PCMS_ET SOFTWARE EXPLORING ALL OF THE MAZE BY OVERVIEW FLOW-CHART OF THE SHORTEST ROUTE MAIN LOOP our programming is based on Base Method: We designed a maze exploration method that the Robot Operating System maintains a two-dimensional map of discovered tiles and selects (ROS), an environment that robot actions based on its orientation and local information. provides files and services for Baseline Reference and Its Limitation: As a reference, we applied communication between a right-hand-based exploration strategy. When the number of programs. This allows us to maze tiles is N, this approach requires approximately 2.2N search divide the tasks between several actions on average, resulting in inefficient exploration. programs at the same time. LAO CHI IENG LUI IEK TONG Proposed Solution: To improve efficiency, we developed an original algorithm that combines depth-first search and breadth- CIRCUIT DESIGN Hardware Hardware first search, named DBFS (Depth and Breadth First Search). SUB BOARD Traversing alorithm Documentation Algorithm Mechanics: DBFS prioritizes movement toward the - Controls 10 laser distance sensors and a gyroscope via I2C nearest reachable tile among known candidates. Robot behavior - Aggregates sensor readings and sends data to the main board is divided into search and move, defined below. through UART WONG CHI KIO LEI UN HOU MAIN BOARD SEARCH MOVE Central controller with reserved PCB interfaces: Software Logs Image recognition Sensor system Adjacent unexplored tiles are The robot selects the next - 2 motor ports (2.54P), driving 4 motors (2 front, 2 rear) added to a LIFO structure destination from UNSEARCH. - 3 I2C ports, 4 ADC inputs, and 4 GPIO pins named UNSEARCH with the Using BFS, the robot computes - 2 UART connections (sub board + camera module) priority: the shortest path and runs a - Powered by a 12V battery with a 12Vā€“5V converter for TB6612FNG [ LEFT ā†’ FRONT ā†’ RIGHT ] brief simulation before moving. board operation JY61P SERIAL ACCELERATION SENSOR ELECTRONIC GYROSCOPE MODULE ATTITUDE ANGLE - Includes an OLED display and three buttons (power, MEASUREMENT Next destinationis motor enable, start) tile 3. Run a simulation tofind the shortestpath PUSH POP-UP to this tile. HARDWARE DC DC BUCK BOOST CONVERTER VARIABLE VOLTAGE REGULATOR: 12V TO 5V SENSOR & ACTUATOR INTEGRATION SEARCH AND MOVE ARE EXECUTED ALTERNATELY. - Path tracking: 9 Laser Sensor VL53L0X array at front. EXPLORATION ENDS WHEN UNSEARCH BECOMES EMPTY - Victim recognition: Vision modules (Sipeed MaixCAM) on RESULT ESP32 WROOM-32E both sides. - Terrain perception: Color sensor (bottom) for forbidden DBFS was evaluated against a right-hand-based method. The zones; bumper switches (front sides) for collision ESP32-PICO KIT number of search actions was reduced by approximately 33%, prevention. improving exploration efficiency and enabling more reliable - Drive system: 25GA-370 DC geared motors with encoders ESP32 DEVELOPMENT BOARD exit reach and Exit Bonus achievement for odometry. CONTROL & CIRCUIT SYSTEM IMAGE RECOGNITION USING MACHINE - ESP32 main controller. LEARNING - Two custom PCBs for power management, signal Victims on maze walls are detected while the robot moves transmission, and sensor integration, reducing wiring issues forward using UART communication with camera modules. and improving anti-interference. CHASSIS & SUSPENSION Color victims are identified using LAB thresholding, while - Ground clearance: 25 mm. letter victims are recognized by a trained machine learning - Rocker-bogie (parallel linkage) suspension for terrain model. White LEDs are used to reduce the DS3225 30KG RC adaptability. DIGITAL SERVO influence of ambient lighting, improving - Battery placed at center-bottom for low CG and balanced 25GA-370 12V 58RPM DC recognition accuracy. REDUCER GEAR MOTOR weight distribution. Machine Learning Details: Letter RESCUE KIT DEPLOYMENT SYSTEM X9 recognition uses a MobileNet-V2 model - Dual-slide track with a central rotating disk for left/right V2 LASER RANGING SENSOR VL53L0X trained on approximately 6,000 self- delivery. collected images. The model is converted - Bottom guide rail ensures precise drop into victim zones. to TensorFlow Lite and deployed for real- RP-L-170 THIN FILM WHEELS & TIRE DESIGN FLEXIBLE FORCE time inference ROBOT & PRESSURE SENSOR - 3D-printed resin hubs with tooth grooves for obstacle climbing. Training progress is monitored using learning curves to prevent - Ring grooves lock silicone tires firmly, preventing slippage or 25GA-370 12V 58RPM DC COMPONENT overfitting, and recognition accuracy improves with continued detachment during turns/climbs. REDUCER GEAR MOTOR training. WITH ENCODER SIPEED MAIXCAM