Inspiration
Fire accidents in coal mines, industrial plants, warehouses, and other hazardous environments often put human lives at risk. Traditional firefighting methods require human intervention in dangerous situations, which can lead to injuries and delayed response times. We were inspired to develop an autonomous robotic system that can detect fire, monitor environmental conditions, navigate safely through obstacles, and assist in early fire suppression without risking human lives.
What it does
The AI & IoT Based Autonomous Fire-Fighting Robot is designed to detect fire, smoke, and temperature changes in real time. The robot uses multiple flame sensors to identify the direction of the fire, an ultrasonic sensor for obstacle detection, and intelligent navigation algorithms to move safely toward the fire source. It continuously monitors smoke concentration and temperature levels while transmitting critical information for emergency response. The system can also activate a water pump mechanism to suppress fire when it reaches the target location.
How we built it
The robot was developed using an Arduino UNO as the primary control unit and Raspberry Pi 4 for future AI and computer vision integration. The hardware includes:
Arduino UNO Raspberry Pi 4 HC-SR04 Ultrasonic Sensor DHT11 Temperature Sensor MQ2 Smoke Sensor Three Flame Sensors SG90 Servo Motor L298N Motor Driver Four BO Motors Relay Module Water Pump Raspberry Pi Camera
The software was developed using Arduino IDE and Python. Sensor fusion techniques were used to combine data from flame, smoke, temperature, and distance sensors. Obstacle avoidance and navigation logic were implemented to ensure safe movement in hazardous environments.
Challenges we ran into
One of the biggest challenges was integrating multiple sensors and ensuring reliable communication between components. Sensor calibration was required to improve the accuracy of flame detection and obstacle avoidance. Power management was another challenge because motors, sensors, and control systems require stable power sources. Developing a navigation strategy that could move toward a fire source while dynamically avoiding obstacles also required extensive testing and refinement.
What we learned
Through this project, we gained hands-on experience in embedded systems, robotics, IoT, sensor integration, motor control, and autonomous navigation. We also learned how AI and computer vision can be integrated with robotic systems to solve real-world safety problems and improve emergency response capabilities.
What's next
Future improvements include integrating real-time computer vision using Raspberry Pi Camera and AI models for accurate fire localization, cloud-based monitoring through IoT dashboards, thermal imaging for enhanced fire detection, and autonomous fire suppression strategies for large-scale industrial applications.
This project demonstrates how AI, IoT, and robotics can work together to improve safety, reduce response times, and minimize damage caused by fire-related emergencies.
Log in or sign up for Devpost to join the conversation.