education Through Face-to-Face Implementation of a Mobile Robotics Methodology
Advances in information and communication technologies have significantly transformed engineering education. Virtual laboratories are increasingly adopted to enhance student interaction with control system simulations. The improved visualization and interaction capabilities of modern computers offer a more organic way to teach theoretical foundations. This paper describes the implementation of a methodology for teaching control theory that until now was entirely online in a face-to-face environment at the University of the Federal District in Brasilia, Brazil. This methodology utilizes virtual laboratories with CoppeliaSim, MATLAB, and the EVA mobile robot to teach control theory focused on single-input, single-output (SISO) systems for mobile robot tracking and obstacle avoidance applications. The results of the face-to-face implementation are compared to those of the fully online methodology, revealing that in-person teaching significantly enhances the quality of instruction.
Roboldo: An Affective Robot for Empathetic Social Interactions in Education
Educational robotics has become a highly relevant tool in the current educational scenario, with a growing number of robotic agents being used to assist in teaching subjects such as mathematics, logic, and other areas of science. However, most of these agents do not have social or emotional characteristics, limiting their potential for application in different areas of knowl-edge, such as history, literacy, and biology, which could benefit from more interactive and emotionally engaging approaches. This article proposes a new approach to educational robotics by introducing a robot with emotional intelligence, designed to interact in a playful and empathetic way. The construction of the robot combines elements of low-cost electronics, artificial intelligence, and emotional design, with the aim of creating a teaching tool that goes beyond supporting exact subjects, promoting a richer and more diverse educational experience. This study covers aspects from the design of the robot to its implementation and initial evaluation, focusing on the expression and recognition of emotions, as well as the robot's ability to interact with individuals of different ages in different contexts.
Asimov: A Remote Laboratory for Experimentation in Educational Robotics
This paper discusses the development overview, testing, and evaluation of the Asimov - Remote Robotics Laboratory system. It is a remote experimentation environment that allows people, anywhere in the world, to conduct practical programming experiments on robots or microcontrolled devices remotely. The system was practically tested through the implementation of a basic programming and robotics course, delivered entirely online in an asynchronous format, with the practical component conducted through the developed system. Finally, a usability evaluation of the system was performed based on user perceptions of its usefulness, ease of use, graphical interface, and educational value.
FluxProg 2.0 - Extended Version of a Visual Tool for Teaching Programming Logic Using Flowcharts with Real and Simulated Robots
This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interface and robotics, expanding on the existing Fluxprog project. New features were implemented, such as variables, logical and mathematical operations, conditional structures, and loops, to motivate students to engage in challenges that promote logical thinking. This paper presents the design, implementation and evaluation of a programming teaching tool using a visual language based on flowcharts, with subsequent data collection through a questionnaire and analysis of preliminary results to evaluate the user's experience while interacting with the tool.
A Digital Platform for Educational Robotics in Non-Formal Settings
This study explores the development and implementation of a digital platform designed to deliver educational robotics to underprivileged and at-risk children within a non-formal educational context. The platform aims to democratize access to STEM education, fostering an inclusive environment where all students, regardless of their socioeconomic background, can develop critical skills for the 21st century. Aligned with the Brazilian National Common Core Curriculum (BNCC), the platform supports a variety of activities tailored to the competencies outlined in the BNCC, ensuring standardized and equitable educational outcomes. The initial testing phase, conducted by the project coordinators of an NGO's robotics program, involved an examination of the platform's functionalities and usability from the perspectives of educators, mediators, and students. The preliminary results indicate that, while the platform effectively supports educational objectives and provides valuable learning experiences, some adjustments are necessary to optimize its performance.
A Systematic Review of Educational Robotics in Early Childhood Education from the Workshop on Robotics in Education
This article aims to analyze the works published in the Workshop on Robotics in Education (WRE), with a focus on the application of Educational Robotics to the first level of basic education. For deepening our understanding of this topic, we carried out an in-depth analysis on 16 relevant studies that we found from this event, which is the most relevant scientific event in South America, covering a period from 2010 to the present day. This comprises the whole WRE existence. Our analysis revealed a rich diversity of teaching methodologies, focusing on different age groups and learning objectives.
Robot Finder v2: Search and Ingestion of Educational Robotics Data from Youtube
Educational Robotics offers an interactive learning approach where students can turn abstract theories into concrete projects. With the exponential growth in content creation on internet platforms, finding specific, high-quality learning objects about robotics in education can be challenging. Platforms like YouTube, for example, have a vast collection of educational videos that cover various topics related to educational robotics. However, they are scattered among a massive amount of available data, making it difficult to have more assertive searches and identify the best resources. This work aims to present Robot Finder v2, a software that uses an automated approach to feed RepositORE, a repository of Learning Objects in Educational Robotics. The Robot Finder v2 searches and stores data from YouTube videos to make the search for this content simpler and more effective. The collected data feeds the RepostiORE, where these objects can be adapted and researched by users looking to acquire skills in educational robotics. For the objects to be found accurately, the software uses the YouTube Data API v3, which has a set of metadata in addition to the descriptions of the videos provided by their creators. The processing done in the Robot Finder optimizes the search and registration of information by mapping API endpoints and seeking correlations of metadata between RepositORE objects and YouTube videos.
Ryllari Raianne Marques de Santana Costa
UERJ Robotics Team's Educational Project
The educational robotics project at UERJ was cre-ated in 2018 aiming at democratizing access to technological knowledge, connecting universities and public schools. It is intended to awaken the interest of children and adolescents in robotics by promoting stimulating challenges while strengthening the bond between school and students with a more interesting environment. The project's scope encompasses development of expository and practical content, including printed and digital teaching materials, as well as educational videos, participation in educational fairs and school visits to universities and other places, inspiring students to consider robotics as a possibility of a professional career. Despite the challenges, the project has made significant progress in promoting children's cognitive and creative development, contributing to a more knowledgeable society.
Assessment Strategies in Robotics Courses: An Analysis of Methodology and Learning Outcomes
This study investigates the implementation of robotics classes in five schools, targeting both elementary and high school students. The classes, designed according to Fink's Significant Learning Taxonomy and integrating the National Common Curricular Base (BNCC) into subjects like mathematics and sciences, included practical activities, teamwork, and reflections on technology's societal impact. An assessment method based on Fink's Taxonomy was previously developed to validate these courses. Two evaluations were conducted at different phases to measure the development of competencies such as problem-solving, teamwork, and technical skills. This paper presents a structured literature review, detailed analysis of the methodology, application, and results, aiming to understand the actual impact of the robotics course and identify areas for future improvement. Analysis of theoretical questions revealed lower success rates in programming-related questions, suggesting the need for a rotation of functions among students. Despite some challenges, the self-assessment responses showed increased student interest and reduced difficulties over time.
Educational Robotics Public Policies Implementation Issues
We aim to analyze the implementation process of a public policy provided by a municipal law enacted for the provision of educational robotics as a complementary activity in the education network of Natal, RN, Brazil. From the perspective of educational robotics trainees (undergraduate students), we highlight the construction potential and the challenges faced out by them using an action research paradigm done in six school units of the municipal network. The performance of the monitors has been systematized in the period from late 2021 to 2024, from which we aim to understand the law implementation. Activity reports were produced with the actions developed in each school in the mentioned period, to carry out a reflective data analysis in accordance with the legal provisions in the municipal regulatory framework. Therefore, the main contribution of the article is the analysis carried out on the experiences of implementing RE as a public policy, which can be used or replicated in other basic level education networks in Brazil. This includes a possible methodology that can serve as a support for other similar projects abroad. Above all, we noticed that planning must be rigorous, and strictly followed, otherwise it will demotivate teachers and school students. Also, the interference of external actors such as monitors and teachers from Higher Education Institutions is another factor that we analyze as having a strong influence in the continuity of such initiative.
Project Edubot: Exploring Pedagogical Approaches in High School Robotics Education
This study focus on passive and active learning within the context of robotics education on public high schools. By synthesizing research findings and practical insights, this paper offers valuable recommendations for educators aiming to optimize the delivery of robotics education in public high schools, addressing the significance of the utilization of appropriate resources and technologies to support effective instruction. We implemented two distinct teaching methodologies to compare and study students' reactions and learning outcomes: Group A experienced a passive methodology, while Group B engaged with an active methodology. We conducted a survey and the results show that the majority of the students prefer the active approach used in classes.
Educational Assistant Robot for Strengthening Children's Learning Using IoT
Digital Information and Communication Technolo-gies (DICTs) have increasingly become part of people's daily lives, whether they are children, adults, or the elderly. However, children's unfettered and increasingly early access to these technologies has been the subject of numerous studies aimed at measuring the impacts of their usage. In parallel, another issue arises: the influence of technology on education. When using technology, children often neglect their studies due to the distractions that technological devices (such as cell phones, computers, or tablets) can cause. In light of this, and aiming to reconcile technology with education, this work seeks to present the development of an intelligent educational assistant robot using the Internet of Things (IoT), with the goal of assisting children in the educational process through active learning approaches. Thus, upon completing the development of the robot, it was possible to verify its full functionality by engaging in interactions, asking questions, and providing details on distance and temperature values.