Hardware Multi-Processor Architecture The robot uses a Teensy 4.0 as the main controller, a Raspberry Pi 5 for image processing and AI inference, and two XIAO RP2040 boards as sub-controllers. The Raspberry Pi processes camera data, while the RP2040 boards handle dedicated sensor and actuator tasks. This distributed architecture enables high-speed control, accurate vision-based recognition, and reliable parallel processing. Powerful Power Train The robot is driven by four STS3032 serial servo motors. These compact yet powerful motors enable the robot to overcome bumps reliably. The wheels feature a large diameter, narrow width, League : Rescue Line and custom-molded silicone tires. This design Country : Japan provides high grip on bumps and ramps, improving traction and driving performance. Multifunctional OUR TEAM Twisting Mechanism Rescue Mechanism Our team, TOK AI Ghost, consists of students The front and rear halves of the robot are Our rescue mechanism can collect, identif y, from the s ame high school . We began connected by linear shafts, allowing them to classify, store, and evacuate victims within a participating in RoboCup Junior Rescue Line last twist relative to each other. This mechanism compac t system. B y stor ing all vic tims season. Building on the experience we gained at keeps all wheels in contact with the floor on simultaneously, the robot reduces travel time and RoboCup 2025 S alvador, we developed a uneven terrain, improving driving performance improves rescue efficiency. The rescue arm uses powerful, compact, and reliable robot. on bumps and ramps. It also maintains a stable sensors to detect obstacles, confirm victim distance between the line sensors and the floor, capture, and identif y victim conductivity. A TAKUMI TAKANO enabling smooth and reliable line following even at transitions between flat tiles and ramps. one-sided gripper and tilting gate mechanism enable victim sorting and selective evacuation. Overview Captain, Hardware and Software Developer We designed the entire robot Takumi Takano designed all the electronic components and most structure using Autodesk Fusion and of the robot’s structure, and he manages the development manufactured most components using 3D printing to maximize Rotating Camera The camera is mounted on a servo-driven mechanism and is primarily used for victim detection. In gap situations, the camera space efficiency. The robot measures 163 mm × 174 mm. An schedule. He also coded the algorithms for line following and rescue. Additionally, he coordinates the opinions of team Mechanism rotates downward and is used for camera-assisted gap control. aluminum chassis provides durability and lowers the center of members. gravity, while lightweight 3D-printed components are used in the upper half of the robot. All custom circuit boards were designed HONGYI DAI Software using KiCad. The robot software was developed using the Arduino framework, MicroPython, OpenCV, and Ultralytics. Software Developer Hongyi Dai coded the algorithms for detecting evacuation points. He also developed a camera-based assist system for Line Following Evacuation Zone line following. Furthermore, he is responsible for setting up a Raspberry Pi 5 environment and configuring the camera. The robot calculates the line position from line sensor data and follows the line An overview of the evacuation zone process is shown in the figure below. Throughout using PD control. When it detects an intersection with the color sensor, it the rescue operation, the robot utilizes its multifunctional rescue mechanism and SHINICHI KADO determines the appropriate turn based on the elapsed time since the last black distance sensors to handle challenging environments with many obstacles. Because Software Developer line was detected. On ramps, it applies slope-specific compensation to the robot collects victims regardless of their type, it can perform rescue operations Shinichi Kado joined the team this season. He developed a optimize line-following performance for each incline. When no line is detected, efficiently without prioritizing specific victims. The number of rescued victims is stored machine learning model for victim detection. He also developed it first moves backward and determines whether the situation is a gap or a line on the Raspberry Pi throughout a run. After a LoP, the robot refers to this information, following error, significantly reducing the risk of LoP. When encountering an allowing it to perform optimally even on subsequent attempts. The algorithm is Victim Detection efficient methods for capturing images and augmenting data for the dataset. Thanks to his work in introducing new technology, obstacle, it uses distance and touch sensors to navigate around it while designed to handle various situations, such as failing to collect all victims or being We previously used an OpenCV-based algorithm for victim detection. However, the robot can reliably detect victims. maintaining the shortest possible clearance. unable to locate a corner. distinguishing victims from the background was difficult, so we adopted YOLOv8, a deep learning-based object detection model. Using transfer learning based on the pretrained YOLOv8n model, the robot can detect both the position and type of victims from camera images. RoboCup Junior We collected and processed our own dataset of approximately 3,000 images and manually annotated all victims. To improve robustness, the dataset JapanOpen 2026 included dummy balls, unlabeled images, and Blender-generated images. ・1st Place ・Software Award ・IRS Award Additional datasets containing flashlight interference were also created to address the new competition rules. RoboCup 2025 Salvador ・Rescue Line 5th Place Innovation Although the victim detection model was trained using images containing ・Outstanding Design Award flashlight interference, false detections still occurred under certain light Camera-Assisted Gap Control intensities and angles. FLL FIRST 2025 Championship To address this issue, we installed a polarizing film in front of the camera lens. Line sensors provide stable and low-noise line following. However, because This modification significantly reduced strong reflections from the floor and ・Engineering Excellence Award Winner they can only detect the presence of a line, they cannot determine the improved detection reliability under challenging lighting conditions. ・Encore Celebration Alliance FInalist direction of the line while traversing a gap. To address this limitation, the robot uses a camera during gap traversal. When a gap is detected, a servo motor rotates the camera downward, and the line angle is calculated using OpenCV's PCACompute function. The robot then Corner Detection Supported by uses this angle to align itself with the recovery line. The robot detec t s the evacuation point using By combining sensor-based line following with camera-based directional OpenCV-based image processing. Red and green analysis, the robot can navigate challenging gaps more reliably. regions are extracted from the camera image, and connected-component labeling is used to remove Hi! noise. The evacuation point is then identified based on X note YouTube the aspect ratio of the detected region. To improve robustness when obstacles partially occlude the evacuation point, the robot evaluates the two largest labeled regions instead of only the largest one.