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Autonomous Drones for Air Pollution Tracking (ADAPT)

Motion Planning, Wind Dispersion Modeling, Data Visualization, Sensor Integration, Automation

Featured in MIT NewsTackling air pollution with autonomous drones↗

Project Overview

NEET Capstone, Completed Spring 2021

Over half of the world’s population has no access to official government data on air quality, despite the fact that nine out of 10 people breathe air containing high levels of pollutants. In the US alone, particulate matter 2.5 (PM 2.5) Levels cause 200,000 early deaths in the US each year. For decades, people living next to sources of pollution lacked evidence that could convince regulators and polluters that they were being harmed. We aim to create a network of autonomous UAVs with on-board PM 2.5 sensors that collect and share accurate and real time air quality data over large areas. Data will be displayed through a live, interactive web application.

People

Jeana Choi, Daniel Diaz, Chloe Nelson, Rima Rebei, Berke Saat, Leilani Trautman

Featured in MIT News ↗ ↗

Control Architecture

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Air Pollution Visualization Web App Flow Chart

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Hardware for Pollution and Wind Sensing and Cellular Data Transfer

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