ROBOCUPJUNIOR RESCUE LINE 2026 TEAM DESCRIPTION PAPER Brainbots Arthur Nogueira, Caio Fidelis, Gabriel Cavalcante and Pedro Gomes Abstract Our robot was developed for the RoboCupJunior Rescue Line competition and is built around a computer vision architecture, where visual perception serves as the primary source of navigation and decision-making. Unlike traditional Rescue Line robots that rely heavily on dedicated line sensors, our system employs two cameras: one for real-time line following and track analysis, and another for victim detection and evacuation-zone operations. The robot uses a heterogeneous processing architecture consisting of an Arduino Mega for low-level control and motor management, alongside a Raspberry Pi 5 equipped with a Hailo-8 AI accelerator for computer vision, artificial intelligence inference, and high-level decision-making. To improve navigation accuracy, we developed a bi-vectorial line-following system that combines information from near and far sections of the detected path, reducing premature steering corrections and improving performance in complex track layouts. Victim recognition is performed using a custom-trained AI model accelerated by the Hailo-8, and whenever failure cases are identified, the dataset is expanded and the model is retrained through an iterative improvement process. The robot also incorporates a custom 3D-printed modular chassis, a dedicated rescue mechanism, and a custom electronics board, enabling robust autonomous navigation and efficient evacuation-zone operations in approximately 90 seconds. 1.​ Introduction a.​ Arthur Nogueira Arthur is responsible for the robot’s electronic integration, wiring, power distribution, and maintenance. He contributed to the design and assembly of the electrical system, including cable management, soldering, and subsystem integration. Throughout the development process, he ensured reliable communication between modules and performed testing, repairs, and preventive maintenance to improve the robot’s robustness and operational reliability. b.​ Caio Fidelis Caio is responsible for the robot’s navigation and motion-control systems. His work focused on developing the line-following algorithms, PID control loops, and movement strategies used throughout the Rescue Line course. He also contributed to the implementation and tuning of navigation behaviors for curves, intersections, ramps, obstacles, and recovery procedures, improving the robot’s accuracy and stability under challenging conditions. c.​ Gabriel Cavalcante Gabriel serves as the team leader and is responsible for the robot’s mechanical design and structural development. He designed the chassis, rescue mechanism, and most custom mechanical components using CAD and 3D-printing technologies. His contributions focused on creating a compact, modular, and maintainable architecture while ensuring efficient integration of mechanical, electronic, and computational subsystems. d.​ Pedro Gomes Pedro is responsible for the computer vision and rescue-room systems. He developed the vision architecture used for line analysis, victim detection, and high-level decision-making, combining OpenCV, artificial intelligence, and real-time image processing. He also designed the rescue-room strategy and contributed to the training, validation, and continuous improvement of the AI models used by the robot. 2.​ Project Planning a.​ Overall Project Plan Objective: After participating in RoboCupJunior Rescue Line for several consecutive years, our main objective this season is to surpass our previous competition results, including our 7th place finish at RoboCupJunior Rescue Line 2025, as well as the overall performance achieved by previous versions of the robot. To accomplish this goal, we focused on improving the robot’s reliability, navigation accuracy, victim detection capabilities, and autonomous decision-making throughout the development process. Table 1: Overview of Requirements and Final Solutions 1 Requirements Possible solutions Final Solution Follow the black Infrared sensors; We chose a camera-based vision system with OpenCV image line and detect Camera-based system processing and a bi-vectorial navigation algorithm because it provides green intersection greater adaptability and reliability performance on uneven terrain and markers and the challenging track conditions. red goal tile Detect the silver Dedicated silver We selected a dedicated silver detection sensor because our initial reflective tape at sensor; AI classification HSV-based approach was not sufficiently reliable. the evacuation model; HSV camera zone entry filter Detect obstacles Camera-based system; After evaluating several approaches, we concluded that a Pressure sensor; ToF front-mounted ToF sensor provided the most accurate and reliable distance sensor; short-range obstacle detection. Ultrasonic distance sensor Perform a 180° Time-based turning; We adopted gyroscope-based turning because it provides greater turn at double Gyroscope-based precision and consistency than time-based rotation. If the robot green markers turning becomes stuck during the maneuver, it can continue correcting its position until the turn is completed. Overcome Mecanum Wheels; We chose custom-made silicone wheels with small surface cavities bumpers, ramps, Self-made Mecanum that absorb impacts and improve stability on uneven terrain while and seesaws while wheels; Self-made maintaining strong traction on ramps. A stuck detection system was maintaining precise silicone wheels; Stuck also implemented to identify when the robot is trapped and line following detection system automatically execute recovery maneuvers. Compliance with Compromising on We maintained a compact design even with the victim storage height restrictions features for a smaller compartment mounted on top, allowing the robot to remain within robot; Compact claw competition height limits without sacrificing functionality. system Detect victims and Pattern search; AI We selected an AI-based detection model because it provides high evacuation points detection model; reliability and adaptability. When failures are identified, the dataset is OpenCV’s Hough expanded and the model is retrained to improve accuracy. Circles; Color Sensor Pick up victims Claw; Gripper; Cup We implemented a claw mechanism with two independently actuated mechanism arms. The claw lowers in a controlled manner to avoid debris before safely lifting the victim. Sort and deliver Storage with multiple We chose a compartment-based storage system because it reduces victims compartments; unnecessary movement and allows victims to be collected without Delivering each victim interrupting navigation. individually Adding a handle 3D printed handle; We selected a 3D-printed handle that also serves as a divider for the Metal wire handle storage compartment, improving structural efficiency without with electrical tape interfering with other components. Reliable power Single battery system; We adopted a distributed power architecture consisting of separate distribution Shared power bus; battery systems for locomotion and computation. This reduces Distributed power voltage drops caused by motor loads, minimizes electrical architecture interference, and improves the stability of the Raspberry Pi 5, Hailo-8 accelerator, and control electronics. 2 Project Schedule: After classifying for RoboCup Junior Rescue Line 2026, we began the new season by reviewing recordings and evaluating the performance of our robot in previous competitions, particularly our experience at RoboCupJunior Rescue Line 2025. By analyzing both competition runs and testing sessions, we identified limitations in the mechanical and software systems and used these findings to refine several of the solutions presented in Table 1. Based on this analysis, we established a development schedule focused on improving reliability, navigation performance, computer vision, and rescue-room operations. Tasks were distributed among the four team members according to their areas of expertise: Arthur (A), Caio (C), Gabriel (G), and Pedro (P). This structured planning enabled us to maintain steady progress throughout the season while ensuring that every part of the robot could be carefully tested, refined, and fully integrated before the competition. A detailed overview of the project timeline, milestones, and responsibilities is presented in Table 2. Table 2: Overview of Project Schedule Tasks Deadline Review of RoboCupJunior Rescue Line 2025 performance, identification of weaknesses, and Early August definition of requirements for the new season (A, C, G, P) Selection of core components, processing architecture, sensors, and drivetrain concept. Mid August Definition of the robot’s initial architecture for CAD development (A, G) Evaluation of previous AI models and navigation algorithms. Definition of strategies to improve Mid August reliability, confidence, and detection accuracy (C, P) Checkpoint: Validation of major design decisions and component selection, enabling detailed End August mechanical design (A, G) Design of the chassis, rescue mechanism, and structural components in CAD. Evaluation of Early motor and wheel configurations (A, G) September Optimization of communication protocols between the Raspberry Pi and Arduino. Initial Early software architecture implementation (C, P) September Optimization of drivetrain geometry, wheel design, weight distribution, and mechanical Mid September integration (A, G) Evaluation of camera models, mounting positions, lighting systems, and field-of-view End September configurations (P) Design and validation of the custom power distribution system and electronics integration Mid October strategy (A, G) Checkpoint: Completion and validation of the robot’s core CAD design, enabling manufacturing Early November and assembly preparation (A, G) Development and testing of line-following algorithms in simulation and bench-testing Early December environments before full robot assembly (C) Collection of image datasets, validation of camera systems, and initial victim-detection model Mid December training (P) Checkpoint: Assembly of the robot structure and line-following camera. Initial Early January hardware/software integration followed by the first autonomous track tests (A, C, G, P) Installation and integration of the rescue mechanism and victim-detection camera (A, G, P) Mid January Development and tuning of PID control and navigation behaviors for line following, Early February intersections, gaps, and recovery procedures (C) Expansion of the victim dataset and training of the final AI model under varying environmental Mid February conditions (P) 3 Checkpoint: Completion of the robot’s physical platform, including the chassis, drivetrain, End February electronics integration, rescue mechanism, and sensor installation. (A, G). Development of advanced navigation functions including obstacle handling, ramp handling, Early March stuck recovery, and robustness improvements (C, P) Development and integration of the rescue-room system, including victim collection, storage, End March and delivery strategies (P) Mechanical refinement, weight optimization, durability improvements, and End April maintenance-oriented redesigns based on testing feedback (A, G) Checkpoint: Fully functional robot completed. All subsystems integrated and validated, with Mid May only minor optimizations remaining (A, C, G, P) Extensive testing, reliability validation, competition simulations, preventive maintenance, and Mid June final preparation for RoboCupJunior 2026 (A, C, G, P) The project schedule was defined collaboratively after analyzing our performance at RoboCupJunior Rescue Line 2025 and identifying the main limitations observed during testing and competition runs. Our objective was to develop a reliable and competitive robot capable of autonomously completing all Rescue Line challenges while complying with competition rules, time constraints, and hardware limitations. The project plan followed a structured sequence: research and planning, component selection, mechanical design, manufacturing and assembly, electronics integration, software development, subsystem testing, and final integration. Each milestone was assigned to the team members responsible for the corresponding subsystem and included validation gates to review progress before advancing. Testing was performed after every major milestone under representative competition conditions. Mechanical systems were evaluated for durability and stability, electronics for sensor reliability, and software for line following, obstacle avoidance, victim detection, and rescue-room performance. Results from each iteration guided subsequent improvements. Based on lessons learned from RoboCupJunior 2025, particular emphasis was placed on improving navigation robustness and vision-based systems. Although most milestones were completed on schedule, additional development time was allocated to the computer-vision line-following system due to its complexity, resulting in minor adjustments to the original timeline. Regular meetings and continuous communication ensured coordination and successful integration of all subsystems throughout the season. b.​ Integration Plan The development of the robot followed a modular integration strategy in which each subsystem was initially tested independently before being incorporated into the complete robot. Mechanical assemblies, sensors, cameras, communication interfaces, navigation algorithms, and artificial intelligence models were first validated in dedicated testing environments to verify their functionality and performance. After successful validation, each subsystem was integrated into the robot and tested together with the remaining modules. Additional testing was then performed to evaluate communication reliability, synchronization, and overall system stability. This iterative process allowed potential issues to be identified and resolved before full system integration. The final architecture consists of an Arduino Mega responsible for low-level tasks, including motor control, sensor acquisition, and PID-based motion control , while a Raspberry Pi 5 performs computer vision processing, artificial intelligence inference, and high-level decision-making. Communication between both processors is achieved through a custom serial protocol, enabling fast and reliable data exchange between subsystems. The navigation camera, victim detection camera, sensors, rescue mechanism, and motor control system were integrated through this architecture to satisfy the requirements of the Rescue Line competition. Table 1 summarizes how each subsystem addresses the competition requirements, while Figure 1 illustrates the overall system architecture and communication flow between the main components. 4 Figure 1: Integration Plan 3.​ Hardware From the beginning of the project, we chose to design and manufacture the entire robot using CAD software and 3D printing, enabling rapid prototyping, efficient use of space, and easy implementation of design improvements. The robot was built around a dual-processor architecture consisting of an Arduino Mega, a Raspberry Pi 5 (8 GB), and a Hailo-8 AI accelerator, integrated with dedicated cameras, sensors, and a custom JMP.V1 power-distribution board. Component placement was carefully optimized to improve stability, maintenance, and weight distribution, with heavy elements positioned near the center of the robot. The upper section houses the rescue mechanism and victim storage system, while the internal structure accommodates the processing hardware and electronics. An additional innovation is the use of custom internally manufactured bicomponent polyurethane wheels (Shore 20), which provide improved surface adhesion and enhance the robot’s ability to overcome Rescue Line obstacles. Figure 2: JMP.V1 ​ Figure 3: Wheel 5 Figure 4: Hardware diagram of all installed components a.​ Mechanical Design and Manufacturing Chassis: Since the beginning of the project, we have designed and manufactured the entire robot structure using CAD software and 3D printing. SketchUp was used to develop and optimize all structural components, while PLA filament was selected due to its availability, ease of manufacturing, low weight, and sufficient mechanical strength for the competition environment. This approach allows rapid prototyping and enables mechanical improvements to be implemented quickly whenever weaknesses are identified during testing. The chassis was designed around the requirements defined in Table 1, particularly those related to size constraints, obstacle handling, ramps, bumpers, and elevated tiles. The robot is powered by four Pololu 100:1 Metal Gearmotors (20Dx44L) coupled to custom-made silicone wheels specifically developed to maximize traction and stability. Unlike conventional wheels, the wheels contain small cavities distributed along their surface, helping absorb impacts from bumpers while maintaining consistent contact with the ground on uneven terrain. Their soft silicone compound also provides high grip on ramps and seesaws, allowing reliable operation despite the robot's relatively high weight. Special attention was also given to the overall geometry of the chassis. Internal components were arranged to achieve a compact footprint while maintaining accessibility for maintenance and rapid component replacement. The robot follows a modular design philosophy. The lower section accommodates the drivetrain, batteries, custom power-distribution board (JMP.V1), and processing hardware, while the upper section houses the victim storage system, rescue mechanism, and display interface. This separation simplifies maintenance and allows individual subsystems to be removed or replaced without requiring major disassembly. The complete CAD model of the robot is shown in Figure 5. Figure 5: Robot Chassis design in CAD Rescue Mechanism: As identified during the project planning phase, one of the primary objectives was to minimize the time spent inside the evacuation zone while maintaining reliable victim collection. To achieve this, we adopted a strategy in which all victims are collected before any delivery operation is performed, significantly reducing unnecessary movement and improving overall efficiency. The rescue mechanism consists of a custom-designed, lightweight 3D-printed claw with two independently servo-actuated arms and a lifting mechanism that enables secure victim collection while avoiding collisions with walls and obstacles. After collection, victims are transferred to an onboard storage compartment, allowing multiple victims to be transported simultaneously without interrupting the search process. Several design iterations were evaluated, testing different claw geometries and pickup heights, resulting in a compact and reliable mechanism capable of consistently acquiring and transporting victims while satisfying the dimensional constraints of the competition. 6 Figure 6: Victim Rescue Gripper Arm b.​ Electronic Design and Manufacturing The robot’s electronic architecture is based on a heterogeneous dual-processor system consisting of an Arduino Mega and a Raspberry Pi 5 (8 GB) connected through a custom serial communication protocol. The Arduino handles low-level tasks such as motor control, sensor acquisition, gyroscope-based navigation, and rescue mechanism actuation, while the Raspberry Pi, accelerated by a Hailo-8 AI processor, performs computer vision, victim detection, line-following analysis, and high-level decision making. The robot uses two dedicated cameras, an IMX219 for line following and an IMX179 for victim detection, with high-intensity LED illumination to improve image quality. Additional sensors include a VL53L0X Time-of-Flight sensor, an MPU6050 gyroscope, and a dedicated infrared silver sensor for reliable evacuation-zone detection. Power is supplied by six 18650 lithium-ion cells divided into independent subsystems for the Raspberry Pi and the Arduino, improving stability under high loads. A custom JMP.V1 board centralizes power distribution, motor control, and communication, resulting in a reliable electronic system validated through extensive competition-like testing. 4.​ Software a.​ General software architecture The robot’s software is based on Arduino C++ and Python and is distributed between two processing units: an Arduino Mega and a Raspberry Pi 5 equipped with a Hailo-8 AI accelerator. The Arduino is responsible for low-level tasks, including sensor reading, motor control, PID-based movement correction, and navigation logic, while the Raspberry Pi performs computer vision and artificial intelligence processing using OpenCV and NumPy. The robot operates with two cameras simultaneously: one for real-time line following and track analysis, and another for victim detection and evacuation-zone analysis through a custom AI model accelerated by the Hailo-8. Information is exchanged between the Raspberry Pi and the Arduino through a custom byte-based serial protocol, enabling efficient real-time communication. The Raspberry Pi dynamically switches between navigation and rescue modules according to the current stage of the run, while a modular software architecture and a custom graphical interface support real-time debugging, monitoring, and reliable autonomous operation. 7 Figure 7: General software flow diagram Line following: The line following system is responsible for autonomous navigation throughout the Rescue Line field and operates at approximately 25–30 FPS. A wide-angle camera mounted 10 cm above the ground captures images that are illuminated by high-intensity LEDs to eliminate shadows and improve detection reliability under varying lighting conditions. The captured image is processed using adaptive thresholding and morphological filtering techniques to isolate the black line from the background. From the resulting line mask, four key reference points are extracted: the lowest, highest, leftmost, and rightmost points. These points provide a simplified representation of the track geometry and are used to determine the robot’s navigation direction. Under normal conditions, the robot follows the highest visible point of the line. At intersections, where multiple candidate paths may appear simultaneously, a projected reference point is calculated from the line geometry to identify the most likely continuation of the track, allowing the robot to maintain a smooth and reliable trajectory. Green markers are detected using HSV color segmentation and analyzed according to the surrounding black line configuration, enabling the robot to correctly identify left turns, right turns, and U-turns in accordance with the competition rules. The system also performs gap detection, obstacle handling, red strip identification, and other line-related challenges encountered throughout the course. Due to the camera being positioned significantly ahead of the robot’s center of rotation, a bi-vectorial movement system was developed to improve navigation accuracy by combining information from both near and far sections of the line. This approach reduces premature steering corrections and improves performance in tight corners and complex track layouts. The bi-vectorial navigation strategy is further detailed in Section B. The final steering angle generated by the vision system is sent to the controller process, where a PID algorithm calculates the motor corrections required to keep the robot accurately aligned with the line while maintaining high travel speeds. Figure 8: Line following flow diagram 8 Evacuation zone: The evacuation-zone system is activated immediately after the dedicated silver sensor confirms the entrance to the rescue area. At this stage, the robot switches from line-following mode to a vision-based rescue mode, using the victim-detection camera and AI model to continuously search for victims and evacuation points. Once a victim is detected, the robot approaches it, performs the collection procedure, and stores it in the onboard storage compartment. We adopted a strategy in which all victims are collected before any delivery operation is performed. Although this approach increases the number of victims carried simultaneously, it significantly reduces unnecessary movement inside the evacuation zone and minimizes the total rescue time. Victim detection is performed using a neural-network model accelerated by the Hailo-8 AI processor. Compared to traditional image-processing methods, the AI-based approach proved considerably more robust to changes in lighting conditions, victim orientation, partial occlusions, and variations in camera perspective. The complete rescue-room strategy and decision-making process are illustrated in Figure 9. Figure 9: Evacuation zone flow diagram b.​ Innovative solutions Bi-vectorial movement system: due to hardware constraints, the line camera had to be positioned significantly ahead of the robot's center of rotation, introducing a substantial challenge in path tracking. Since the wide-angle camera captures a large portion of the field, it detects line features and trajectory changes long before they become relevant to the robot's actual position, causing the control system to react prematurely to distant path segments. This behavior was particularly problematic in constrained environments, where early steering corrections could lead the robot to initiate turns before reaching the appropriate location, increasing the risk of misalignment, collision with obstacles, or failure to complete the maneuver. To mitigate this issue, a bi-vectorial movement system was developed, in which the camera frame is divided into two regions that independently generate a near vector and a far vector, representing the local and future line directions, respectively. The navigation algorithm prioritizes the near vector to ensure that control actions are primarily based on line information immediately relevant to the robot's position, while the far vector is incorporated as a secondary reference to provide trajectory anticipation. This architecture effectively compensates for the forward displacement of the camera, reducing premature steering responses and significantly improving navigation stability, cornering performance, and maneuverability in environments with limited available space, as illustrated in Figure 10. Figure 10: Bi-vectorial movement system 9 Stuck detection due to the robot’s mechanical design, which prioritizes a low center of gravity through the use of relatively small wheels and a chassis positioned close to the ground, the platform is inherently more susceptible to becoming lodged on speed bumps, ramps, floor transitions, and other elevated field elements. To mitigate this limitation, a dedicated stuck detection and recovery system was developed based on the combined analysis of line-tracking behavior and wheel encoder feedback. The algorithm continuously monitors the temporal variation of the detected line position together with the number of encoder pulses generated within a predefined interval, allowing it to identify situations in which the robot is attempting to move but exhibits insufficient effective displacement. By correlating these independent sources of information, the system can reliably distinguish between normal navigation conditions and genuine immobilization events. Once a stuck condition is detected, the recovery module automatically applies corrective actions, ranging from temporary motor power increases to predefined unjamming maneuvers designed to overcome the obstacle and restore normal operation. This approach substantially increases the robot’s resilience to adverse terrain conditions, minimizing the likelihood of prolonged immobilization and ensuring more reliable navigation performance throughout the course, as illustrated in Figure X. 5.​ Performance evaluation Throughout the development of the robot, extensive testing was conducted to evaluate its performance in the main RoboCupJunior Rescue Line challenges, including line following, intersections, gaps, obstacles, victim detection, and evacuation-zone operation. The testing process followed a modular approach, with individual subsystems evaluated independently before being integrated into complete competition scenarios. To support development, we created dedicated tools for camera calibration, image-processing visualization, communication monitoring, motor-control verification, and AI evaluation, along with a custom graphical interface for real-time debugging and data visualization. Whenever performance issues were identified, the corresponding subsystem was analyzed and refined before being retested. For the AI system, new images from failure cases were incorporated into the training dataset, allowing iterative retraining and validation. This continuous cycle of testing and improvement led to significant enhancements in navigation, mechanical design, and artificial intelligence, increasing the robot’s robustness and competition readiness. Figure 11: Victim Detection GUI 6.​ Conclusion After more than six years of continuous development, redesign, and testing, our team believes that we have achieved most of our goals, especially during this season. We focused on improving the robot’s mechanical structure, electronic integration, software architecture, and artificial intelligence systems, transforming weaknesses identified in previous competitions into significant gains in reliability, precision, and overall performance. The team also evolved in organization and collaboration, enabling faster development cycles and more efficient problem solving through continuous testing and iterative redesign. We believe this project reflects not only our technical growth in robotics but also our ability to work effectively under real competition constraints. We look forward to participating in RoboCupJunior, where we hope to further improve our technologies and exchange knowledge and experiences with teams from around the world. 10 Appendix If you want to learn more about our robot, feel free to visit these sites: ●​ Our YouTube channel ●​ Our Instagram account References a. Software Tools & Platforms ●​ Arduino IDE ●​ Github ●​ Google Colab ●​ Raspberry Pi OS ●​ Roboflow ●​ Sketch Up ●​ Python b. Libraries & Frameworks ●​ OpenCV ●​ NumPy ●​ Ultralytics ●​ PySerial ●​ Tkinter ●​ Picamera2 c. Hardware ●​ Arduino Mega 2560 ●​ Raspberry Pi 5 ●​ Hailo-8 AI Accelerator ●​ Raspberry Pi Camera Module 3 ●​ Pololu 25D Metal Gearmotors ●​ VL53L0X Time-of-Flight Sensor ●​ HC-SR04 Ultrasonic Sensor 11