AIRSENTINEL ROVER

Turning Invisible Pollution into Real-Time Action

Comment

Inspiration

Air pollution is one of the most critical yet invisible threats to human health today. Despite its severe impact on communities—especially children, the elderly, and people with respiratory conditions—most cities still rely on a few static air quality monitoring stations that fail to capture real, localized pollution variations. We were inspired by the gap between how widespread the problem is and how limited real-time, actionable data currently is.

Watching people commute daily through highly polluted roads without any awareness of immediate air quality risks highlighted the urgent need for a mobile, real-time, and intelligent monitoring system. This motivated us to combine robotics, IoT sensors, and AI to create a solution that doesn’t just measure air quality, but actively moves through environments, maps pollution, and predicts hazardous conditions in advance.

AirSentinel Rover was inspired by the belief that clean air is a basic right, and technology—when made accessible and scalable—can empower communities, institutions, and cities to take timely, data-driven action for healthier living.

What it does

AirSentinel Rover is an autonomous mobile robot that monitors, maps, and predicts air pollution in real time. As it moves through streets, campuses, or indoor spaces, it continuously measures key air quality parameters such as PM2.5, PM10, harmful gases, temperature, and humidity using onboard sensors. These readings are instantly converted into Air Quality Index (AQI) values and transmitted wirelessly to a live dashboard.

The system visualizes this data as interactive graphs and pollution heatmaps, clearly highlighting clean and hazardous zones. Beyond real-time monitoring, AirSentinel Rover uses AI-based forecasting to predict short-term AQI trends and identify potential pollution spikes before they become dangerous. When unsafe levels are detected or predicted, the system generates health alerts and safety recommendations for users.

By combining robotic mobility, real-time sensing, data visualization, and predictive AI, AirSentinel Rover transforms invisible air pollution into actionable intelligence. It enables schools, hospitals, campuses, and city authorities to make faster, smarter decisions to protect public health and improve environmental planning.

How we built it

We built AirSentinel Rover by integrating robotics hardware, environmental sensors, wireless communication, real-time data visualization, and AI-based prediction into a single end-to-end system. The mobile robot is powered by an ESP32 microcontroller, which controls the motors through a motor driver and enables Wi-Fi connectivity. An ultrasonic sensor allows the rover to move autonomously while avoiding obstacles.

For air quality monitoring, we equipped the rover with PM2.5/PM10 particulate sensors and temperature, humidity, and gas sensors. These sensors continuously collect environmental data, which is processed on the ESP32 to calculate real-time Air Quality Index (AQI) values. The data is sent wirelessly to a backend server using lightweight HTTP-based communication.

On the software side, we developed a backend service that stores incoming data and feeds it to a live web dashboard. The dashboard displays real-time AQI values, interactive graphs, and pollution heatmaps for easy interpretation. To add intelligence, we trained a machine learning model on historical AQI data to forecast short-term air quality trends and detect potential pollution spikes. These predictions are displayed on the dashboard along with automated alerts.

This modular architecture allowed us to rapidly prototype, test each component independently, and integrate them into a reliable, scalable system suitable for real-world deployment.

Challenges we ran into

While building AirSentinel Rover, we faced several technical and practical challenges.

Sensor Accuracy: Low-cost air quality sensors can produce noisy readings, especially for PM2.5 and gas concentrations. We had to calibrate the sensors and implement filtering techniques to ensure reliable data.

Autonomous Navigation: Programming the rover to move smoothly while avoiding obstacles required fine-tuning the ultrasonic sensor logic and motor controls. Ensuring it didn’t get stuck in tight spaces took multiple iterations.

Data Transmission: Sending real-time sensor data over Wi-Fi posed connectivity challenges, particularly in areas with weak signal. We optimized the communication protocol to reduce data loss and maintain dashboard updates.

Integration of Components: Combining robotics, IoT, software, and AI into a single functional system was challenging. Each module had to be tested independently before integration to prevent conflicts.

AI Forecasting: With limited historical data for some areas, training an accurate AQI prediction model required using public datasets and simulating short-term trends for demonstration purposes.

Accomplishments that we're proud of

Real-Time Data Visualization: We created a live dashboard with interactive graphs and heatmaps, allowing users to see pollution levels and trends instantly.

AI-Based Forecasting: Our system predicts short-term AQI trends and alerts users to potential hazardous zones, adding intelligence beyond simple monitoring.

Integration Across Domains: We combined robotics, IoT, software, and AI into a single working system, demonstrating a scalable solution that bridges technology and real-world impact.

Social and Environmental Impact: AirSentinel Rover provides actionable insights for schools, hospitals, and city authorities, helping protect public health and contributing to smarter, cleaner urban environments.

What we learned

Building AirSentinel Rover taught us valuable lessons across robotics, AI, and real-world problem-solving. We learned how to integrate hardware and software seamlessly, from sensors and microcontrollers to live dashboards. Navigating challenges like sensor calibration, autonomous movement, and reliable data transmission strengthened our troubleshooting and iterative design skills. Additionally, we gained hands-on experience in AI forecasting, seeing how predictive models can enhance practical solutions. Most importantly, we learned how to design technology with a real-world social and environmental impact, turning abstract data into actionable insights for public health and urban planning.

What's next for AIRSENTINEL ROVER

We plan to expand AirSentinel Rover beyond a prototype into a fully scalable solution for smart cities. Next steps include:

Enhanced mobility and coverage: Adding better navigation, longer battery life, and outdoor-ready hardware.

Multi-sensor integration: Including more gas sensors, weather sensors, and GPS for precise mapping.

Improved AI models: Using real-time and historical data to make more accurate short- and long-term AQI forecasts.

Mobile and cloud dashboards: Allowing remote monitoring for multiple rovers across large urban areas.

Startup potential: Partnering with schools, hospitals, environmental agencies, and city planners to deploy AirSentinel Rover as a service for healthier cities and smarter urban planning.

Built With

Share this project:

Updates