GeoFireBot: GPS-Guided Autonomous Fire-Fighting Robot

Inspiration

The rising frequency of fire accidents in urban and rural areas, especially those left unattended in isolated streets or alleys, inspired us to create a fire-fighting robot that does more than just local detection. Most fire-fighting robots rely purely on flame sensors, limiting their response to nearby threats. We wanted to go beyond — to enable a robot to respond to remote fire alerts using GPS navigation, ensuring a **faster and more intelligent emergency response system.

What it does

We designed and built a four-wheeled robotic vehicle controlled by an STM32 (Blue Pill) microcontroller, equipped with the following components:

Neo-6M GPS Module to get real-time location of the robot GSM Module (SIM800L) to receive fire location coordinates via SMS L298N Motor Driver to control the movement of 4 BO motors Flame Sensors (Front & Back) to confirm fire presence at the destination Compass Module (HMC5883L) for heading alignment and direction Fan or Pump with Relay to act as a fire-extinguishing mechanism 3.7V Li-ion Batteries with Buck Converter for stable power delivery

The GPS coordinates of a fire are received remotely via the GSM module. Once the robot receives the location, it compares it with its current position and begins autonomous navigation. Upon reaching the destination, flame sensors verify the presence of fire, and the extinguishing system is activated.

How we built it

• How to parse GPS data (NMEA sentences) and extract latitude/longitude
• Communicating between GSM and STM32 using UART
• Calculating distance and direction between two GPS coordinates (Haversine formula)
• Integrating multiple modules (GPS, GSM, Compass, Flame Sensors) into a single working system
• Basic autonomous navigation using heading and location logic
• Designing safe power distribution for multiple modules

Challenges we ran into

GPS accuracy: GPS modules have an average error margin of 3–5 meters, making precise navigation challenging in tight spaces. -Direction sensing: Without a full IMU, we had to rely on compass modules and controlled motor logic to maintain heading. Power management: Simultaneously powering GSM, GPS, motors, and sensors required careful voltage regulation and power balancing. UART conflicts: STM32 has limited UART ports; we used software serial or alternate UART pins where necessary. Testing in real-world scenarios: Simulating remote fire alerts and testing GPS navigation in open environments took time and effort.

Accomplishments that we're proud of

Successfully integrated GPS and GSM modules with STM32 to receive and process remote fire location alerts.

Implemented autonomous navigation based on real-time GPS coordinates using heading logic.

Built a reliable fire verification system using flame sensors to confirm fire presence before extinguishing.

Achieved stable multi-module communication (GPS, GSM, sensors, motors) through proper UART handling and power management.

Proved that a robot can respond to fire emergencies outside its sensor range, pushing the boundaries of traditional fire-fighting robots.

What we learned

How to parse and use GPS data (NMEA sentences) in embedded systems. Efficient UART communication handling in STM32 using bare-metal or HAL approaches. Concepts of geospatial navigation – calculating distance and heading between two coordinates. Real-time decision making using sensor input combined with location-based data. Power regulation for a multi-component embedded system using buck converters and Li-ion batteries. The importance of robust testing in real-world conditions to simulate emergency scenarios.

What's next for GeoFireBot: GPS-Guided Autonomous Fire-Fighting Robot

Add Obstacle Avoidance: Integrate ultrasonic or infrared sensors for safer autonomous travel in cluttered environments. Real-Time Path Planning: Implement a basic route optimization or A* algorithm using map APIs or coordinate sets. IoT Dashboard Integration: Create a web or mobile interface to monitor robot status, location, and fire alerts in real time. Camera Module for Visual Confirmation: Add a camera for live video feed or AI-based fire detection using image processing. Multi-Robot Coordination: Enable communication between multiple GeoFireBots for large-scale fire response in different zones. Solar Charging or Smart Docking: Improve endurance by enabling automatic recharging capabilities.

Built With

• and
• bo-motors
• buck-converter
• c/c++
• embedded-c
• fan-module
• fire-detection
• gps-navigation
• hmc5883l-compass
• ir-flame-sensors
• l298n-motor-driver
• li-ion-batteries
• neo-6m-gps-module
• pwm
• relay-module
• relays
• sim800l-gsm-module
• stm32cubeide
• stm32cubeide-?-firmware-development
• stm32f103c8t6
• uart-communication
• ultrasonic-sensor
• water-pump