Instagram THE TEAM OUR HISTORY SOFTWARE GABRIEL CAVALCANTE We are Brainbots and we will be representing Brazil in Robocup Junior Rescue Line 2026! This is our fifth year in As team leader, I oversee the robot’s structural design and overall development. I design the chassis, rescue Robocup, and our second year participating internationally. With a total of three second places and one victory at PROGRAMMING LANGUAGE AND DEVELOPMENT ENVIROMENT our local district championship, one third place and one victory at Brazil championship, and participation at the last mechanisms, and other components using CAD and 3D printing, focusing on compact and efficient The main program relies on Arduino based C++ and Python. Meanwhile, the Raspberry Pi 5 handles computationally year´s world championship, where we finished 7th, we are thrilled for this year´s competition in Incheon! solutions. I also support the assembly and integration of the robot’s mechanical and electronic systems. intensive computer vision and artificial intelligence processing, using Python together with libraries such as OpenCV and NumPy, playing a major role in the functionality of our software system. CAIO FIDELIS As one of the lead programmers, I specialize in line-following algorithms and movement control systems. I improved navigation accuracy through PID-based tunings and adaptive movement strategies. I also refine the robot’s performance on curves, intersections, ramps, and uneven surfaces to ensure reliable motion. ARTHUR NOGUEIRA Since joining Brainbots, I have worked on the robot’s electrical integration and mechanical assembly. I am Brasil Championship 2025 District Championship 2025 World Championship 2025 responsible for wiring, power management, and component integration. I also perform maintenance and repairs to ensure reliable performance during competitions. SOFTWARE ARCHITECTURE PEDRO GOMES The robot uses an Arduino Mega for sensor processing, motor control, PID-based navigation, and autonomous My primary responsibility is the robot’s rescue room and computer vision system. I developed the rescue decision-making, while a Raspberry Pi handles computer vision and AI tasks. Two cameras operate simultaneously: logic and vision architecture for victim recognition and autonomous decision-making. Using AI, OpenCV, and one for line tracking and navigation using OpenCV, and another for victim detection and evacuation zone analysis real-time object detection, I improved the robot’s reliability during navigation and rescue tasks. accelerated by the Hailo-8 AI module. A custom serial protocol enables real-time communication between both Brasil Championship 2024 District Championship 2024 Brasil Championship 2023 systems, ensuring efficient task distribution, fast decision-making, and reliable performance during Rescue Line competitions. OUR ROBOT EVACUATION ZONE LINE FOLLOWER CHARLES JR. The evacuation-zone system is activated as soon as the dedicated silver sensor detects the entrance to the rescue area. At this point, the robot transitions from line-following mode to a vision-based rescue mode, using the victim-detection camera and an AI model to continuously identify victims and evacuation points. Once a victim is detected, the robot approaches it, performs the collection procedure, and stores it in its onboard compartment. The robot performs autonomous navigation using a wide-angle camera operating at approximately 25–30 FPS. The vision algorithm extracts key points from the detected black line to estimate its geometry, enabling reliable path tracking, intersection handling, green marker recognition, gap detection, obstacle avoidance, and red strip identification. Since the camera is positioned significantly ahead of the robot’s center of rotation, a bi-vectorial navigation system was The robot features a custom 3D-printed PLA structure that Our strategy is to collect all victims before carrying out any delivery operation. Although this means the robot transports developed. Instead of relying on a single reference, the image is divided into near and far regions, generating two integrates all mechanical and electronic components while reducing multiple victims simultaneously, it significantly reduces unnecessary movement inside the evacuation zone and decreases the independent vectors that represent the immediate and future directions of the line. By combining these vectors, the robot weight, simplifying maintenance, and allowing rapid design overall rescue time. Victim detection is performed by a neural network accelerated by the Hailo-8 AI processor. Compared with prioritizes local corrections while anticipating upcoming turns, reducing premature steering and improving stability and modifications. Its drivetrain uses 100:1 Metal Gearmotor 20Dx44L traditional image-processing techniques, this AI-based approach proved more robust against variations in lighting, victim maneuverability in sharp corners and complex track layouts. The resulting steering angle is then processed by a PID mm 6V CB motors and polyurethane rubber wheels (Shore 20, 30 orientation, partial occlusions, and changes in camera perspective. controller, which generates smooth and accurate motor commands for high-speed navigation. mm radius × 20 mm width), providing reliable traction, stability, and precise maneuverability throughout the Rescue Line field. The modular design also facilitates the replacement and adjustment of components during testing and competition. The robot uses a wide-angle camera (200°) dedicated to line following and an other (125°) to victim detection. Both are connected to a Raspberry Pi 5, which executes the computer vision and artificial intelligence algorithms responsible for processing images and making autonomous decisions in real time. High-intensity LEDs provide controlled and uniform illumination, reducing the effects of shadows and ambient light variations to improve detection reliability. An Arduino handles real-time sensor acquisition and motor control, receiving commands from the Raspberry Pi and ensuring fast and reliable communication between all subsystems. HARDWARE SILVER RECOGNITION LEDs Locomotion system 125° Zone camera 200° Line camera 3D MODELING Recognizing the silver tile using the camera proved to be unreliable due to variations in lighting and surface reflections. To overcome this challenge, we adopted a dedicated hardware solution based on a silver detection sensor(TCRT 5000 IR COLLECTION MECHANISM JPM.V1 RASPBERRY PI 5 HAILO 8 ARDUINO MEGA Sensor), providing fast and consistent identification of the evacuation zone entrance. This approach increased reliability Organizes the Runs the robot’s AI, Responsible for Reads sensor data, while allowing the vision system to focus on navigation and The rescue gripper was designed with a fully 3D-printed structure to minimize weight connection, houses processes camera accelerating the controls the motors, victim detection. while maintaining the rigidity required for reliable operation. Its lightweight design two motor controllers images, and sends artificial intelligence and executes real- SketchUp reduces the load on the robot and enables faster and more precise movements during (DRV8833), and also real-time control model. time commands including an angle data. received from the We chose SketchUp to design our robot victim collection. The lifting mechanism is actuated by an S3003 (90–180°) servo, while sensor (BNO055). Raspberry Pi. because it provides an intuitive and two MG90S 9g metal gear servos control the opening and closing of the gripper to securely capture the rescue balls. This simple and compact design provides an efficient efficient environment for 3D modeling, allowing rapid prototyping and easy POWER SUPPLY and reliable solution for the rescue task. modifications throughout the The robot is powered by six 18650 lithium development process. Its user-friendly batteries. Two batteries supply the Arduino, interface enabled the team to quickly test motors, sensors, and servos, while the different layouts, verify component remaining four are dedicated to the Raspberry Pi HC-SR04 VL53L0X (ToF) UPS HAT placement, and optimize the chassis 5, providing a stable power source for the AI and We use three HC- Efficiently manages before manufacturing. Additionally, the computer vision systems and preventing We use three ToF SR04 ultrasonic the Raspberry Pi 5 compatibility of the models with 3D interference between the processing and sensors to detect sensors for obstacle power, eliminating printing workflows made SketchUp a actuation subsystems. side and front and wall detection. the need for external practical and cost-effective solution that distance variations converters. fit the needs of our project.