Meredith Doan
Pronouns: she/her
Research Mentor(s): Kimberlee Kearfott
Research Mentor School/College/Department: NERS/BME / Engineering
Program: UROP
Session: Session 2 (10:00am – 10:50am)
Authors: Meredith Doan, Hythem Beydoun , Ryan Kim, Christopher Davis, Kimberlee Kearfott
Abstract
An Intelligent Radiation Awareness Drone (iRAD) is being developed which utilizes a unique algorithm (HazNav) that creates maps of ionizing radiation sources producing measurable fields. Ultimately, this source mapping software will optimize flight paths for maximal data collection efficiency. Both WiFi signals and gamma rays experience an intensity decrease that is inversely proportional to the square of the distance from their source, the primary algorithmic assumption. A WiFi-sensing payload would allow iRAD to be tested to scale without creating undue ionizing radiation hazards. WiFi intensity maps would themselves be useful for identifying weaknesses in coverage. In addition, a drone-mounted WiFi-sensing payload could locate the origins of any signal interference. Communication has been established between a WiFi sensor (Espressif Systems ESP32) and an inexpensive single-board computer, a RaspberryPi 3B. The sensor module is a microcontroller unit with integrated WiFi and Bluetooth connectivity. The RaspberryPi 4B is a fourth generation credit-card-sized single board computer with wireless Local Area Network. The WiFi-sensing software is roughly 85 C++ lines and performs a continuous WiFi scan that prints nearby network names and Received Signal Strength Indicator. The next step is to ensure the output of the program is compatible with HazNav. The HazNav software, which will take in signal strength data points and create a map including the individual’s location and signal strength values, is being developed separately. This could be implemented on the Raspberry Pi, the flight computer, or a remote system. The WiFi-sensing payload, having a <1 kg target weight, will require weatherproofing for robustness. One potential weather-proofing method is a snuggly-fitting 3D printed case. The payload will be tested experimentally using multiple emitters before flight deployment. The resulting WiFi-sensing payload and mapping software may prove useful for both an affordable homemade drone and a commercially available heavy-lift drone.
