TEAM VAST Anya Shruti Trischa Won 2nd Place National (India) - Score ~ 470-500 points Anya Agarwal Shruti Purumala Trischa Taneja Anya led hardware integration Shruti developed Samarth's Trischa led Samarth's and navigation development, DFS/BFS navigation mechanical design, including PID wall-following, algorithms, heading creating the custom heading correction, ramp correction, and cell detection, and OpenMV-based Fusion 360 chassis, realignment system. She also victim detection. She has component mounts, managed overall software experience of over 8 years in sensor cutouts, and the integration and has robotics including multiple dual-purpose rescue kit languages and has led multiple experience in Python, C++, deployment teams for international and computer vision using mechanism. competitions. OpenCV. OpenMV H7 + Camera: Detects Storage for rescue kits: Stores HARDWARE victims and provides visual up to 8 rescue kits and guides information for navigation. them toward the release mechanism using gravity. The servo below will block the Front Ultrasonic Sensor (1): rescue kits form dropping into Measures distance to walls and the dropping chute. obstacles ahead. Servo: The servo will be Side Ultrasonic Sensor (2- left & mounted here and will be right): Maintains wall alignment attached with thearm. Rotates and detects openings. the deployment arm to release rescue kits when a victim is detected Gyroscope: Tracks robot orientation and turning angles for accurate movement. Release Arm: Rotates based on victim detection to release a Rescue Kit Drop Chute: Guides rescue kits from specific number of rescue kits. the release mechanism to the ground for to position and release rescue Color Sensor: Detects floor tiles, accurate victim aid delivery. It has 2 ways for kits accurately. checkpoints, and special maze ease and so the kit doesn’t get stuck. zones. Main Rescue Kit Battery: Controller Dropper Motors (4 )(2 Powers all (Cytron Mechanism: right and 2 electronic 2040): Main Automatically left): Drives Wheels (4): Provide traction and processing deploys rescue the left and and mobility throughout the mechanica unit that reads kits when a right wheel maze. l systems. sensors, victim is during executes detected, movement algorithms, ensuring and turning. Camera detects Cytron RP2040 Signal sent to servo Kit falls through Release gate opens and controls accurate and victim (letter/cognitive) processes and verifies location to rotate arm and path is cleared gravity at the victim location downward robot reliable delivery. movement. SOFTWARE Depth-First Search (DFS) Exploration: Depth-First Search (DFS) is used to systematically explore the maze. The robot prioritizes unexplored paths, marking each tile as visited Programming Environment: and storing junction information. When a dead end is reached, the Language: C++, Python robot backtracks to the nearest unexplored junction and continues Controller: Cytron 2040 exploring until the entire maze has been mapped. Libraries Used: Key Features MPU6050_LIGHT: Processes IMU data for orientation and turn Explores every reachable path. tracking. Prevents repeated exploration of visited areas. Adafruit_TCS34725: Reads color sensor data for tile and Uses backtracking to recover from dead ends. checkpoint detection. Builds a complete map of the maze for later navigation. Adafruit_NeoPixel: Controls RGB LEDs for robot status and victim indication. Breadth-First Search (BFS) Recovery & Path Planning: When no unexplored paths remain nearby, Samarth uses Breadth-First Search (BFS) to find the shortest route through previously explored Entire Software Overview Diagram- BFS & DFS safe cells. The algorithm searches the mapped maze, identifies the nearest unexplored frontier or the start tile, and generates an efficient path to reach it. This allows the robot to recover from dead ends, continue exploration, and reliably return to the starting position after completing the maze. Key Features Allows blue-tile recrossing avoidance Recovers efficiently from dead ends. Routes the robot to te nearest exploration frontier. Guarantees a return path to the start tile. Victim Detection System Samarth uses an OpenMV H7+ camera running an Edge Impulse machine learning model to autonomously identify victims. Images captured by the camera are processed onboard in real time, allowing the robot to classify victims, trigger LED indicators, and initiate rescue kit deployment without external computation. Key Features Real-time onboard image processing. Edge Impulse machine learning model. Autonomous victim classification. Triggers rescue kit deployment. Provides LED-based victim indication.