Research Projects

Project No. 1

Title: Vision-Based Control of Unmanned Aerial Systems (UAs) Swarm

Mentor: Dr. Wei Sun

Nowadays many aerial systems (UAs) utilize the fusion of multiple sensors to accurately estimate vehicle position and orientation. These sensors normally include GPS and Inertial Measurement Units and lead to expensive, heavy and complex navigation systems that are not suitable for a swarm of small UAS operating in tricky environments, such as indoor or urban environments where the GPS signals are disrupted. Swarm systems can complete tasks in a collaborative manner that are quite difficult for a single agent to achieve. Furthermore, a swarm of UAS is much more flexible in performing tasks compared to a single agent and is adaptive to both exogenous influence and endogenous disturbance. Objectives: This research aims to develop vision-based navigation and control of a swarm of UAS where each vehicle is mounted with a single camera to generate navigation information for autonomous guidance and control. For the vision-based guidance and control task at hand, the noisy measurements produced by each camera need to pass through a nonlinear filter to estimate the position, velocity and orientation of each UAS. Differences between consecutive camera images and image information collected by neighboring UAS-mounted cameras need to be fused to determine relative states of the UAS, which will then be utilized to control the swarm of UAS. In terms of the control design, a formation control approach, which treats the swarm as a unity or leader-follower and adopts the control strategies developed for a single agent for the swarm, will be utilized. Graph theoretic methods and differential game methods in multi-agent networks need to be explored to achieve coordinated control and collision avoidance of the swarm. The guidance and control scheme of the swarm of UAS developed in this research will first be evaluated in a simulation environment and then validated by Quadcopter flight tests. The flight tests will be performed in the DroneDome at Dr. Sun’s Lab. The REU group will also conduct testing in the open field with an application in mind—package delivery. The team will travel to the Choctaw Nation Unmanned Aircraft System Integration Pilot Program (UASIPP) test site for testing in an open environment. The Choctaw Nation UASIPP test site provides a unique landscape (trees, ponds, wind, large distances between targets, etc.) for the REU team to study the delivery of packages.

Project No. 2

Title: Aerial Sampling of Pollutants (Particulate Matter) from Human Generated Activities

Mentor: Dr. Wilson Merchan-Merchan

REU interns will apply drones to collect particulate matter in an open atmosphere. At OU we have developed and perfected various intrusive techniques for collecting samples of particulate matter (carbon and metal oxide particulates) directly from within the volume of a variety of flame geometries. In recent years, aerial sampling in open atmosphere has been conducted using drones to study gaseous emissions released from human activity into our environment. An innovative approach will be used in this study to sample airborne particulate matter emissions generated from human activity. In the framework of the project, a sampling device will be mounted on a remote-controlled drone and elevated to certain altitudes in the atmosphere to collect samples of soot particulates/urban dust emitted into the atmosphere from combustion and other industrial activities. The sampling device mounted on the drone is composed of an Al disk with double sided carbon tape that serves to trap airborne particulates/emissions. The disk holder assembly securing the Al disk is attached to a lightweight carbon fiber tube to distance the sampler holder from the drone propellers. The REU participants involved in this project will study the physicochemical properties of the collected PM samples (airborne) using low and high transmission electron microscopy (HR/LR-TEM), Electron Energy Loss Spectroscopy (EELS), scanning electron microscopy (SEM) and energy disperse x-ray spectroscopy (EDX). Objectives: The REU team will develop and implement a novel aerial method to collect particulate matter from an open environment. This will be done using a drone and a customized collection device mounted on the drone. Participants will learn the importance of drone payloads (structural & stability aspects), drone control, combustion fundamentals, and fundamentals of electron microscopy. The sampling method designs should be dynamic with adaptive and high-resolution sampling capabilities to function in laminar, transition and turbulent flow environments. REU interns will also have the unique opportunity to work with electron microscopy for sample analysis; they will be exposed to sample preparation, SEM/TEM/EELS/EDX image capturing, and the analysis of the collected images.

Project No. 3

Title: Small Drones to Assist in Human Exploration of Mars

Mentor: Dr. Diogo Merguizo Sanchez

NASA's Ingenuity Mars Helicopter was the first aircraft to achieve powered, controlled flight on another planet, successfully flying in the Martian atmosphere, which is over 100 times thinner than Earth's and is primarily composed of carbon dioxide (95.1%), nitrogen (2.59%), argon (1.94%), and traces of other gases. With the possibility of human astronauts being sent to Mars in the near future, the use of drones can support human exploration on the red planet. Therefore, new models of small drones designed to fly on Mars can be central for colonization. Objectives: In this project, the REU interns will first work on the design of drone frames for motors and propellers that would make small drones capable of flying in ultra-low atmosphere conditions similar to that of Mars in density and gaseous components. The number and configuration of the propellers, as well as the shape of the blades, will be considered in the CAD design and modeling. The groups will also test different materials for manufacturing the blades/frames. Furthermore, the REU participants will have the opportunity to test their drone models in a chamber that emulates the Martian atmosphere. To measure the efficiency of the models/designs, the REU team will analyze the lifting and stability of the small drone for several Martian altitudes emulated through different densities in the test chamber. The study will also involve subjecting the small drones to fluctuating low temperatures within the test chamber to assess their flight capabilities and overall performance, mimicking the low temperature on Mars. The REU team engaged in this project will employ a range of 3D printing techniques, encompassing additive manufacturing and stereolithography, to convert their designs into the constituent components of the drones.

Project No. 4

Title: 3D Printed Frames for Drones

Mentor: Dr. Yingtao Liu

The aerial distance from ground to drone flying/hovering location is very critical depending on the application of the drone. For instance, for drone surveillance the collection of rapid close up views of a ground area (details of the surveilled objects) may be necessary. In order to address those issues a large “Mother” drone with several smaller “baby” or mini-drones stored on its back (or platform) may help to reduce the time for the surveillance and the number of smaller drones could increase the surveillance area. In search-and-rescue missions this system can be very handy as the mini-drones can cover more area and in other applications the mini-drones are less visible/detectable due to their size and noise. Besides drone delivery and data collection the drone mothership could act as a base for the mini- drones to return to and recharge when their relatively small batteries become discharged. REU interns involved in this research will work on the design, fabrication and flight tests of a platform for “mother and baby drones” for space vehicles (drones). 3D printing or additive manufacturing is a popular fabrication technique that constructs a three-dimensional object from a CAD model or a digital 3D model; a tool that will be used by the team working on this project. Objectives: The REU team will study the design, fabrication, and flight of the 3D printed platform or drone carrier. The REU team will conduct flight tests of the drone carrier in the DroneDome to evaluate its performance and stability. The team will also test the drone carrier in the open field to mimic the effect of real-world conditions where the carrier is intended to operate. This includes factors like wind, turbulence, and obstacles that drones will encounter when flying in various outdoor scenarios. Interns will conduct bending tests on parts of the 3D printed platform to obtain a correlation between the strength and type of filament or resin used in the carrier's construction. The research team efforts will contribute to the design of drone platforms that expand the capabilities and functionality of drone fleets.

Project No. 5

Title: Title: Characterization of Drone Battery Performance under Variable Thermal and Aerodynamic Environments(Psychrometric chamber)

Mentor: Dr. Jie Cai

Lithium-ion batteries are predominantly used in drones and UAS. The battery performance, such as round-trip efficiency and rate of capacity degradation, is highly dependent on the usage patterns and thermal environments. REU participants will experiment with the drones available in Dr. Wei Sun’s lab and collect typical power usage patterns for predefined tasks (e.g., moving a parcel between two specified locations) with a specifically designed data acquisition system installed on the drones. The obtained battery discharge patterns will be utilized for offline testing of the same Lithium-ion battery within the psychrometric chamber in Dr. Cai’s research lab. A range of climate conditions will be emulated inside the psychrometric chamber and battery operation data will be collected to analyze the dependence of battery performance on weather conditions. Objectives: The REU team will acquire representative load cycles for drone batteries and develop a battery performance testing methodology under variable climate conditions, such as temperature, humidity and wind speed. REU interns will analyze the performance data and establish a weather-dependent equivalent circuit model and a capacity degradation model for drone batteries. The established models can be leveraged to predict the ranges of drones and aging rates of the onboard batteries using real-time weather forecast. More intelligent control algorithms can also be devised based on the performance models to maximize the service range with minimum battery lifetime impact. Dr. Cai has extensive experience in modeling, control and thermal management of lithium-ion batteries.

Project No. 6

Title: Natural Gas Leak Detection Using Unmanned Aerial Vehicle (UAV)

Mentor: Dr. Pejman Kazempoor

REU participants will be exposed to novel/modern technology to quantify natural gas leaks using a drone equipped with a gas sensor. They will learn to design and develop a sensor package mounted onto a drone for detecting and quantifying natural gas leaks. Recent studies show the oil and gas sector leak this potent greenhouse gas far more than previously thought. Unmanned aerial systems can increase the reliability and speed of methane detection and quantification, especially in remote and hazardous locations. The main objective of this project is to develop an aerial-based methane detection platform, including a laser-based sensor (or an infrared camera) and a commercial drone. The expected product will help oil and gas companies protect the environment by detecting harmful leaks. Objectives: The REU team will have an excellent opportunity to work with drones and sensors while learning how the emissions data can be measured, transferred, and interpreted. Interns will be introduced to current practices and future prospects in the field of collecting methane emissions/leaks via an autonomous aerial platform. Currently, the detection of gas leaks using UAS is challenging. The accuracy of this new method is hampered by the limit of UAS payload and measurement interference from the engine’s vibrations. Other non-controllable scenarios that make the application of UAS for this task very challenging is the flow environment (subject to turbulence). Therefore, the REU team working on this task will collaborate with the wind tunnel group to expose the drone to a simulated turbulent flow. The desired end product is a drone capable of detecting gas leaks in less than ideal conditions.