EcoDrone: Autonomous Environmental Monitoring

This project presents EcoDrone, an autonomous aerial drone designed for continuous and automated environmental monitoring. Current environmental monitoring methods rely on stationary sensors or manual data collection, limiting real-time response capabilities. This reliance leads to delayed, incomplete, and spatially limited data and restricts the ability to capture real-time changes. Another challenge includes the difficulty of environmental monitoring in challenging terrain, whether it be wildfire areas, dense forestry, or mountainous terrain. EcoDrone overcomes these challenges by autonomously navigating difficult terrain to collect real-time data, offering more flexible and timely monitoring than stationary or manual methods. The central research question investigates integrating standard consumer components and open-source software to create a drone system to improve real-time data collection. EcoDrone integrates a suite of onboard sensors to measure temperature, humidity, pressure, and CO2 concentration, transmitting real-time data to a ground station for analysis. The research methodology includes hardware and software systems integration and preliminary flight testing. System integration includes implementing the base DJI Tello drone's flight control and status logging onto a separate onboard ESP32 microcontroller. The controller, living on top of the drone, sends pre-programmed flight path commands to the drone and records the drone's status (i.e. battery, height, velocity, acceleration). Simultaneously, the external sensors take measurements and are logged locally on the microcontroller's built-in storage and transmitted in real time. Expected results include successful flight control and environmental sensing integration, reliable real-time data transmission, and improved spatial coverage in difficult terrain. EcoDrone is expected to demonstrate the feasibility of low-cost, autonomous monitoring solutions for capturing dynamic ecological data with greater efficiency and adaptability than conventional methods.