Wall and geometry mapping TEAM About us: ÁGUABOTS We are the Brazil team Águabots, representing the Farias Brito educational institution. The name INCHEON- ROBOCUP 2026 - RESCUE SIMULATION Águabots was chosen to symbolize the fluidity, adaptability, and intelligence of our robots, just like the importance of water for life and our mission to rescue victims in disaster situations, together with the BRAZIL following members: Isaac Newton S. Araújo Manuella A. N. Magalhães Responsible for AI and Software Responsible for mapping Archictecture NAVIGATION AND EXPLORATION Isabella F. G. P. Araujo Vinícius F.S. Oliveira Custom tile-based exploration combining semantic mapping, LiDAR Responsible for the design and Responsible for navigation and Exploration path planning movement clearance analysis, backtracking and failed-goal memory. Goal Selection Selects the next open unvisited neighbor tile or initiates backtracking when no new tile is reachable. Awards: 1st place in the Brazilian Robotics Competition (CBR) Planning DFS-style planning with weighted backtracking through visited non- Source: Authors, 2026 Rescue Simulation 2025. blocked tiles. Motion Control Converts tile movements into calibrated Webots wheel-position targets. VICTIM RECOGNITION Safety & Cancels unsafe movements, reverses partial motion, marks blocked HARDWARE Recovery tiles, and ensures return-to-start behavior. Image Processing Frame filtering Create a bitmask Duplicate detection removal Use LIDAR to for the tile detect walls Object Detection NO Lightweight ONNX CNN Depth-First Has the Are there current tile any Breadth-First Is there an Detects: Target and victim Search YES NO unvisited NO Start already been unvisited Search (BFS) End path (DFS) visited? neighbors? available? Target Recognition Source: Authors, 2026 Source: Authors, 2026 ROBOT COMPONENTS ROBOT IN SIMULATION CNN + Computer Vision Move the Circle detection Wheels: Two side-mounted wheels are responsible for the robot’s movement, allowing it to robot Polar unwrapping move forward, make smooth turns, and rotate on its own axis. HSV color decoding Update the tile YES Inertial Unit: Used to ensure that the robot makes turns with exact 90º precision. Flowchart: Authors, 2026 bitmask Distance validation Color Sensor: Used to identify the color of the simulated arena’s floor. It allows the detection of holes that cannot be crossed. Victim Recognition GPS: Used to record and transmit the precise location of victims detected by the robot. It is CNN Pipeline also used to assist in mapping. LiDAR: Used for accurate wall detection in the environment. The data provided by the LiDAR is MAPPING Bounding box detection Image classification essential to avoid collisions and to build representations of the traversed space. Fake victim rejection The mapping system represents the Rescue Simulation maze as a semantic tile-based map, since the arena is Cameras: Two cameras are positioned on the sides of the robot, assisting in precise victim organized as a grid of square tiles Training & Deployment detection through computer vision. Both cameras have a resolution of 64x64. The mapper receives standardized ROS 2 data, mainly filtered odometry and LiDAR scans. Odometry provides the Distance Sensor: Used to detect holes. It compensates for the color sensor’s limitation of robot’s position, while LiDAR detects nearby walls and obstacles. Ground sensors classify each tile as normal, colored, analyzing only one point, making hole detection more reliable. swamp, hole. The mapper converts sensor observations into a semantic tile representation SYSTEM ARCHITECTURE of the maze. WEBOTS CORE ROS 2 DECISION MODULES EKF TILE LIDAR LOCALIZATION EXPLORATION TCP ROS GPU + IMU BRIDGE SLAM PATH TOOLBOX PLANNING CÂMERA CAMERA SEMANTIC TILE MOTION MAPPER EXECUTOR MOTOR CÂMERA VICTIM MOTOR COMMANDS DETECTATION COMMANDS Flowchart: Authors, 2026 Flowchart: Authors, 2026 Source: Authors, 2026