CAP.MARCO MORETTI LORIS BERRA SANDRA D’ARRIGO FEDERICO BARBAN SOFTWARE ENGINEER ELETTRONIC ARDUINO ENGINEER DESIGNER 3D TEAM Our team brings together students from electronics and computer science, each contributing specialized skills that strengthen the project. Loris Berra, an electronics student with three years of RoboCup experience across multiple categories, has worked on industrial projects at STMicroelectronics and Pomini, and developed sensor‑based safety systems using ultrasound and YOLO‑based AI. He handled the electronic integration and PCB development. Marco Moretti, computer science student and Lead Software Engineer, specializes in Python, AI, mapping, pathfinding, and computer vision. He competed in the 2025 Italian Rescue Maze Championship, the 2025 OnStage Advanced World Cup - earning recognition for best technical documentation - and the 2026 Italian Rescue Maze Championship, finishing 5th. Sandra D’Arrigo, computer science student and Arduino specialist, competed in the 2025 and 2026 Italian Rescue Maze Championships and contributed to the visual design and presentation materials for the 2025 World Cup. Federico Barban, computer science student and mechanical/3D modeling specialist, has been active in 3D design since age 12 and created all robot components from scratch. This is his first robotics competition. The strength of our team lies in the diversity of our skills - electronics, software, mechanics, and design. Each member brings a different perspective, and this variety allows us to solve problems more effectively, learn from one another, and continuously improve. Collaboration is essential: by combining our individual strengths, we transform complex challenges into achievable goals and build a more complete, innovative robot. COMPONENTS HARDWARE INNOVATIONS 6 1. 1× Raspberry Pi 5 (8GB): Handles high-level processing 13 3 3 2. 4× Motors: JGY‑370 DC 12V 100 RPM: Precise movement with 7 encoders 12 3. 2× OpenMV Cam H7 Plus: For target & possible letter 7 7 recognition 1 1 The robot uses 8 cm wheels equipped with a toothed TPU tread that ensures 4. 1× Raspberry Pi Active Cooler: Cool down Raspy excellent traction on ramps, obstacles, and uneven terrain. The tires are made 5. 1× 9G Micro Servo Motor: Handles kits deployment 2 entirely of TPU, a flexible and durable material chosen for its superior grip and 6. 1× TCS34725FN RGB Color Sensor: Tile type recognition 2 4 ability to maintain consistent contact with the ground. This combination improves 14 7. 6× VL6180X ToF Sensors: Mesure distance from walls stability during movement, enhances obstacle‑handling performance, and 8. 1× BNO055 Gyroscope: For precise rotations & straight contributes to smoother, more reliable locomotion across irregular surfaces. movements 8 18 9. 1× Arduino Mega Dollatek PRO ATmega USB CH340G: 9 7 Manages low-level hardware operations 10. 1× TB6612FNG Motor Driver Board: Handle motors 15 10 2 7 7 11. 1× PCB: Connect components while reducing cables 17 12. 1× XHC‑N 84 Power Bank: Charge the Raspy 2 13. 1× MOD‑HW‑201 Infrared Sensor: Identify checkpoints 11 Each motor is mounted on a dedicated suspension system that absorbs terrain 14. 1× HALJIA Red Push Button: Trigger a LoP irregularities, reduces vibrations, and maintains consistent wheel‑to‑ground 15. 2× LED Strips: Illuminates the walls for better recognition 7 contact. The system helps the robot keep traction even when crossing uneven 16. 1x LED Strip: Report victims to the referee 16 surfaces or small obstacles. The lubricated rods further minimize friction and 17. 1× Mini Toggle Switch: Cut the power 5 mechanical oscillations, improving smoothness during acceleration and rotation. 18. 1× Sport Power 1600 mAh 11.1 V 120C Battery: Powers the This setup increases overall stability, protects the mechanical components from motors & the LED strips stress, and ensures more reliable movement in variable terrain conditions. SOFTWARE HARDWARE MANAGING (ARDUINO) TARGET RECOGNITION (PYTHON) COMMAND W Leveraging a concurrent, multi-threaded software framework, Python and MicroPython form the core In our architecture, the Arduino Mega manages low-level hardware operations in real time, controlling the cognitive backbone of our robot's high-level intelligence, seamlessly orchestrating real-time maze mapping, motors and polling sensors to guarantee immediate responsiveness. The only exceptions are the color and dynamic pathfinding, and advanced computer vision. infrared reflection sensors, which are routed directly to the Raspberry Pi 5 to feed its high-level processing To achieve real-time execution, our vision pipeline was consist of a distributed, two-tiered framework. and strategic logic. Visual target localization is offloaded to an OpenMV H7 Plus edge camera running MicroPython. It isolates To ensure precise maneuverability, a full PID controller executes stable rotations by comparing target regions of interest using wide-spectrum color blobbing followed by a localized Hough Circle Transform. angles with real-time feedback, while a wall-alignment routine dynamically calculates the robot's heading error The detected circle is split into five concentric rings analyzed via a LAB color space area-voting by applying the atan2 function to the differential readings of two parallel side-facing ToF sensors. finally a mechanism. By algebraically summing the numerical weights of the dominant colors, the system dedicated speed-ramping module smoothly governs acceleration and braking to prevent wheel slippage and automatically identifies victim types and filters out fake targets. If a non-circular shape or character is found, minimize mechanical stress. the cropped frame is streamed to the Raspberry Pi 5 for letter classification. This integrated Master-Slave paradigm effectively balances deterministic hardware control with high computational power , ensuring highly reliable autonomous navigation even under complex arena conditions. KNN (PYTHON) MAPPING (PYTHON) Once a visual region of interest (ROI) is isolated by the OpenMV camera, the cropped frame is instantly streamed to the Raspberry Pi 5 via a dedicated communication interface. A specialized Python-based K- The robot builds a map of the environment as it moves, updating it in real time using sensor and camera data. Nearest Neighbors (KNN) pipeline then executes high-speed, rotation-invariant classification on the The area is divided into a grid of cells, where each cell stores information such as explored zones, tile types, detected blob. By offloading the initial shape detection to the edge co-processor, the Raspberry Pi's CPU and walls. Python handles logging this data and updating the map with every forward step, marking features overhead is minimized, dedicating its multi-threaded architecture strictly to classification and pathfinding. This encountered along the way. This enables the robot to avoid previously visited areas, choose more efficient distributed approach ensures deterministic real-time processing, allowing the master system to instantly routes using Dijkstra algorithm, and plan the optimal path to the goal. Ultimately, this mapping system is an update the maze navigation matrix or execute corresponding victim-handling routines without continuosly essential tool for autonomous navigation, allowing the robot to orient itself and make real-time decisions halting the robot's exploration. based on exploration data. Modello 3D PCB Github