Proceedings of the Brazilian Symposium on Robotics and Latin American Robotics Symposium (SBR/LARS)
Enhancing VSSS Pose Control with Hardware Acceleration and Bio-Inspired Algorithm Optimization
This paper presents a novel approach to enhancing pose control for Very Small Size Soccer (VSSS) robots through the integration of hardware acceleration and bio-inspired optimization algorithms. The proposed system leverages Field-Programmable Gate Array (FPGA) technology to implement a PID controller with varying levels of floating-point precision (11, 16, 23, and 27 bits), enabling efficient and high-performance control. Hardware-in-the-Loop (HIL) simulations, conducted using the CoppeliaSim platform, validate the system's effectiveness, demonstrating significant improvements in control accuracy as precision increases. Two bio-inspired algorithms—Differential Evolution (DE) and Moth Flame Optimization (MFO)—are employed to fine-tune the PID parameters. Both algorithms demonstrate strong convergence, with DE achieving a fitness function value of 0.4172 and MFO reaching 0.3736. The results highlight the potential of combining FPGA-based hardware acceleration with bio-inspired optimization to address the dynamic and demanding challenges of the VSSS competition robot.
Comparative Analysis of Controllers for Autonomous Vehicle Steering
Technological advances in recent years have allowed major developments in the field of autonomous vehicles. This technology has proven to be increasingly reliable and efficient in reducing accidents and optimizing transportation resources. However, it is a rapidly developing area, and comparative studies are still needed to clarify the advantages and disadvantages of each type of controller in different use cases. This article addresses the development of steering control for autonomous vehicles by comparing the classic PID control technique and the modern control techniques using a Robust Linear Quadratic Regulator. The objective is to assess the advantages and disad-vantages of each of these controllers when applied to automotive models in a numerical simulation environment. PID, nominal LQR, and robust LQR controllers were designed and applied to autonomously drive the car along a reference path. The simulated results show the highest efficiency in the nominal LQR controller when there are no car parameter variations, the PID controller shows performance degradation with mass variations, while the Robust LQR controller maintains its performance. Therefore, applications that are subject to high parametric uncertainties are better served by the robust LQR controller.
Coverage Path Planning Strategies for Low-Cost UAVs in Biological Control in Large Areas
This study develops and validates coverage path planning strategies for low-cost UAVs in biological pest control in large agricultural areas. Two approaches were implemented in a web application: (i) decomposition of complete routes into subroutes using a back-and-forth pattern, and (ii) segmentation of areas through Voronoi diagrams for balanced partitions. Validation was carried out through real UAV flights under different payload configurations, with contributions from UAV and biological control experts. The results showed that the Subroutes algorithm covered 114.61 ha in 44 minutes and 48 seconds, while the Subareas algorithm reduced distance by 2.6 km (2.8%) and flight time by 1 minute and 51 seconds (4.1%), ensuring a more uniform workload distribution. These findings confirm the practical applicability of the proposed strategies and their potential to improve efficiency in UAV-based biological control operations.
Simulation of Multivariable Monitoring and Operational Fault Prediction Using a Digital Twin for ROVs in Subsea Installation Operations
The increasing complexity of offshore operations and the growing demand for efficiency, safety, and sustainability have driven the development of intelligent digital solutions. This study proposes a digital twin model for remotely operated vehicles (ROVs) applied to subsea installation activities, integrating multiple interdependent operational variables such as motor temperature, electric current, depth, and accumulated operating time. Using a synthetic Big Data set, the system applies multivariable monitoring, anomaly detection, and machine learning algorithms to perform real-time fault prediction. The results demonstrate the model's effectiveness in anticipating failures, generating contextual recommendations, and reducing unplanned downtime, thereby improving the reliability of subsea systems. Although validated with synthetic data, the methodology is designed for straightforward adaptation to real-world sensor data and can be extended to other offshore assets such as subsea trees, launch towers, offshore cranes, and subsea production systems. This approach supports ESG (Environmental, Social, and Governance) objectives by promoting safer, more efficient, and sustainable offshore operations.
Bridging the Gaps: A Comparative Analysis of ISO 21434, ISO 26262 and Machine Learning in Autonomous Vehicles
The integration of Machine Learning (ML) in Au-tonomous Vehicles (AV s) raises challenges not fully addressed by current automotive standards, specifically ISO 26262 (functional safety) and ISO/SAE 21434 (cybersecurity). This paper reviews gaps in these standards concerning ML-enabled systems, focusing on explainability, data lifecycle, runtime assurance, and ethical compliance. To bridge these gaps, we propose the ML-Aware Compliance Bridge, a four-layer framework covering: (i) Data Lifecycle Management, (ii) Model Governance, (iii) Runtime Assurance, and (iv) Ethics and Explainability. The framework maps standard deficiencies to actionable recommendations, sup-porting the development of safer and more trustworthy AI -driven autonomous systems.
RD-GMM: A GMM-Based Framework for Robotic Dressing with a Single Fixed Robotic Arm Applied to T-Shirt Wearing
Robotic assistance for dressing remains one of the most challenging tasks in assistive robotics due to the complex dynamics of deformable objects and the need for precise coordination around human bodies. Many recent works combine vision-based keypoint detection with learning-based motion generation, but they often rely on strong assumptions or large training datasets. Motivated by the need for reproducible dressing frameworks, this work proposes a modular framework for autonomously dressing t-shirts using a robotic manipulator, Robotic Dressing Gaussian Mixture Modeling (RD-GMM). The system integrates RGB-D perception for body keypoint detection, motion generation based on Gaussian Mixture Models (GMM) and Gaussian Mixture Regression (GMR), which learn trajectories from a limited number of kinesthetic demonstrations, all managed by a state machine. The framework is experimentally validated on a physical mannequin using a Kinova Gen3 robot, demonstrating high system performance in dressing a t-shirt task with the inclusion of GMM. Despite the challenges of handling deformable objects and the assumption of a static human posture, the results show the feasibility of dressing a human with a single manipulator without the need for regrasping.
Evaluation of Workload and User Preference in a Smart Walker with Augmented Reality: A Study in Competitive and Individual Scenarios
The global aging population has increased the de-mand for rehabilitation solutions that address motor impairments and improve quality of life. However, traditional rehabilitation often struggles with low patient engagement due to the repetitive and monotonous nature of tasks. To address this, immersive technologies like mixed reality and competitive elements have been explored as strategies to enhance motivation. This study evaluated user workload, cybersickness, and scenario preference during tasks with a smart walker in an augmented reality environment, comparing competitive and individual conditions. Six participants performed object collection tasks under both scenarios. Performance metrics (task time, distance traveled, and average speed) and subjective workload (NASA-TLX) were assessed. Cybersickness symptoms were measured using the Vir-tual Reality Sickness Questionnaire (VRSQ), and an open-ended question captured participants' scenario preferences. Results showed that the competitive scenario increased mental demand by 133.5 % and perceived effort by 56.96 %. Despite the higher workload, all participants preferred the competitive scenario. The individual condition, in contrast, resulted in an 11.17% increase in physical demand and 43.4% more frustration. A learning effect was also observed. These findings suggest that, although competitive elements increase cognitive load, they can significantly enhance motivation and engagement.
An Assistive Haptic Interface for Robotic Teleoperation in Mixed Reality Environments
Traditional 2D interfaces used in robot teleoperation frequently lack intuitiveness and depth perception, which can limit the effectiveness of human-robot interaction (HRI) in dynamic and complex settings. Addressing these challenges, this work presents a HRI system based on mixed reality and assistive haptic feedback for remote navigation. The proposed approach integrates a digital twin developed in Unity, connected to a real mobile robot via the ROS 2 middleware. The haptic interface, implemented using the Falcon Novint device, provides tactile feedback to the operator based on the detection of physical obstacles and virtual ones (simulated within the Unity environment). The system employs a detection ellipse to compute directional forces that assist the operator's decision-making during navigation without compromising their autonomy. The pilot study with four volunteers followed an experimental protocol that included familiarization and testing sessions, during which both objective and subjective data were collected to evaluate system performance, workload (NASA-TLX), and presence perception (PQ). The results indicated no collisions among participants, regardless of their familiarity with haptic technologies and virtual environments. The study provides relevant insights for optimizing, highlighting the benefits of assistive and immersive systems in promoting safety and user engagement in teleoperation tasks.
Natural Language-Based Assisted Navigation in a Smart Walker
Mobility is a determining factor for maintaining quality of life of individuals, especially in a world where the overall population is continuously aging. In this regard, smart walkers (i.e., traditional walkers augmented with sensors and intelligent systems) emerge as a promising solution for gait monitoring and locomotion assistance. Human-robot interaction is a fundamental area in smart walker development and, with the rise of Large Language Models (LLMs), there is an opportunity to enable more natural and adaptive channels of communication for context-aware interaction. In this work, we propose an intelligent system for assisted navigation in smart walkers that integrates an LLM-based multi-agent with ROS 2 to dynamically set interaction modes. The system has three stages: command detection, operating mode definition, and secondary mode selection. To validate the proposed solution, two datasets were created: one with textual commands and another with voice commands transcribed using Whisper. The results demonstrate promising performance, with accuracy, precision, recall, and F1-score values above 86 percent in all stages for both scenarios. The observed results pave the way for practical implementations on the robot itself and for validation on realistic scenarios.
Robotic Eye System for Animatronic Faces in Human-Robot Interaction
Anthropomorphism plays a crucial role in Human-Robot Interaction (HRI), as nonverbal cues, such as eye gaze and facial expressions, significantly influence social perception. Among facial features, the eyes are particularly important, as they convey emotion and indicate the focus of attention. This work presents the development of a compact and functional animatronic eye system designed to improve an existing mechanism integrated into the robotic face of a domestic service robot. The current system, which mechanically links both eyes, limits expressiveness and restricts natural movement. To address these issues, two alternative mechanisms were explored: ABENICS and a system inspired by the HITSZ-Snakebot II robot. These were developed through CAD modeling and fabricated using both FDM and DLP 3D printing techniques. Three prototypes were created-two based on ABENICS and one on the snakebot design. The final prototype, based on the snakebot model, proved to be the most effective, offering a greater angular range, size reduction, and smoother, more natural movements. Its modular, compact structure makes it suitable for integration into expressive robotic faces, contributing to the development of more relatable and socially capable robots.
A Comparison Between the Particle Swarm and the Newton-Raphson Algorithms for Solving the Inverse Kinematics Problem in a 4-DOF Robotic Arm
Solving the inverse kinematics problem of a manipulator robot is one of the main concerns of industrial robotics, since it involves solving nonlinear equations that may have no, one or several solutions. This problem has typically been solved using traditional methods such as iterative numerical algorithms, which have proved to be computationally costly. This prompted researchers to investigate alternative approaches to this problem, and the most commonly used nowadays are artificial intelligence and combinatorial optimization algorithms. With this in mind, this paper presents a study that investigated the use of the particle swarm optimization algorithm to solve the inverse kinematics problem of a robotic manipulator and compared its results with those of the Newton-Raphson algorithm. This led to the conclusion that although the particle swarm optimization is not superior to the Newton-Raphson algorithm in terms of time and memory, it provides more accurate and reliable results.
A Robotics Club as a Strategy to Integrate Teaching, Research, and Community Engagement: Early Results
This paper presents the experience of the Robotics Club EPIIBOTS at the Federal Institute of Minas Gerais - Campus Ibirité, focusing on its integration of teaching, research, and community engagement in 2024 and 2025. Through practical activities with LEGO EV3 and Arduino, the club promotes technical training and student leadership. Its research efforts include participation in academic events and publications, while extension actions target digital inclusion through workshops for socially vulnerable youth. Using a qualitative case study approach, the work analyzes how such initiatives contribute to technical education, scientific engagement, and social impact in public vocational institutions.
Integrated Walk and Kick for a Humanoid Robot Using Model Predictive Control
In humanoid robot soccer, the control of the kick motion plays an important role in various situations from ball challenges and passes to shots on goal. Improving kick performance requires not only increasing its power, but also reducing its duration and enhancing accuracy, all while maintaining stability. For instance, methods that require the robot to completely stop before kicking are disadvantageous in ball challenge scenarios. Additionally, approaches that rely on predefined swing foot trajectories demand highly precise positioning relative to the ball, which increases execution time and may compromise kick accuracy. This paper contributes by proposing methods for generating both the swing foot trajectory and the robot's center of mass trajectory to enable accurate and stable kicking during continuous walking. The swing foot trajectory is generated online as a B-spline, based on time-varying inputs such as the ball position and the desired shot direction. Meanwhile, the torso trajectory is generated by a model predictive controller as a continuation of the walking motion. The method was validated in simulation and real-robot experiments, demonstrating successful kick execution without interrupting gait while preserving kick power.
Robotic Arm Teleoperation Through Recognition of Body Movements
The teleoperation of robotic arms traditionally relies on specialized devices, such as wearable sensors or depth cameras, which increase costs and limit applicability in resource-constrained environments. This paper proposes an alternative approach to robotic arm teleoperation using a single two-dimensional RGB camera to recognize hand gestures and estimate body poses. Using the MediaPipe framework, keypoints are extracted in real time, enabling both the recognition of manual gestures for trajectory planning and execution, and the mapping of arm extension to determine movement directions and amplitudes. To validate the approach, the Panda robotic arm was simulated in the Gazebo environment. The operator interacts with the system through an intuitive interface, defining target poses and-based on real-time gesture detection-sending commands to the robot. Results show that execution times are comparable to those achieved with joystick-based teleoperation, demonstrating the feasibility and efficiency of the proposed method. This study contributes to the advancement of robotics by presenting an accessible and effective solution for robotic system teleoperation.
Hybrid Algorithm Based on Bidirectional A* and Floodfill Applied to Coverage Path Planning for AGVs
This paper presents a hybrid Coverage Path Plan-ning (CPP) strategy that integrates the Floodfill algorithm with bidirectional A * (BA *) to optimize navigation for Autonomous Ground Vehicles (AGVs) tasked with inspecting photovoltaic farms. The proposed approach leverages the global exploration capabilities of Floodfill with the heuristic efficiency of bidirectional A *, enabling fast and complete area coverage while min-imizing redundant traversal and computational overhead. The framework is validated in a Software-In-the-Loop (SITL) simulation using Gazebo and ROS, and its performance is benchmarked against conventional Floodfill, Floodfill with A *, and Wavefront-based methods. Results demonstrate significant improvements in coverage efficiency, path smoothness, and execution time, highlighting the method's potential for real-world large-scale CPP applications. The proposed method achieved overlap and direction change rates below 1 %, significantly improving path efficiency.
Comparison of Delay Compensation Strategies in ESKF Low-Latency Navigation for More-Autonomous Aircraft
Accurate state estimation is critical for the safe and reliable operation of autonomous aerial vehicles. However, the inherent delay in sensor measurements, such as those from vision-based systems, poses a significant challenge. This study presents a comparative analysis of two delay compensation techniques, Filter Recalculation (FR) and Measurement Extrapolation (ME), for an Error-State Kalman Filter (ESKF) designed for inertial navigation, particularly in GNSS-denied environments. We developed a simulation environment in C++ to model a navigation system fusing IMU and camera data, subjecting it to different measurement delays. The results indicate that the FR method achieves superior accuracy and robustness, nearly eliminating delay-induced errors, but at a significant computational cost. The ME method offers a more computationally efficient alternative but exhibits numerical instability with larger delays. This analysis provides crucial insights for selecting an appropriate delay compensation strategy based on system-specific accuracy and latency requirements.
MUSE-SLAM: Multi-Sensor Underwater State Estimation SLAM
This paper presents MUSE-SLAM, a robust multi-sensor fusion framework designed for state estimation of Autonomous Underwater Vehicles (AUVs) operating in GPS-denied and visually degraded environments, such as underwater. The system is based on an Extended Kalman Filter (EKF) and four complementary sensors to overcome the drift commonly associated with traditional dead-reckoning methods. The sensors are: an Inertial Measurement Unit (IMU) for high-frequency motion tracking, a Doppler Velocity Log (DVL) for velocity relative to the seafloor, a pressure sensor for absolute depth measurement, and a mechanically scanned imaging sonar (MSIS) used to detect environmental landmarks. An important aspect of MUSE-SLAM is its asynchronous sensor integration strategy, which processes timestamped measurements in chronological order, allowing for varying sensor update rates and accommodating communication delays. The approach is validated using a real-world dataset. Experimental results highlight the clear benefits of the multi-sensor setup: when comparing different sensor combinations, the full configuration (using all four sensors) showed significantly improved performance.
Optimized Kalman Gain for Enhanced Localization in Differential Drive Robots
Autonomous mobile robots require accurate localization and efficient obstacle-avoidance strategies to navigate complex environments. This work presents a navigation system that integrates data from an Inertial Measurement Unit (IMU) and wheel encoders using sensor fusion techniques to enhance localization accuracy. The proposed approach is implemented on a Nexus Mecanum 4WD robot and tested within the Robot Operating System (ROS) framework. Its effectiveness is evaluated through a series of experiments comparing raw sensor readings, unit gain filtering, and optimized Kalman gain filtering. Results show that the Extended Kalman Filter (EKF) reduces position estimation errors by up to 47% compared to unfiltered sensor data, achieving an average localization error of 0.08 meters over a to-meter trajectory. These results confirm the robustness and effectiveness of the proposed approach for enhancing localization accuracy in autonomous ground robots.
Stationary Wavelet Transforms with Binary Trees for Acoustic Emission
This work proposes a method for feature extraction from acoustic emission signals using a binary tree structure based on the Stationary Wavelet Transform (SWT). The objective is to differentiate corrosion levels in carbon steel pipelines through multiscale decomposition. Each signal is transformed into 62 components using a SWT tree of depth 5, and basic statistical features (mean and standard deviation) are computed from each node. To evaluate the discriminative capacity of these representations, dimensionality reduction techniques (PCA, UMAP, and t-SNE) and quantitative clustering metrics were applied. Results show that nonlinear approaches, particularly t-SNE and UMAP, are more effective in revealing the separation between low and moderate corrosion signals, highlighting the potential of the proposed method for structural health monitoring applications.
A Comparison on Task Orchestration Systems for ROS Robotic Systems
Developments in service robotics brought a plethora of capabilities to robots, however, stringing them into useful tasks is an yet unsolved challenge being tackled by in several directions. One of the challenges is how to instruct a service robot to solve an specific problem by leveraging a set of basic capabilities already implemented by the robot, we call this “task orchestration”. There are libraries and tools that can be used to orchestrate such plans for robots to perform their tasks. In this paper we perform a comparison between two popular task orchestration systems, Behavior Trees and State Machines as a middle layer to supply behaviors for a robot using ROS (The Robotic Operating System). The comparison is performed through the lens of design and implementation complexity, as presented by 2 libraries, as well as how simple they are to mix and match parts of the system to create a new behavior. The implementation and mixing of parts are compared through the development of a sub task for the facial recognition task at Robocup@Home competition. The design complexity is analysed through a pick and place example.
Comparing Sensors for Position Estimation of a Small Size League Robot
Mobile robots are increasingly relevant across various applications, where accurate localization-determining the robot's position within its environment-remains a fundamental and challenging task for effective navigation and decision-making. This paper details a comparative analysis of sensor configurations for an embedded localization system in RoboCup Small Size League robots. Using an Extended Kalman Filter (EKF), we fuse data from wheel encoders, an Inertial Measurement Unit (IMU), the SSL-Vision system, and an omnidirectional robot kinematic model. Results show that combining onboard sensors (encoders and IMU) in the EKF's prediction phase with SSL-Vision for correction significantly improves accuracy and maintains a rapid 3.5 ms update rate. This sensor fusion proves effective in dynamic environments and mitigates temporary vision occlusions. Future work involves implementing this system into robot position control for enhanced gameplay.
State of the Trekking Autonomous Robots Competition
This paper presents a comprehensive analysis of the 2025 RoboCore Experience's Trekking competition, one of Latin America's largest autonomous robotics challenges. Modeled after RoboMagellan, the Trekking category tasks ground robots with autonomously navigating a known environment, reaching predefined landmarks in sequence in the shortest possible time. We provide a benchmark of hardware and software approaches employed by participating teams, covering mechanical design, perception, planning, and control algorithms. Our survey reveals key trends, the dominance of Ackermann steering and differential drive systems, the widespread use of inertial navigation, and the growing adoption of ROS 2. Additionally, we examine team composition, highlighting the competition's relevance across high school, undergraduate, and graduate levels. The findings suggest that while simpler approaches remain competitive, advanced techniques such as sensor fusion and machine learning are gaining influence. We conclude with recommendations for future editions, emphasizing knowledge sharing, dynamic challenges, and ongoing benchmarking to reduce entry barriers and accelerate innovation in autonomous robotics.
Autonomous Vehicle Localization with Deep Reinforcement Learning
Accurately estimating a vehicle's position is a key challenge in autonomous navigation, requiring real-time performance and robustness to sensor noise, outages, and unmodeled dynamics. Recent advances in deep reinforcement learning (DRL) have shown promise as a data-driven, model-free alternative for various robotics and autonomous driving tasks. This work proposes an end-to-end DRL-based pipeline for vehicle localization using only proprioceptive sensor data, including IMU, speed, and steering angle, combined with estimates from an Extended Kalman Filter. The proposed system is built around a containerized architecture that enables simplified deployment from training in the CARLA simulator to real-time execution on embedded hardware. The approach is validated both in simulation and on a 1: 10 scale physical vehicle, demonstrating stable training convergence and real-time inference capabilities on resource-constrained platforms. While the model's accuracy is not yet sufficient for closed-loop operation, the results confirm the viability of the architecture and the training approach as a foundation for further development.
Collision Dynamics of Autonomous Drones in Confined Fluid Environments Using the Lattice Boltzmann Method
This paper introduces a novel simulation framework that couples a D3Q19 BGK Lattice Boltzmann Method (LBM) fluid solver with rigid-body drone models. This framework captures two-way momentum exchange between drones and fluid and models inter-drone collisions as perfectly elastic. We simulate swarms of 30–60 drones under periodic boundary conditions on 3D grids of up to 1303 cells across fluid densities from ρ=1.0 to 4.55. We perform statistical simulations (Spearman's rs. Kendall's T, confidence intervals) to evaluate and confirm collision frequency trends. Results show that collision frequency increases nonlinearly with swarm size, whereas fluid density has minimal impact. This work provides a validated tool for fluid-swarm simulations and offers insight into collision risks for drone swarms in confined fluid environments. The findings can provide new directions for optimizing autonomous drone operations, particularly in industrial inspections and aerial swarms in urban settings.
Design and Development of an Affordable Anthropomorphic Hand Prosthesis
This work aims to develop an open-source anthropomorphic hand prosthesis. The methodology involves designing a manipulator that replicates the precision grips of the human hand. The development process integrated CAD modeling and 3D printing for prototyping a biomimetic structure that combines a reinterpretation of organic anatomy with practical functionalities in a synthetic design. The prosthesis features 22 degrees of freedom, actuated by N20 motors controlled through shift registers and motor drivers, with joint position feedback provided by resistive sensors. The embedded firmware running on an ESP32 microcontroller utilizes an on-off control strategy with a dead zone to achieve accurate joint positioning. Experimental tests demonstrate the system's ability to perform various grasp types and manipulate small objects, highlighting its potential applications in assistive robotics and dexterous manipulation.
Residual Reinforcement Learning to Generate a Closed-Loop Policy from a Keyframe Kick Motion
The RoboCup is an international robot football competition that aims to push the boundaries of robotics and artificial intelligence to a point where a team of robotic players could beat a human World Cup champion team. One of the major categories of RoboCup is the 3D Soccer Simulation League, which consists of a full game of football executed in a physically simulated environment. In this league, keyframe interpolation is a very common practice for executing specific tasks such as getting up, diving, or kicking the ball. A primary problem with this method is its open-loop kinematic description of movement, in the sense that it lacks feedback information from the world, which makes the behavior prone to underperformance or failure in complex and noisy environments. This work contributes by providing a methodology to enhance keyframe movements using residual learning, in which we train a policy using the Proximal Policy Optimization (PPO) algorithm to improve a keyframe kick motion. The approach not only improved the reliability of the kick but also enhanced its performance metrics, including distance and precision.
Accelerating Robotic Intelligence: An FPGA -Optimized MLP Generator
The rapid advancement of robotics has created a demand for intelligent systems capable of executing complex tasks with precision, adaptability, and efficiency. Multi-Layer Perceptrons (MLPs) neural networks have become a critical component in various applications, including object recognition, motion planning, and decision-making. However, deploying MLPs in real-time robotic systems presents significant challenges, such as stringent latency requirements, limited power budgets, and the need for high computational throughput. Field-Programmable Gate Arrays (FPGAs) provide a compelling solution by leveraging their reconfigurability, parallelism, and energy efficiency to accelerate MLP-based robotic intelligence. This paper introduces an FPGA-optimized MLP generator, a Python-based tool that automates the mapping of MLP models onto FPGA hardware. The generator incorporates advanced techniques, including custom precision floating-point arithmetic, to maximize performance. Experimental results demonstrate that 16-bit floating-point precision achieves an optimal balance between performance and resource utilization, delivering high accuracy with minimal power consumption. Furthermore, Hardware-in-the-Loop (HIL) validation on real-world scenarios confirms the tool's effectiveness, showcasing its potential for practical deployment in intelligent robotic systems.
Reinforcement Learning-Based Path Following for Robots: A Survey of Reward Functions
Reward functions define an agent's behavior in reinforcement learning by determining actions based on the feedback received. In the domain of path following, reward functions guide the agent toward successfully following the desired path. This paper presents a survey of reward functions focused on path-following tasks for mobile robots. The characteristics, strategies, and challenges associated with creating feedback mechanisms in various domains are highlighted. Components of the reward functions that can be controlled are explored and discussed. Furthermore, for each reward function, scenarios and mobile robots are indicated. Thus, this study provides insights into the reward components that influence reinforcement learning systems in robotic navigation.
Comparison of Tuning Techniques for PID Controllers in Mobile Robots Using the Empirical Method and the Firefly Metaheuristic Algorithm
Trajectory tracking remains a fundamental chal-lenge in mobile robotics, particularly for non-holonomic differential-drive robots such as those used in the IEEE Very Small Size Soccer (VSSS) competition. This work investigates and compares two control tuning approaches aimed at improving the performance of a VSSS robot in following a predefined path: the Firefly Metaheuristic Algorithm (FMA) and a traditional empirical method. The Bernoulli Lemniscate is adopted as the reference trajectory due to its variable curvature and suitability as a benchmark path in motion control tasks. Simulations in a virtual VSSS environment were conducted to evaluate tracking accuracy and system responsiveness under both strate-gies. Results show that FMA-based tuning leads to significant improvements in trajectory adherence and system stability. This study contributes to a deeper understanding of how systematic optimization methods can enhance control performance in small-scale autonomous robotic systems.
QTree-RRT: Improving RRT Through Quadtree-Guided Sampling for Path Planning
Path planning plays a crucial role for autonomous vehicles operating in complex environments, where efficient and reliable navigation is of paramount importance. Traditional path planning algorithms, such as the Rapidly-Exploring Random Tree (RRT) and its variants, are widely used but often suffer from inefficient exploration and suboptimal path generation, particularly in cluttered environments. This work introduces QTree-RRT, a hybrid approach designed to improve path planning by guiding the exploration process through a hierarchical spatial decomposition. The method first decomposes the environment using a Quadtree representation, then computes an initial coarse path through the free regions using a graph search algorithm (e.g., Dijkstra or A*). This preliminary path defines the relevant regions in which RRT sampling is then concentrated, leading to a more focused and efficient exploration. Experimental evaluations in simulated environments with varying obstacle densities show that QTree-RRT consistently outperforms classical RRT and its variants RRT* and RRT-Connect. The proposed approach achieves lower execution times (1.6s), reduces the number of required samples (295), and generates paths with better cost (475.39) and quality, demonstrating its effectiveness as a path planning solution for mobile robots.
Path Planning Based on Visibility Graph with Circular Obstacles for RoboCup Small Size League
The RoboCup Small Size League (SSL) demands fast and efficient path planning due to its dynamic and competitive environment. While sampling-based techniques have been widely adopted, they often suffer from inconsistencies between replans. In this work, we propose using a path planning approach based on a visibility graph with circular obstacles in SSL. To ensure real-time performance, we implement a lazy graph construction strategy and extensively optimize the code to reduce computational overhead. The shortest path is computed using the A* algorithm. Experimental results demonstrate that our method provides smooth, high-performance trajectories suitable for SSL gameplay.
Deriving Trajectory Tracking Requirements for a Small Size League Robot
In the development of an engineering project, it is common to face many design choices in order to achieve a final solution. The earlier such choices are made, the greater the influence in the design and the more impactful it is to change the decision later. Therefore, these decisions must be accompanied by a thorough analysis to assure that the solution is adequate to the project. In this paper, we contribute with a method to obtain design requirements using a Monte Carlo approach in the context of the design of a team for the RoboCup Small Size League competition. Focusing in the task of following trajectories, we analyze the impact of parameters that drive the robot design, the feedback control information, and the auxiliary computer software.
Determination of Minimum-Time Velocity Profile for Trajectory Planning of Small Size League Robots
This article proposes using the Minimum-Time Velocity Profile (MTVP) algorithm in the Small Size League (SSL) of RoboCup. This algorithm solves the trajectory planning problem, providing insight on how to navigate an already planned path while accounting for dynamic and kinematic constraints. In this work, the path is composed by line segments and circular arcs, which is generated by the team's visibility-graph-based path planner. However, this is not a limitation of the developed algorithm, which remains applicable to more general paths. Experimental results demonstrate the effectiveness of the MTVP approach: the variant that considers angular acceleration constraints achieved a 1.1% improvement in navigation time over a baseline P controller with saturations, while the variant that ignores this constraint achieved an 8.57% improvement. This work contributes with a solution to the velocity profile determination for trajectory planning in the SSL environment.
IEEE VSSS Dynamic Model Identification Using Standard Maneuvers
Identifying dynamic models of mobile robots, par-ticularly in the IEEE Very Small Size Soccer (VSSS) category, poses significant challenges due to the nonlinear nature and parametric complexity of their dynamics. This work proposes an approach to identify the dynamic model of a VSSS robot based on standard operational maneuvers, employing the interior-point method for parameter estimation. For validation purposes, five models were developed based on different scenarios constructed from these maneuvers. The results demonstrate that the proposed methodology is capable of producing models that satisfactorily represent the robot's dynamic behavior. In particular, models based on forward and coupled movements exhibited superior performance and more consistent generalization capability, pro-viding a solid foundation for future advances in control strategies and autonomy of mobile robotic systems.
BLDC Motor Control for Small Size League Robots
This paper is contextualized in the RoboCup Small Size League, presenting a BLDC motor modeling approach focused on control design for Small Size League robots and proposes two controller architectures: one standard PI controller and one PI controller with the addition of a feedforward gain in order to mitigate the back electromotive force effect, inherent in any electric motor. Furthermore, Monte Carlo simulations were conducted to validate the robustness of both controller architectures. Thus, the contribution of this work is to use formal control theory to develop a robust controller design methodology in order to design a BLDC motor control for Small Size League robots.
Mini Autonomous Car Driving Based on 3D Convolutional Neural Networks
Autonomous driving applications have become increasingly relevant in the automotive industry due to their potential to enhance safety, efficiency, and user experience. Nevertheless, building reliable systems remains challenging because of the complexity of neural models, long training times, and difficulties in generalization. Mini Autonomous Cars (MACs) provide a practical and cost-effective testbed, enabling rapid prototyping and evaluation of learning-based driving algorithms in scaled-down environments. In this work, we present a methodology for MAC autonomous driving based on RGB-D data and Three-Dimensional Convolutional Neural Networks (3D-CNNs), evaluated against Recurrent Neural Networks (RNNs) in two simulated tracks with distinct levels of complexity. Each model was trained and tested over 30 autonomous laps and assessed by lap time, variability, and track deviations. The results indicate that a reduced 3D-CNN architecture delivers the best performance on the visually complex Mini Monaco track, whereas a GRU-based RNN achieved the fastest times on the simpler Generated track. To further analyze robustness, we conducted an ablation study on the 3D-CNN. Interestingly, the version with fewer convolutional layers outperformed deeper variants, highlighting that reduced complexity can improve stability and generalization in challenging environments. These findings provide practical insights for selecting neural architectures in embedded implementations, balancing performance, robustness, and computational cost. Video link: https://youtu.be/6S2mfHWn3E8
Monocular Visual Odometry with YOLO-Based Dynamic Object Filtering
This work addresses the challenge of dynamic elements in monocular visual odometry, which traditionally relies on keypoint matching under the assumption of a fully static scene. In real-world environments, however, moving objects violate this assumption and can degrade pose estimation accuracy. To miti-gate this, we propose enhancing a classical feature-based visual odometry pipeline by using the YOLO (You Only Look Once) neural network to detect potentially dynamic objects and remove keypoints located within their bounding boxes. We compare this approach to a baseline method that uses only geometric keypoint matching, using sequences from the KITTI dataset for evaluation. Quantitative evaluation shows an improvement in the accuracy of the estimated trajectory when keypoints on dynamic objects are excluded from the odometry process.
A Comprehensive Simulation Platform for Image-Based Visual Servoing: Unity 3D and ROS 2 Integration with Performance Analysis
Testing visual servoing algorithms in real robotic systems can be costly, time-consuming, and often limited by hardware availability and safety constraints. To address these challenges, this work presents a simulation platform that inte-grates Unity 3D with ROS 2 to enable the development and validation of image-based visual servoing (IBVS) strategies in virtual environments. The platform simulates a 6-DOF robotic manipulator with a calibrated RGB-D camera and allows real-time communication through the ROS-TCP-Connector. Feature extraction, control computation, and joint actuation are per-formed using ROS 2 packages, while Unity enables the cre-ation of realistic scenes with configurable lighting and image noise. Three simulation scenarios were designed to assess the system's performance, covering both ideal and visually degraded conditions. Results show that the platform provides a reliable, reproducible, and accessible solution for research and education in visual control of robotic manipulators.
Semantic Segmentation Network for Warehouse Indoor Environments from 3D LiDAR Point Clouds
Robust perception is paramount for autonomous systems that operate in real public environments. LiDAR sensors are known for their robustness, making them ideal for autonomous robots, which enable obstacle detection, reliability in adverse lighting conditions, and overall safety assurance. Most 3D LiDAR semantic segmentation networks are fine-tuned for outdoor and urban environments, overlooking other equally important scenes, such as industrial scenarios. In this work, we contribute to an open-source Convolutional Neural Network (CNN) pipeline for semantic segmentation of 3D LiDAR point clouds of indoor warehouse environments. Additionally, we introduce a dataset of simulated industrial point clouds for 3D semantic segmentation called WareSet. We evaluate the quality and viability of our network using well-defined quality metrics, including accuracy, Intersection over Union (IoU) per class, and mean Intersection over Union (mIoU), for both training and validation sets. The experiments were conducted in an unfamiliar warehouse environment, taking into account the proposed dataset and the specified quality metrics. The proposed approach yields satisfactory results of 98.49% and 90.59% for accuracy, and 90.64% and 49.49% for mIoU in training and validation, respectively, with good model convergence. Additionally, it achieves 89.7% accuracy and 50.4% mIoU on the unknown warehouse, emphasizing its generalization capability. The proposed dataset is open-sourced at https://github.com/DeskFanzin/WareSet.
MBIE: Motion-Blurred Image Enhancement for Mobile Robots
Motion blur is a common issue in images captured by mobile robots, often caused by rapid movement or vibrations during navigation. This distortion compromises the robot's ability to accurately perceive and interact with the environment, directly affecting the performance of tasks such as obstacle detection and path planning. In this paper, we propose a deep learning-based method to enhance motion-blurred images, aiming to improve the visual input quality for autonomous navigation. The proposed approach employs a Convolutional Neural Network (CNN) trained to generate sharp images from blurry image inputs. To train and evaluate our model, we used the GoPro dataset, which includes 3214 pairs of blurry and corresponding sharp images. We evaluated the results using standard reference-based quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Our method achieved high scores on both quality metrics, outperforming state-of-the-art techniques, indicating improved visual quality and structural consistency in the enhanced images. These results demonstrate that the proposed approach can significantly enhance the perception capabilities of mobile robots operating in dynamic environments, ultimately contributing to safer and more efficient autonomous navigation.
SSMSS: A Model of Semantic Segmentation for Matching of Satellite and Sonar Images
Establishing accurate correspondence between sonar and satellite images is a nontrivial task due to differences in modality, resolution, and environmental noise, especially in underwater scenarios with GPS-denied environments. This work investigates the integration of semantic segmentation for matching satellite and sonar images. We evaluated a diverse set of state-of-the-art architectures, including convolutional models (U-Net, U-Net++, FPN, PSPNet, LinkNet, and DeepLab v3+) and attention-based models (MA-Net and SegFormer), with a focus on their ability to capture local structures, multiscale features, and contextual dependencies relevant for robust cross-modal matching. Experimental results indicate that, while convolutional networks deliver efficient and accurate segmentation of salient structures, attention-based models improve matching performance in complex scenarios by modeling long-range spatial dependencies. Among the architectures evaluated, MA-Net achieves superior performance, with a pixel accuracy of 0.9519, a mean IoU of 0.9494, and a matching score of 0.3618, underscoring the effectiveness of attention mechanisms. These findings lay the groundwork for future research on unified segmentation and matching frameworks specifically designed for autonomous underwater navigation.
Enabling Face Tracking in an Affordable Social Robot Using a Computer Vision Camera
Social robotics has been explored as a tool to support social interactions in various applications of human well-being. In therapies of children with Autism Spectrum Disorder (ASD) social robots have been included as motivational tools to perform activities within the therapeutic session. This work presents a contribution to the natural behaviours of the CASTOR robot, an open-source robot for assisted therapies of children with ASD. The project proposal involves using a camera with computer vision and implementing face recognition to improve the movements of the robot's neck and eyes. The tests were carried out in two phases; first, validating the functioning of the eye-contact tracking; and then, on the neck movement. The resulting improvements provided more natural and engaging gaze behaviour, where the responsiveness of the system obtained an accuracy of 96.11 % in the eyes and an RMSE of 3.62° of the tracking angle for the neck. Future work will evaluate user perception of the natural gaze system in a social interaction activity using standardized questionnaires.
ALLIE: Autoencoder-based Low-Light Image Enhancement
Operating in low-light environments remains one of the most critical challenges for autonomous robotic systems. Under such conditions, image quality is often compromised by noise, reduced detail, and low visibility, severely limiting the system's ability to perceive and interact with the environment. In this work, we propose ALLIE, an innovative deep neural network model based on an Autoencoder architecture, designed specifically for enhancing images taken in low-light conditions. Unlike traditional methods that rely on manual adjustment of brightness, contrast, or noise filtering, ALLIE performs enhancement automatically through end-to-end learning. The network learns to transform dark images into well-exposed versions by capturing complex image representations that improve clarity while preserving important scene details. We evaluated the proposed method on the paired Low-Light (LOL) image dataset, in both versions v1 and v2, using reference-based quality metrics such as PSNR and SSIM to quantify fidelity between enhanced images and their corresponding groundtruths. Experimental results demonstrate that ALLIE outperforms conventional and state-of-the-art approaches, producing images with superior visual quality, which are suitable for diverse applications in robotics and computer vision.
An Appearance-Based Approach for Indoor Environment Semantic Classification for Autonomous Navigation
Autonomous ground vehicles operating in indoor environments must accurately perceive and classify their surroundings to ensure safe and efficient navigation. A fundamental step in this process is the semantic classification of environments, which provides contextual awareness to decision-making modules. In this work, we propose an appearance-based indoor environment semantic classification process that leverages DINOv2, a self-supervised vision transformer, for feature extraction. The extracted features are used to identify the type of indoor environment such as corridors, offices, or storage rooms based on visual features. To evaluate the effectiveness of our approach, we trained and tested eight different state-of-the-art deep learning models to validate the proposed semantic classification pipeline. Experiments were conducted on the KTH-IDOL2 dataset, comprising diverse indoor scenes captured from the perspective of mobile ground robots. Results demonstrate that our approach based on DINOv2 produced the best overall performance, with ConvFormer obtaining the highest classification accuracy, followed closely by DenseNet201. The proposed pipeline demonstrates to be a promising strategy for enhancing the perception capabilities of autonomous systems operating in structured indoor environments. Additionally, experiments evaluating images under varying temporal and lighting conditions were conducted, demonstrating the robustness of the proposed approach.
Ultra-T: UERJBotz's Mini Autonomous Sumo Robot
Robotics is a well-established means of project-based approach to create knowledge in areas such as mechanics, electronics, computer science, control and automation, communications and artificial intelligence. At universities, robotics provides ways to expand student abilities beyond those acquired within regular classes and laboratories, stimulating teamwork, critical thinking, creativity, autonomy and entrepreneurial attitude. Robot competitions provide a great way to practice robotics, directing challenges that stimulate students to design different types of robots while enjoying themselves along the way. Among the most popular are sumo robots' competitions. In such context, this paper presents the development of a mini sumo robot capable of competing in both radio-controlled and autonomous categories. It is a result of the work of three undergraduate students based on previous robots designed by UERJ robotics team. Design premises, fabrication and programming details are discussed. Its use on a recent competition evidence project's success not only from a technical point of view, but also from an educational perspective.
Magnetic Modular Robotics: An Educational Approach to Robot Competitions
This research presents the design and implemen-tation of a modular robotic platform for educational purposes, based on the mini sumo competition modality. The developed robot consists of magnetically connectable modules that sim-plify both mechanical and electrical integration, eliminating the need for exposed wiring and reducing setup complexity. The platform is based on an Arduino Nano microcontroller, supporting infrared and ultrasonic sensors. It features a modular structure with magnetic alignment and Schottky-based Graetz bridge protection, which helps prevent damage caused by po-larity reversal. The created platform demonstrates modularity, robustness during reconfiguration, and effective operation in sumo competition environments. The proposed solution enhances STEM engagement by enabling rapid prototyping, sensor exper-imentation, and iterative control strategies in hands-on learning settings.
Development and Control of a Quadruped Robot via SolidWorks and MATLAB/Simulink Modeling
This paper describes the development, modeling and simulation of a quadruped robot, with an emphasis on generating stable walking patterns through kinematic control and dynamic analysis. The robot's three-dimensional structure was designed in a CAD environment, considering symmetry and mechanical feasibility criteria, and then imported into MATLAB/Simulink using the Simscape Multibody tool. The analytical equations were implemented in functional blocks in Simulink, allowing the joints to be actuated according to predefined trajectories. To ensure smooth and continuous movements, these trajectories were parameterized by third-degree Bezier curves. Gaits were defined as coordinated sequences, with independent phase, length and step height adjustments. Stability was assessed visually in Mechanics Explorer and quantified in MATLAB, which calculates in real time the support polygon formed by the legs in contact with the ground. The simulated results demonstrate the effectiveness of the gaits, keeping the center of mass within the stability zone.
Denguebusters: Technological Innovation and Robotics Applied to Dengue Control
The DengueBusters project was developed within the scope of basic education, aiming to integrate science, technology, and citizenship in the search for solutions to combat dengue. Based on Educational Robotics as a methodological axis and grounded in the principles of constructionism, the STEAM approach, and active learning methodologies, the project proposed the development of low-cost automated prototypes for monitoring and controlling the Aedes aegypti mosquito. The process involved scientific investigation, community awareness initiatives, and the development of skills in electronics, automation, and programming, along with social mobilization. The results showed that, beyond acquiring technical skills, students also developed socio-emotional competencies, critical thinking, leadership, and a sense of social responsibility. The project reinforces the idea that integrating Educational Robotics with real-world problems enhances meaningful learning, generates social impact, and contributes to the formation of citizens capable of positively transforming their communities - thus aligning with the Sustainable Development Goals (SDGs) of the 2030 Agenda.
Both of Us: Development of a Multi Featured Service Robot
Service robots require the integration of multiple technologies such as Simultaneous Localization and Mapping (SLAM), computer vision, and human-computer interaction to enable effective development. This paper presents the development of a modular, low-cost service robot designed for educational and research purposes. The robot integrates a range of hardware components including RGB-D cameras, LiDAR, and a mobile base with a software stack based on the Robot Operating System 2 (ROS 2). A feature is the implementation of a voice-controlled virtual assistant powered by Large Language Models (LLMs), enabling natural human-robot interaction. The system architecture emphasizes modularity, accessibility, and real-world applicability, supporting experiments in SLAM, computer vision, and multimodal interaction. The robot's construction combines off-the-shelf components, 3D-printed parts, and open-source tools, making it an effective learning platform for robotics and AI. The paper also discusses the technical and cost challenges faced during development, and outlines future improvements including enhanced sensor fusion and SLAM benchmarking.
PID- Based Stabilization of a One- DOF Educational Aero-Pendulum
This work presents the development and experimental validation of a low-cost didactic platform based on a one-degree-of-freedom aero-pendulum controlled by a PID (Proportional-Integral-Derivative) controller. The platform was designed to support control systems education by enabling students to apply theoretical concepts in a real, nonlinear, and unstable system. The experimental setup includes a DC motor with PWM actuation, an angle sensor for feedback, and an Arduino Nano for controller implementation. A graphical interface developed in MATLAB App Designer was also employed for real-time visualization and data analysis. The controller was evaluated under classical input signals such as step, ramp, and sinusoidal references. Results demonstrate that the PID controller is capable of stabilizing the system with acceptable transient and steady-state performance. A comparative study of different gain configurations highlights the importance of proper tuning for system robustness, reinforcing the value of using physical platforms to enhance practical learning, conceptual understanding, and controller design skills in engineering education.
Jessica Pereira de Souza Xavier Evangelista
Robohead: An educational Robot for Deep Robot-Student Interaction
This paper presents an educational robot called Robohead, it is designed for a wide range of students: from elementary school to senior university courses. Robohead allows you to study the multi-machine interaction of ROS, computer vision, speech recognition and speech generation, base algorithms of control joints, touch gestures and AI integration. The paper demonstrates the architecture of Robohead and the experience of its use in educational and competitive activities.
Problem-Based Learning: A Study on Drone Trajectory Planning for Medical Logistics
The shortage of blood in hospital stocks is a critical challenge for the Brazilian healthcare system, especially during emergencies. In this context, the use of drones for blood transportation emerges as a promising alternative, capable of reducing response times and optimizing routes in both urban and remote regions. This paper presents a case study involving Engineering and Computer Science students from public institutions in southern Brazil, using a Problem-Based Learning (PBL) approach. Students were challenged to plan drone trajectories through manual calculations and computer simulations. The results allowed for a comparative analysis of accuracy between the methods and highlighted the pedagogical potential of emerging technologies in training professionals capable of addressing the challenges of medical logistics. The findings also demonstrate the technical and educational feasibility of using drones in the transportation of blood products.
A ROS2-Based Robotics Course for Undergraduate Program
As the field of robotics continues to expand and influence various sectors, it becomes increasingly important to prepare engineering students to work with modern robotic systems. Practical knowledge and real-world experience are essential components in developing these skills. To bridge the gap between theory and practice, this paper presents a structured undergraduate course that introduces students to the Robot Operating System version 2 (ROS 2) using simulation-based and hands-on learning activities. Based on official ROS documentation and tools such as Gazebo and MAVROS, the course guides students through the foundational concepts of robotic control, embedded systems, robot modeling, and autonomous navigation. Each class is designed to progressively build technical skills while reinforcing theoretical understanding through project-based tasks. The primary goal is to enhance the comprehension of robotics topics by introducing a more hands-on and experimental learning environment. This kind of course equips students with computational tools, thereby significantly contributing to their hands-on experience in robotics.
Development of an Indoor Experimental Environment for Cooperative Navigation Using UWB-Based Positioning for Research and Education
The integration of technological advancements in engineering education faces challenges in providing interactive, safe, and practical learning environments. Cooperative robotics, particularly involving of Unmanned Aerial Vehicle (UAV), presents significant opportunities for enhancing academic engagement by facilitating hands-on experimentation and active learning methodologies. However, traditional indoor positioning systems, such as GPS, are inadequate for controlled laboratory settings. This paper proposes an integrated approach that combines a real-world laboratory environment with a simulation framework to enhance cooperative robotics education. The system utilizes Ultra-Wideband (UWB) technology for precise indoor positioning, enabling collaborative navigation between aerial and terrestrial robots. A key contribution of this study is the development and implementation of a dual-environment setup, comprising a physical testbed and a Webots-based simulation, allowing students and researchers to conduct safe and scalable experiments before real-world deployment. Experimental validation demonstrated the effectiveness of the proposed approach by comparing the trajectory accuracy of UAV and rovers in both simulated and physical settings. The results confirm that the system offers a reliable platform for robotics education, with trajectory execution error below 0.4 m and consistent synchronization between simulation and real-world tests, enabling students to develop practical skills in autonomous navigation, positioning technologies, and robotic collaboration. Furthermore, this integrated approach facilitates progressive learning by enabling the application of concepts from basic to advanced levels within the simulation before real-world implementation, supporting continuous educational development in emerging technologies. This work highlights the importance of integrating simulation with real-world testing to foster an effective learning environment in engineering education.
Teaching Continuous Action Space Reinforcement Learning with a Mobile Robot
This paper presents a teaching framework that integrates continuous action space reinforcement learning with a mobile robotics platform. Reinforcement learning (RL) enables agents to learn optimal decision-making policies through interaction with their environment, making it a natural fit for robotics applications. The study examines the challenges and solutions for applying RL algorithms, specifically SARSA and Actor-Critic methods, to continuous state and action spaces. These methods are implemented and compared on a differential-drive robot tasked with maintaining a specific orientation and position. A virtual model is utilized to facilitate safe and efficient learning before deploying the algorithms to a physical robot. This study provides practical insights into teaching reinforcement learning concepts and leveraging them for robotics applications.
Robotic Trajectory Planning Using Computer Vision Based on Signaling Marker Identification and Graph Theory Applications
A mobile robot, called Sunrise Robot, was used in this work to develop an autonomous movement from one point to another. For this purpose, Graph Theory was used, through the Dijkstra's algorithm, which modeled a trajectory between vertices in a directed graph, determining a displacement trajectory. Traffic markers were associated with the vertices and computer vision through YoloV8 recognized the visual elements in these markers. An algorithm was developed to receive the results of computer vision and associate them with the mathematical model of the graphs. This algorithm made the decision for the autonomous movement of the robot, moving it in the correct direction determined by the graph trajectory. The results showed promise in the training and identification of vertices and traffic markers, as well as in the autonomous movement of the robot in detriment of the direction defined by the directed graph.
VRI4WD UFPR-MAP: A Multi-Sensor Dataset with Geodetic Ground-Truth for Wheeled Mobile Robots in Hybrid Environment
This paper presents the UFPR-MAP dataset, collected with the VRI4WD mobile robot platform equipped with diverse low-cost sensors, including GNSS, laser scanner, IMU, ultrasound, infrared, encoders, and WLAN signal receivers. Unlike high-end datasets, UFPR-MAP focuses on affordable sensor blend sets for indoor and outdoor environments, validated through rigorous ground-truth via GNSS and total station measurements. The dataset supports research on multi-sensor fusion and wheeled mobile robot localization in varied architectural contexts. The VRI4WD UFPR-MAP dataset is available in https://github.com/VRI-UFPR/ufpr-map.
Analysis of Rolling Shutter Correction Techniques for Visual SLAM
Visual SLAM methods have become increasingly more popular in the latest years, largely due to their ability to achieve high accuracy while primarily relying on passive camera sensors. However, many of those algorithms restrict their scope to Global Shutter cameras, as the distortion caused by the Rolling Shutter readout can significantly degrade their performance. Still, CMOS cameras have become well established within the consumer market since they have lower cost and lower energy consumption compared to CCD. Recently, Rolling Shutter correction methods, notably those based on deep learning, have started to achieve remarkable accuracy. This work analyzes the performance of a visual odometry solution on multiple sequences of rolling shutter corrected images from the TUM RS-VIO dataset. Three state-of-the-art deep learning based correction methods were selected for analysis: 2F-DFRSC, JAMNet and SUNet. Results indicate that the sequences corrected by the first two algorithms are able to achieve a trajectory error comparable to those from Global Shutter sequence. Nevertheless, there is still room for optimization in terms of inference time before these solutions can be effectively used for real-time visual SLAM.
Robotics Education in Schools: Ac n Inclusive and Scalable Pedagogical Approach
This paper presents a pedagogical proposal for introducing robotics education to children aged 8 to 17 in public schools, with a specific focus on 3D printing and robot construction. Grounded in constructionist learning theory, the approach combines theoretical instruction, hands-on workshops, and formative assessments to foster meaningful engagement in STEM. The methodology was implemented in a series of biweekly workshops conducted by the Titans robotics team in partnership with a local school in Brasilia, Brazil. Instructional content was delivered using open-source tools and adapted to low-resource settings to promote accessibility and inclusion. Evaluation through pre- and post-tests demonstrated significant learning gains, particularly in concepts initially unfamiliar to students. The results support the effectiveness of structured, practical robotics education in fostering STEM competencies at an early age. Limitations and directions for future expansion, including broader implementation and longitudinal tracking, are discussed.
Transforming Engineering Education with Hands-On Robotics: The IEEE RAS-UNB Experience
The growing disinterest of engineering stu-dents in control theory and robotics, combined with the gap between theory and practice in traditional teaching methods, motivated the creation of an innovative approach by the IEEE RAS student chapter at the University of Brasilia. The goal was to develop and imple-ment a practical STEM-based methodology integrating teaching, research, and outreach through the use of real, accessible robotic platforms. The methodology was applied in four distinct projects-VSSS, EVAIMARIA, Edubot, and UnBeatables-involving undergraduate and graduate students in activities such as prototype con-struction, participation in robotics competitions, and scientific production. As a result, there was a significant increase in student engagement, membership growth, the publication of seven papers at IEEE international conferences, and the development of low-cost educational robots with high replicability potential. The implemented methodology proved effective in promoting interest in the field and can be adapted by other institutions, contributing significantly to strengthening engineering education and training new researchers.
Educational Robotics in a Non-Formal Context: A Comparative Analysis of Attitudes in an NGO (2023–2024)
This study investigates the impact of educational robotics interventions on the attitudes of young people in a Non-Governmental Organization (NGO) over two years (2023–2024). Using the Robotics Attitudes Measurement Instrument, four constructs were assessed: willingness to learn, self-confidence, computational thinking, and teamwork. A significant positive progression was observed in the mean scores of all constructs between 2023 and 2024, notably in Willingness to Learn (from 3.06 to 3.61) and Self-Confidence (from 2.76 to 3.24). Analyses, including gender differences, correlations between constructs, and qualitative reports from teachers, demonstrate the potential of robotics to foster engagement and positive attitudes in non-formal educational settings.
Educational Robotics and Block-Based Programming: Teacher Education in Brusque/SC
This article presents the experience of the Itinerant Educational Robotics Laboratory Project (LIRE) in the public school system of Brusque, Santa Catarina, Brazil. The initiative aims to democratize access to educational robotics and digital culture through the continuous training of teachers and monitors. The project promotes the use of block-based programming platforms-such as Scratch, Mblock, and Tinkercad-as accessible and intuitive tools for developing pedagogical practices aligned with the Base Nacional Comum Curricular and active methodologies. The results show increased engagement and autonomy among educators, interdisciplinary teaching practices, and significant student involvement in robotics clubs. Challenges such as infrastructure limitations and initial teacher resistance were addressed through methodological strategies focused on hands-on learning and collective experimentation.
Smart Cities: A Pedagogical Proposal for Arduino in Educational Robotics
This paper presents a pedagogical proposal for an introductory Arduino course focused on Educational Robotics. Classes are based on the Project-Based Learning (PBL) method-ology, covering technical content on electronics and programming, in addition to drawing parallels with the subjects of the National Common Curricular Base (BNCC) for high school students. The teaching process includes theoretical and practical classes, teamwork, exposure to everyday problems and chal-lenges, integrating the concept of smart cities, where students themselves will have to automate it through pre-structured models.
Robotics in Schools as a Way of Promoting Science and Engineering
Maker culture and educational robotics are approaches that foster experimentation, creativity, and logical reasoning. However, a decline in young people's interest in scientific and engineering careers has been observed in Brazil. To reverse this trend, the ROBESC (Robótica Escolar) project proposes an innovative approach by integrating maker culture into robotics education in public schools and vulnerable communities. The methodology comprises two-day workshops introducing robotics concepts, the assembly and programming of LEGO®MINDSTORMS®NXT robots for a sumo competition, all guided by engineering students. Results-based on pre- and post-workshop surveys administered in three schools-indicate that most participants, who initially had no prior knowledge of the subject, reported being moderately capable of applying robotics concepts. Through the project, students developed different robot architectures and programming strategies, enhancing logical reasoning, programming fundamentals, and teamwork. It is concluded that ROBESC proves to be an effective tool for disseminating science and engineering, stimulating student interest, and strengthening both hard and soft skills, thereby forging a vital link between higher education and basic education.
Robotics Education and its Role in Enhancing Academic Persistence and Achievement in the Triângulo Mineiro Region
This article investigates the impacts of higher education students' participation in robotics projects on their academic retention and success. Drawing on the analysis of experiences and project proposals developed in the Triangulo Mineiro region of Brazil, the study discusses how educational robotics can enhance student engagement, motivation, and the development of essential skills, thereby contributing to reduced dropout rates and improved academic performance. The distinctions between educational and pedagogical robotics, as well as the historical context of their application in Brazil, are examined. Recent experiences employing machine learning and data visualization to assess the impacts of robotics initiatives-including those aimed at increasing female participation-underscore the relevance and the need for more in-depth analyses. Although direct statistical data to quantify causal effects remain limited in publicly available databases for this specific region, qualitative evidence and practical examples based on local initiatives indicate a significant positive impact. The study highlights the importance of such initiatives for the comprehensive education of students in engineering and technology programs.
Enhancing Expressive Communication in Children with ASD Through a NAO Robot-Mediated PECS-Adapted System
Children with Autism Spectrum Disorder (ASD) often face significant challenges in verbal communication and social interaction, impacting their educational inclusion. This project paper outlines a proposed intervention to address these challenges by integrating social robotics with an adapted Picture Exchange Communication System. We propose the use of the NAO V6 humanoid robot as a mediator for a PECS-Adapted system, wherein children utilize visual cards featuring QR codes to express actions, desires, and feelings. The robot is designed to recognize these QR codes, verbalize the corresponding message, and provide interactive feedback. This research, grounded in the Design Science Research framework, will involve a qualitative, exploratory study with five children with ASD, aged 5–10 years, over a two-month period. The intervention aims to enhance expressive communication, increase communicative spontaneity, and facilitate social interaction, ultimately promoting more effective school inclusion. This paper details the system architecture, proposed methodology, and expected contributions of this novel approach within the Brazilian educational context.
Cleaning the Oceans: A Pedagogical Practice of Environmental Awareness with Robotics in Early Childhood Education
This article analyzes a pedagogical experience carried out with 3- and 4-year-old children from Early Childhood Education, through a pedagogical practice that addresses ocean cleanup, within the scope of the MandacaruBot extension project (EAJ/UFRN). The proposal integrates scientific, environmental, and technological knowledge with the use of sustainable Educational Robotics and unplugged robotics, seeking to promote scientific literacy and computational thinking in a playful and accessible way. The activities involved the construction of artifacts, interactive game, and group discussions, according to the guidelines of the Common National Curriculum Base (BNCC) and the Computing BNCC. In practice, hydraulic arms developed with syringes, gamification, computational thinking, and unplugged robotics were used. The results demonstrated the development of motor, cognitive, and socioemotional skills in children, in addition to engagement with issues of sustainability and citizenship.
Game Interface for Inclusive Teaching Through Educational Robotics
This article presents a system that uses computer vision and machine learning to interpret gestures in Libras with a focus on promoting social and educational inclusion through an interactive game. The system integrates the YOLO architecture for real-time detection and classification of hand signals. The training data was built from 1735 images of static gestures from the Libras alphabet, captured under different conditions and manually annotated. The model achieved an Overall Average Accuracy of 98.9% and an Overall Accuracy of 89.5%. The classification of gestures allowed the user to control a game environment where sign language signs corresponded to drawing movements of geometric shapes. As far as we know, the direct integration of LIBRAS sign recognition with educational robotics through a game, with an explicit focus on inclusion, highlights the novelty of this research.
Programming Teaching using Arduino and TinkerCad Circuits - Proposal of a Lesson Sequence
This paper presents a lesson sequence to teach programming using the Arduino platform and the free TinkerCad Circuits environment, promoting hands-on activities. The sequence supports block-based and C-like coding, enabling students to learn core programming concepts through online simulations or physical hardware. Freely available materials, including circuit designs and video lessons, support the approach. The sequence was applied in two distinct scenarios: a Multiuser Massive Online Course and in ten freshmen engineering courses of programming. Both experiences are described in the text, focusing on learning outcomes, engagement, and challenges. The main contribution of this work is the presentation of a set of free multimedia course material, together with a flexible, accessible framework for programming education focused on young adults, using a free simulation tool.
A Low-Cost Social Robot for Gesture-Based Educational Activities and Human-Robot Interaction in Learning Environments
This work presents the design and implementation of a low-cost social robot intended for use in educational environments. The robot is constructed using affordable materials such as cardboard and PVC, and uses Arduino-compatible components for actuation and control. It is capable of recognizing and mimicking basic human gestures, such as waving, nodding, and head-shaking, through computer vision techniques using MediaPipe and OpenCV. The system architecture combines gesture recognition, real-time communication, and servomotor actuation to simulate socially meaningful interactions. Beyond its technical capabilities, the robot aims to enhance classroom en-gagement, support socio-emotional learning, and promote hands-on experiences in robotics and programming. The platform is designed for replicability and accessibility, making it particularly suited for public schools and STEM education initiatives.
Using Arduino Alvik for Learning Robotics in Educational Environments
This paper presents Alvik, a new educational robotics platform developed by Arduino, as a promising resource for future robotics education. We explore how Alvik's versatility and broad application potential make it particularly valuable for classroom use. The article outlines key concepts and features of the robot, assessing its suitability as an effective educational tool in the field of computational robotics, especially considering its user-friendly design. Additionally, we propose an original teaching track layout aimed at engaging students through a dynamic and innovative learning experience.
Design of a Low-Cost Autonomous Multitasking Robot for Educational and Competitive Applications
This article presents a low-cost, autonomous multitasking robot designed and implemented by students of a High School technical. Multitasking robots are advantageous in critical environments, such as automated industries and extreme conditions, as they are equipped with features such as line following, object manipulation, obstacle avoidance, and distance measurement, among others. However, the complexity of these functionalities often leads to high production costs and the use of materials that are difficult to obtain. To address this challenge, we propose the design and implementation of a multitasking robot that is both accessible and sustainable. Its construction combined electronic components with disposable materials, enhanced through modeling and 3D printing techniques. The total robots' initial cost was reduced from R$438.94 to R$328.89. Testing has yielded promising results, as demonstrated by the robot's successful performance in the Regional Brazil Robotics Tournament. The main contributions include the creation of an affordable and functional multitasking robot that can be adapted for educational purposes and various socially relevant applications, promoting access and innovation in educational approaches to robotics.
Developmental Enhancement and Application of a Low-Cost Educational Robotics Kit: A Case Study Validation with a University Competition
This paper presents the latest developmental im-provements done on the URA low-cost robotic kit, which uses a open-source and low-cost robotics paradigm and was designed for general activities in educational robotics. Our kit is planned with the aim of facilitating the teaching of the basic concepts of programming, electronics, and mechanics. The current version (9.0) was redesigned with a focus on accessibility and intuitiveness in order to meet the demands of practical training in the educational context with limited infrastructure. The validation of the developments occurred in a mini-robotics competition held at the School of Science and Technology of the Federal University of Rio Grande do Norte, where freshmen undergraduate students assembled a robot with the kit and faced challenges inspired by the Brazilian Robotics Olympiad (OBR). The results pointed to the effectiveness of the kit in forming student teams, as well as its potential for replication in public schools and robotic events. This paper contributes with the development stages, the components, a simulation software, and programming methodology, pointing out the main difficulties encountered during implementation.
OHMLY: Development and Application of an Arduino-Based Educational Kit for Teaching Ohm's First Law in Youth and Adult Education
This article presents the results of a master's research project that investigated the effectiveness of Educational Robotics integrated into physics instruction to enhance the understanding of Ohm's First Law among Youth and Adult Education (YAE) students in a public school located in the city of Extremoz, state of Rio Grande do Norte, Brazil. Using Ausubel's Theory of Meaningful Learning and the Design Science Research (DSR) methodology, an Arduino-based educational kit was developed and implemented. Through hands-on activities, the study aimed to promote student engagement by relating theoretical concepts to practice and everyday life, addressing challenges commonly faced by this group. The results indicate that the use of Educational Robotics, combined with active methodologies, contributes to more meaningful and motivating learning experiences in physics education.
Promoting Low-Cost Educational Robotics: A Project-Based Learning Approach for Public High Schools Preparing for the Brazilian Robotics Olympiad
This paper presents a project-based educational initiative designed to promote low-cost robotics in Brazilian public high schools, with a primary focus on preparing students for the Brazilian Robotics Olympiad (OBR). The project integrates concepts from Constructionism and Project-Based Learning to offer hands-on experiences in programming, electronics, and robot assembly using accessible platforms such as Arduino and ThinkerCad. The methodology was implemented over seven sessions and evaluated through formative assessments and student feedback. Preliminary results indicate improvements in students' confidence, problem-solving skills, and engagement with STEM topics. Despite positive outcomes, the study acknowledges limitations related to sample size, lack of control groups, and non-validated assessment instruments. Future work will address these limitations and explore strategies for broader dissemination and impact evaluation.
The Impact of the Edubot Project on Students' Entry Into STEM Fields
This study examines the impact of the Edubot Project, an educational robotics project created by the University of Brasília IEEE Robotics and Automation Society, on Brazilian student's interest in STEM (Science, Technology, Engineering, and Mathematics) subjects. This study employs a qualitative methodology to examine interviews with two former participants who went on to pursue STEM careers, examining how practical robotics education improved their technical proficiency, and problem-solving capacity. The results show that although the initiative mainly supports STEM-inclined students' future choices, it also greatly enhances their academic readiness by enhancing computational thinking, programming logic, and exposure to college life. The results highlight the potential of robotics-based interventions to bridge educational gaps and inspire future STEM professionals in underprivileged settings.
Undergraduate Experiences in Extension-Based Programming Education for Public School Students
This study explores the impact of an outreach initiative on undergraduate Computer Engineering students at CEFET-MG campus, Divinopolis, who taught programming and robotics in local public schools. Adopting a participant research methodology, the study collected data through structured surveys and interviews conducted between 2023 and 2024. Findings reveal that the project significantly developed essential competencies in the undergraduate students, such as communication, teamwork, and problem-solving. It also had a positive impact on the partner schools by enhancing students' logical thinking and increasing their interest in digital technologies. However, the study also identified key challenges, including limited resources, logistical difficulties, and the need for additional pedagogical training for the student instructors. The paper concludes by emphasizing the need for structured logistical support, didactic preparation, and institutional partnerships to maximize the potential of such projects. It recommends that future studies incorporate longitu-dinal analyses and the perspectives of public school students to gain a more complete understanding of the project's long-term effects.
Enhancing Technical and Soft Skills Through Robotics: The AraraBots 15-Year Case Study
AraraBots is an extension initiative at the Faculty of Computing (FACOM/UFMS) focusing on robotics in education and competition. This paper presents a case study on the educational impact of the AraraBots project on computer science and computer engineering programs. The study analyzes how participation in AraraBots contributed to undergraduate students' technical and soft-skills development in Computer Science and Computer Engineering programs. A survey based on the Technology Acceptance Model (TAM) was conducted with 43 students who participated in the project between 2010 and 2025, revealing that students perceived significant improvements in technical skills, especially in programming, electronics, and project management. The analysis combined quantitative data (scores, percentages) with qualitative data (free-text responses). Additionally, the project fostered the development of soft skills such as teamwork, communication, and leadership. More than 80% of participants agreed that AraraBots filled gaps in the formal curriculum, providing practical experiences and interdisciplinary learning not covered in traditional courses. The findings highlight the role of robotics extension projects in enhancing academic engagement and professional preparation for students in computing fields.
Juca, An Embedded Mobile Robotics Board for Education
Mobile robotics has become an essential tool in educational environments due to its dynamic and practical nature, which allows students to experiment with real-world applications. This paper introduces “Juca”, an embedded mobile robotics board designed specifically for education. Juca combines affordability, expandability, and robust functionality, making it suitable for classroom and competition environments. Key features include modular hardware akin to Arduino shields, support for differential drive robots, and seamless integration with educational programming platforms. Experimental results demonstrate its effectiveness in fostering hands-on learning and engagement.
A Systematic Review on Teacher Training in Educational Robotics: Global and Brazilian Perspectives
This paper presents a systematic literature review on teacher training in educational robotics, with a twofold objective: to map the global research landscape and to provide a focused analysis of the Brazilian context. The review was guided by spe-cific research questions and conducted using the Scopus and Web of Science databases for the global perspective, complemented by the Brazilian Digital Library of Theses and Dissertations (BDTD) for the local analysis. Our findings highlight Brazil's significant international contribution, ranking second in the number of publications in both Scopus (6 out of 28) and Web of Science (4 out of 15). At the national level, our analysis identified 11 recent theses and dissertations completed between 2020 and the present. These results indicate that robust methodologies for teacher training in Educational Robotics are not only being actively researched but also effectively implemented throughout Brazil.
Establishing an Online Community of Practice for Robotics Educators
This paper aims to present and analyze the development of an Online Community of Practice (CoP) composed of teachers who graduated from a hybrid Educational Robotics (ER) course in the state of Espírito Santo, Brazil. The CoP was established to foster deeper engagement with technological and methodological studies, as well as to design and share investigative and problem-solving pedagogical practices involving robotics. This initiative is part of the Educational Product developed within an ongoing Professional Doctorate research project, which adopts action research as its methodological framework. The study enabled the identification of the group's stage of development and an in-depth analysis of its formation process. The findings indicate that the CoP is an effective approach to continuing teacher education in ER, as it promotes collaborative learning, allows for flexible engagement with study topics, and supports the creation of a collectively validated repertoire of teaching practices.
Educational Robotics Practices and Progress in Brazil and Peru
Educational Robotics (ER) has emerged as a dy-namic tool to enhance learning in STEAM (Science, Technology, Engineering, Arts, and Mathematics) education, fostering com-petencies, creativity, and problem-solving skills. Latin America, particularly Brazil and Peru, has seen a growing interest in incorporating ER into school curricula. However, the scale and form of implementation differ significantly between these two countries, especially when looking at public and private educational institutions. This article presents a joint analysis of ER policies, practices, and results in public and private schools in Brazil and Peru. Through examining national initiatives, pedagogical methodologies, and teacher training programs, we elucidate the advancements and challenges in integrating ER into educational systems of both countries. We highlight the challenges, innovations, and educational impacts observed in both nations and provide recommendations for equitable and effective expansion.
Robotics and AI as Resources for Teaching Geometry in Argentine Secondary Schools
This work presents a proposal for Geometry teaching in secondary schools in Argentina using robotics and Artificial Intelligence (AI) as class resources. This proposal is materialized via a Mathematics problem that involves concepts such as distance, symmetry and turn angles. The algebraic solution to this problem, in its most general version, is complex for the secondary school level, since it involves two variable functions and their derivatives. However, a simplified version can be used at this level enabling the use of robots to explore the problem as well as to provide a solution in the learning context and according to the background of students.
Using LLM for Learning Objects Metadata Extraction on Educational Robotics Content
RepositORE is a collaborative web platform for registering learning objects and educational robotics activities. Manually adding new objects - with their 32 metadata fields each - quickly becomes a tedious and discouraging process for users. This complexity discourages educators and developers from incorporating new resources and maintaining the repository up-to-date. Automating the extraction and completion of metadata would reduce errors, save time, and encourage sharing new content, thereby strengthening the robotics education ecosys-tem. Large Language Models (LLMs), pre-trained on vast text corpora, are capable of understanding the nuances of human language and generating contextualized responses. Owing to their prior training across diverse domains, they can handle questions and commands on a wide variety of subjects, thereby enhancing educational applications and efficiently automating complex tasks. This article presents a methodology for automatically evaluating and capturing educational robotics content from text content. This methodology employs Prompt Engineering (PE) to guide the LLMs in locating and extracting from heterogeneous sources, improving the precision and consistency of the outputs. The developed subsystem, RobotFinder v3, extracts metadata from web sites that contain relevant material and incorporates it into RepositORE. It was also applied to extract robotics-specific metadata through the analysis of YouTube video subtitles using LLMs. For this purpose, we employed the Llama 3.1, DeepSeek R-l, and Gemma 3 models for relevance assessment and data extraction. In our experiments, Llama 3.1 and Gemma 3 demonstrated superior performance in this highly specialized context.
Ryllari Raianne Marques De Santana Costa