Rescue Rats

RESCUE RATS - SLOVAKIA
                       RoboCupJunior - Rescue Maze 2026
Movement System                                                     Victim Recognition
Motors and Motor Bus                                               Letter Victim Recognition                                                  Cognitive Targets Recognition
                                                                   Using two separates cameras on each side and OpenCV on                     For cognitive targets, the robot samples the average color
JGY-370 Motors: The robot is driven                                the Raspberry Pi 5, the vision pipeline adjusts image                      values of 5 concentric rings around the largest detected
by four 12V JGY-370 DC motors                                      contrast and saturation before thresholding. It utilizes                   shape contour. Each ring is scored based on its BGR signature.
equipped with built-in quadrature                                  OpenCV’s matchShapes() function to compare runtime contours                If all 5 rings match the target criteria, their values are
encoders for distance counting.                                    against pre-made 64x64 .npy reference arrays of Greek letters              compiled into a validated victim score.
                                                                                                         (Ω- Omega, Φ- Phi, and Ψ- Psi).
Motor Bus: To handle the high-current                                                                                                         Reliability Filter:
demands of the power subsystem without                                                                                                        To eliminate false positives
interfering with sensitive logic, the motors are wired into                                                                                   from single-frame noise,
a power bus supplied by a 14.7V LiPo battery. The ESP32                                                                                       the camera processes 5
regulates this power via MDD10A Cytron motor drivers, running                                                                                 sequential frames per wall.
them at a lower PWM (60-80/255) to prevent overheating.                                                                                       The final victim classification
                                                                                                                                              is decided by majority vote
Wheel Design and Turning                                                                                                                      across those 5 readings.

Movement relies on specialized wheels featuring integrated
outer rollers. Because these rollers rotate perpendicular to the
direction of travel, they reduce the friction and force required
to spin in place. Backed by real-time gyroscope tracking, this
setup allows the robot to execute 90°and 180° grid turns while
maintaining a straight line during forward travel.

                      Prior Solution: Our initial build utilized
                      standard Arduino Kit Wheels. These
                      suffered from turning resistance, causing
                      wheel slippage, odometry drift, and                                                                                             Maze Mapping
                      frequent wall collisions during sharp grid
                      maneuvers.                                                                                                                      The mapping engine treats the maze as
                                                                                                                                                      an expanding coordinate dictionary. Each
                                                                                                                                                      coordinate entry tracks cardinal wall states, tile
                                                                                                                                                      color, and victim registration to prevent accidental
                                                                                                                                                      double-drops of rescue kits.

                                                                                                                                                      The Weighted Dijkstra Exploration Algorithm

                                                                                                                                                      Instead of standard flood-fill, our navigation relies
                                                                                                                                                      on a weighted version of Dijkstra’s Algorithm that
                                                                                                                                                      dynamically recalculates paths at every exploration

Obstacle Detection
                                                                                                                                                      cycle iteration based on live map data:

                                                                                                                                                      Unvisited Tiles: Cost = 1 (Highly Preferred)
Wall Detection                                                                                                                                        Visited / Silver Tiles: Cost = 2
                                                                                                                                                      Incline Tiles (Ramps): Cost = 3
The robot features 8x VL53L1X                                                                                                                         Red Penalty Tiles: Cost = 10
Time-of-Flight (ToF) laser distance                                                                                                                   Blue Penalty Tiles: Cost = 50
sensors placed one on each corner of the sidewalls. These                                                                                             Black Tiles (Pits): Cost = (Strictly Unpassable)
sensors allow the robot to maintain a safe distance from
walls and calculate its alignment. Because all 8 sensors                                                                                              This cost matrix forces the robot to prioritize
share the same default I2C address, we utilize the hardware                                                                                           efficient, unvisited routes and avoiding high-penalty
XSHUT pins at boot, to dynamically overwrite and reassign                                                                                             terrain or dead ends.
unique addresses.
                                                                                                                                                                                      Output format:
Color of Tiles Detection                                           Microcontrollers                                                                                                   {(x_value, y_value):
                                                                                                                                                                                          {'Walls':{'direction':True/False
                                                                                                                                                                                          ..., ..., ...}
A TCS34725 RGB color sensor is mounted to the underside            Our architecture relies on a clean                                                                                     'tile_color':'color',
                                                                   split between high-level computation                                                                                   'victim':None}}
                of the front chassis. It constantly scans
                the floor to identify special tiles (such as       and real-time hardware execution.                                                                                  Output of the top-right tile
                                                                                                                                                                                      {(3,1):
                black pits, penalty zones, or silver                                                                                                                                      {'Walls':{'N':True,'S':False,
                checkpoints), triggering immediate path            Raspberry Pi 5: Acts as the main                                                                                       'W':True,'E':True},
                recalculations.                                    master controller, running the Python                                                                                  'tile_color':'blue',
                                                                   software stack responsible for heavy                                                                                   'victim':None}}
                                                                   OpenCV image processing, mapping data, and Dijkstra
                                                                   pathfinding decisions.
Detection of Stairs and Ramps                                                                                                                           visualization of map matrix
                                                                   ESP32 Microcontroller: Acts as the hardware control unit
A BNO055 9-axis IMU (combining an                                  running embedded C++. It directly interfaces with the motor
accelerometer and gyroscope) tracks the                            drivers, reads the encoder ticks, manages the I2C sensor bus
robot's real-time incline. When a ramp or                          telemetry, and executes low-level movement adjustments.
stair configuration is detected, the low-level
controller automatically raises the motor
PWM output to prevent stalling on the slopes.

                                                                                                                                                      Challenges
                                                                                                                                                     I2C Bus Reliability
                                                                                                                                                     The initial I2C bus built with Dupont connectors caused
                                                                                                                                                     connection dropouts between the eight VL53L1X sensors
                                                                                                                                                     and the IMU.We replaced it with a custom screw-in
                                                                                                                                                     terminal bus, which improved physical durability
                                                                                                                                                     and connection stability.
Rescue Kits Handling                                               Strategy                                                                          Electromagnetic Interference (EMI)
                                                                                                                                                     Motor driver noise often caused sensor communication
Dispensing Mechanism: The rescue kit                               Our navigation strategy relies on a continuous loop of sensor                     failures. We mitigated this by wrapping sensitive cables
deployment module is entirely modeled                              polling, environmental mapping, and real-time path                                in aluminum shielding and intertwining SDA/SCL lines
in-house and 3D printed. It features a                             recalculation. At each tile, the robot averages its sensor pairs to               to create an enclosed field, effectively cancelling out
gravity-fed vertical storage magazine that                         classify tile attributes and record wall structures. It then passes               electromagnetic interference.
holds the rescue kits securely during maze                         this live map data into a weighted Dijkstra algorithm, which
navigation.                                                        naturally prioritizes exploration by assigning low costs to                       Crash-Proofing
                                                                   unvisited tiles while heavily penalizing dangerous zones or                       To prevent data loss during crashes, we utilize Python
Servo-Driven Actuation: When the                                   blocking black pits entirely.                                                     Context Managers. If the software fails, the system
mapping logic confirms a valid victim,                                                                                                               automatically closes the serial port and saves the
a 5V servo motor controlled by the                                                                                                                   active map as a .npy file. This ensures the map is
ESP32-S3 rotates a notched distribution                            If the robot experiences a lack of progress or gets trapped,                      preserved and prevents "port busy" errors during rapid
wheel. This mechanical sweep isolates                              the emergency recovery routine is triggered via a physical switch.                restarts.
and drops exactly one rescue kit down                              The internal coordinates then automatically reset to the nearest
the deployment chute onto the tile.                                safe silver checkpoint tile, allowing the system to seamlessly
                                                                   recalculate a new path and continue the autonomous run without
                                                                   human intervention.

Meet the Team
Karolína (Mechanical Design):                                            Martin (Software):                                       Our Journey
                                                                         Developed the entire software stack from scratch,        We started from scratch, building our own practice field and refining our
Managed the mechanical development, including                                                                                     design through many iterations. While we faced challenges, including
the 3D modeling and printing of the main chassis and                     encompassing the maze-solving algorithms, sensor
                                                                         data processing, and victim identification using         burned components and failed tests, each mistake taught us something new.
the rescue kit deployment mechanism, ensuring all                                                                                 This process led us to win the national round, earning our place
components were refined through multiple design                          OpenCV and Python.
                                                                                                                                  at the world competition RoboCupJunior 2026 in Incheon, South Korea.
iterations.
                                                                         Nikita (Documentation):
Anton (Electronics): Oversaw the assembly and wiring                     Managed all project records, including the creation of   Future Plans
of all electronic systems, such as the distance sensors,                 the competition poster, which proved essential for       We are focused on rigorous testing, improving software stability
color sensor, and IMU, leveraging past experience                        maintaining clear documentation during building and      and enhancing mechanical durability.
to ensure reliable connectivity and testing throughout                   testing periods.                                         Our goal is to complete a full maze run, identify every victim,
the competition.                                                                                                                  and return to the start.

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