Implementation of Collision Avoidance in the Intelligent Radiation Awareness Drone – UROP Spring Symposium 2022

Implementation of Collision Avoidance in the Intelligent Radiation Awareness Drone

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Ricky Ho

Pronouns: he/him/his

Research Mentor(s): Kimberlee Kearfott
Co-Presenter:
Research Mentor School/College/Department: NERS/BME / Engineering
Presentation Date: April 20
Presentation Type: Oral5
Session: Session 3 – 1:40pm – 2:30 pm
Room: Breakout Room 6
Authors: Ricky S Ho, Marlee E Trager, Kimberlee J Kearfott
Presenter: 1

Abstract

The Territory of Lakota Tribe includes abandoned uranium mines and milling operations created for U. S. government atomic weapons production in the 1950s and 60s. The Lakota suspect that they face health problems linked to their continuous exposure to the radioactive contamination remaining from these mines. There are currently no affordable viable ways to map large areas contaminated with radiation. This project was undertaken to investigate the possibilities of using smart radiation sensors on a drone to collect, process, and analyze radiation data to map the distribution of radiological contamination. The drone will have to follow a set flight path, collect data, and relay it to a ground station. It is constructed using a LiDAR-Lite, Logitech D435 RealSense depth camera, PX4Flow camera, and other sensors connected to a Pixhawk 4 flight controller and Raspberry Pi 4 to perform tasks necessary for autonomous flight. The Hazardous Navigation (HazNav) algorithm, undergoing development for use with the drone, uses algorithms to reconstruct and refine radionuclide distributions based upon measurements taken at different locations. In addition, other algorithms that are responsible for mandatory flight operations in unmanned missions, such as obstacle avoidance planners, must also be created. This work will focus on the development, implementation, and testing of the flight algorithms which must interface cleanly with the HazNav approach. Upon the completion of the drone assembly, experiments utilizing the entire software system will be conducted using nonionizing radiation to test the effectiveness of the HazNav algorithm in accurately mapping locations of radiation sources.

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Engineering, Interdisciplinary

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