IITA IITA TEAM SONG Rescue-BOT Rescue-BOT Teensy 4.1 SOFTWARE The motor control system uses encoder R ob o Cup Ju n i o r R e s cu e L i n e 2 0 26 - S a lt a , A r g e nt i n a The Teensy 4.1 is responsible for all real-time feedback and PID algorithms to regulate LINE robot control tasks. Developed in C++ using PlatformIO, the firmware manages line speed and improve movement accuracy, enabling more consistent navigation during following, sensor acquisition, motor line following and rescue tasks. FOLLOWING Yosoo Health Gear 2 Million Pixels 140° Camera Computer vision and autonomous navigation. The control, and rescue-zone operations. Raspberry Pi 4 wide-angle camera captures real-time visual The control system continuously processes HW-803 5V Relay Module: information used for line tracking, green marker The Raspberry Pi 4 is responsible for all high- Auxiliary system power control. information from the BNO055 IMU, VL53L0X level computer vision tasks. Using Python, detection, victim identification, and rescue zone APDS-9960: This relay module allows the robot to time-of-flight sensors, ultrasonic sensors, navigation. OpenCV, and a custom-trained YOLOv8 Color detection and safely switch external devices Its large field of view enables the robot to perceive more of its color sensors, wheel encoders, and limit model, it processes images captured by the verification system. The independently from the main APDS-9960 sensor is used surroundings, supporting accurate decision-making and autonomous switches. This data is used to maintain stable onboard camera to detect victims, controller. It provides electrical to identify different movement throughout the competition. navigation, detect obstacles and walls, isolation and reliable control of evacuation zones, special line markers, and colors present on the auxiliary systems used during rescue identify ramps, estimate traveled distance, competition field and other relevant objects on the field. To improve and evacuation tasks, improving and execute precise maneuvers throughout robustness, the model was trained with more inside the rescue area. operational safety and power Limit Switches(x2): the competition field. than 6,000 images under different lighting It provides an additional layer of verification for the management. Precise victim deposition positioning. These To improve software organization, conditions and backgrounds, allowing TEAM camera-based detection system, improving reliability and Additionally, it enables secure power distribution to mechanical sensors confirm physical contact reducing the possibility of false identifications during peripheral devices, preventing interference with the robot's custom libraries were developed reliable operation in varying with the evacuation zone, autonomous operation. for both the drive system and the primary control electronics and enhancing overall system ensuring the robot is competition environments. reliability. correctly aligned before victim collection mechanism. Once a target is detected, the Raspberry Pi releasing collected This modular architecture simplifies testing, calculates its position relative to the camera Rescue-Bot Team was founded in 2023 by a group of students who shared a passion for robotics. victims. debugging, and future development while frame. During victim collection, the robot Our first robot was a simple line follower with HC-SR04 (x3): allowing different subsystems to operate limited mechanical and electronic systems. Distance measurement and obstacle continuously adjusts its movement until the We first competed at RoboLiga 2023, where the independently. detected victim is aligned with the center of detection. These ultrasonic sensors continuously experience motivated us to continue improving. Since then, we have focused monitor nearby walls and obstacles, providing VL53L0X (x2): the image, increasing collection accuracy on understanding every environmental awareness that helps the robot High-precision wall distance sensing. and reducing positioning errors. The aspect of our robot, including CHAMPIONS navigate safely, avoid collisions, and make autonomous decisions during These Time-of-Flight sensors measure Raspberry Pi then sends navigation and rescue Teensy and Raspberry Pi ROBOLIGA line following and rescue tasks. lateral distances from walls and obstacles, commands to the Teensy 4.1 through serial ARGENT INA boards, motor encoders, helping the robot sensors, and control communication. 2025 maintain accurate systems. positioning and This technical foundation orientation within allowed us to continuously Teensy 4.1: Communication System improve our design. Over the years, the rescue area. Main robot controller. It processes sensor data, executes control Both processing units communicate through a we developed custom 3D-printed algorithms, manages communication with the Raspberry Pi, custom UART protocol operating at 115200 parts, improved navigation, and created a . and controls motors, servos, and other rescue gripper mechanism. We also introduced a baud. This architecture divides the camera-based vision system powered by artificial electronic components, ensuring reliable intelligence to enhance victim detection. real-time operation. computational workload between the two Our team played an important role in introducing fully custom electronic robots at our BNO055: boards: the Raspberry Pi performs . institute, moving beyond LEGO-based platforms and developing robots entirely from Orientation and motion tracking. This intelligent IMU scratch. In 2024, we achieved second place at the National Tournament and later intensive won the Innovation Award for learning, development, and teamwork, qualifying for combines accelerometer, the RoboCup World Championship 2026. Today, our team consists of three members: gyroscope, and image- Brushless Motors DC (×4): Benjamin, responsible for electronics integration; Laureano, responsible for 3D magnetometer data to processing design and Teensy programming; and Lucio, responsible for Raspberry Pi and vision Robot locomotion system. These motors provide the power, speed, and precision provide accurate heading tasks, while the system development. Our goal is to continue learning, improving our technology, and sharing experiences with teams from around the world through RoboCup. required for movement. . Their efficiency and information, allowing stable navigation, incline Teensy handles reliability allow smooth compensation, and precise turns. low-latency Laureano Monteros I am 16 years old and have been studying navigation and control robotics since 2019. I started with LEGO robotics accurate maneuvering operations. and participated in internal competitions, where I throughout the 11,1 V CNHL LiPo Battery: developed teamwork, problem-solving, and competition. perseverance skills. Primary power source. The In 2023, I transitioned from block programming battery supplies stable energy to The Raspberry Pi transmits steering commands, speed references, to text-based programming, learning Python and motors, controllers, sensors, and victim-detection results, and rescue-state information. The Teensy later C++. actuators, ensuring consistent interprets these commands, executes the corresponding actions, performance and sufficient Through robotics challenges, I became particularly interested in low-level control and autonomous systems.Today, I am one of the team's main programmers, Artificial Intelligence Mode autonomy throughout the monitors sensors, and controls actuators. In return, it sends status focusing on autonomous navigation, sensor integration, robot control, and To improve victim detection reliability, our team developed a custom artificial intelligence model competition. updates that allow the Raspberry Pi to coordinate transitions between software development for Rescue Line competitions. based on YOLOv8. The model was trained using 6,256 labeled images and 9,521 annotations Servo motor 300°(x5): stages such as line following, victim collection, and evacuation. collected under different lighting conditions, camera angles, backgrounds, and wall colors. Victim manipulation and sorting. The Lucio Saucedo To address the new RoboCupJunior 2026 LED-wall challenge, the vision pipeline servos operate the claw, lifting I am 15 years old and have been involved in incorporates an Anti-Flash + AGCWD (Adaptive Gamma Correction with mechanism, sorting system, and robotics since I was 9, starting with LEGO Weighting Distribution) preprocessing stage. Anti-Flash reduces glare and deposition structure, enabling precise Robotics and learning the fundamentals of overexposed regions caused by strong LED reflections, while AGCWD victim collection, classification, RESCUE AND EVACUATION ZONE programming, robotics, and teamwork. automatically enhances image contrast according to the illumination Since 2023, I have focused on text-based distribution of each frame. This improves detection robustness under and release during rescue challenging lighting conditions without requiring manual recalibration. operations. Raspberry Pi 4: programming and autonomous systems, improving my programming and problem-solving Computer vision and image processing. The Raspberry Pi detects This allows the robot to detect victims reliably even when environmental conditions differ from skills through robotics projects and competitions. those used during development. The model runs directly on the Raspberry Pi 4 and currently lines, intersections, green markers, rescue victims, and other field achieves approximately 18.10 FPS, a significant improvement over previous versions that elements, generating navigation data that is sent to the Teensy for Within the team, I am responsible for Raspberry Pi development, sensor operated at 7.14 FPS, enabling real-time victim detection during competition. autonomous decision-making. integration, communication systems, and software that enables autonomous robot Once a victim is detected, the Raspberry Pi calculates its centroid and continuously adjusts the operation during competitions. robot's position until the target is centered in the camera frame. After alignment, the Raspberry Benjamin Villagrán Pi sends commands to the Teensy 4.1 through a custom UART communication protocol, which executes the complete collection sequence. Innovative Solution: Custom PCB Design I am 18 years old and started studying robotics The system can distinguish between black and silver victims, enabling automatic classification at Instituto de Innovación y Tecnología Aplicada and storage in separate compartments. This reduces evacuation time and improves overall when I was 15. I initially worked on the robot’s One of the main electronic innovations of our rescue efficiency while maintaining reliable performance under extreme lighting conditions. electronics, learning about sensors, power robot is a custom-designed PCB that serves as systems, and control boards. the central hub for power distribution, signal As I gained experience, I expanded into software routing, and subsystem integration. The board development, working with Raspberry Pi, connects all major components, including the Python, computer vision, serial communication, Teensy 4.1, Raspberry Pi, motor drivers, servos, and artificial intelligence for victim detection. BNO055 IMU, APDS9960 color sensor, VL53L0X Today, I contribute to both hardware and software. My main responsibilities distance sensors, ultrasonic sensors, indicators, include AI and computer vision development, Raspberry Pi programming, relay, and control switches. electronics integration, 3D design with Fusion 360, and system documentation. 6256 images To maximize system stability, the electronics were designed around separated power HARDWARE domains. High-current actuators are powered through a dedicated 6.1 V rail, Innovative Solution: 5-Servo Rescue Module while the computing and sensing systems operate on an independent regulated 5.0 V rail. Our robot features a fully custom chassis designed by our team, composed of 3D-printed parts and a custom PCB that allows us to Collection of victims This reduces noise, prevents voltage fluctuations, and improves organize electronic components efficiently and simplify maintenance. overall system robustness during demanding rescue tasks. After a victim is detected by the It measures 157 mm long by 177 mm wide by 176 mm high and The PCB significantly reduces wiring complexity, improves TFLite-based vision system, the weighs 1,404 grams. serviceability, and allows rapid diagnostics and repairs during robot continuously adjusts its For locomotion, we use front traction wheels that provide grip on the competitions. Compared to conventional wiring solutions, this trajectory until the object's centroid field and rear omnidirectional wheels that improve maneuverability approach provides a more compact, reliable, and scalable electronics is aligned with the center of the during turns. The drive motors are equipped with encoders, allowing architecture that supports the robot's advanced sensing, control, and camera image. This visual tracking us to accurately measure the distance traveled and perform more artificial intelligence systems. process minimizes positioning The rescue mechanism automatically classifies collected victims precise movements. errors and allows the robot to according to their detected type (black or silver). After pickup, a The robot's electronic architecture is based on a Teensy 4.1 and a approach the target with high dedicated sorting mechanism routes each victim into the corresponding Raspberry Pi 4. The Teensy handles real-time motor and sensor accuracy. storage compartment, allowing the robot to prepare for rapid deposition control, while the Raspberry Pi performs more advanced proce ssing tasks. Once the victim is centered, the Raspberry Pi sends collection commands inside the evacuation zone. Our sensor system enables the robot to detect walls, obstacles, and ramps throughout the course. The to the Teensy 4.1 through a custom UART protocol. The Teensy then This design reduces the number of actions required during the final rescue stage and improves overall mission efficiency. Compared with earlier gyroscope, BNO055, provides orientation data for accurate turns and ramp navigation. In addition, rear limit executes the complete pickup sequence, coordinating the servo-driven versions that relied on a simple containment system, the current mechanism integrates automatic detection, collection, classification, and storage into a switches detect contact with the rescue zone walls, improving stability during rescue maneuvers. gripper and lift mechanism to securely capture the victim and transfer it streamlined and more reliable rescue process. Over the years, our hardware has continuously evolved through mechanical, electronic, and structural to the storage system. improvements, increasing the robot’s precision, reliability, and overall performance.