Background: Despite rapid technological development of UAS technology, a completely autonomous unmanned aerial system is not a reality yet, particularly for civilian or commercial uses. There are many technological hurdles to overcome before autonomous unmanned aerial systems become widespread. Even though some low-level of autonomy has been achieved for UAS, it still requires a human operator to control the flight of an UAS. There are many applications that can benefit from autonomous UAS technology. For example, running surveillance UAS flights around the clock can be too expensive due to the need for human operators. Similarly, flying an UAS in some inaccessible or hostile location can be too inconvenient or dangerous for a human operator. It is possible though to develop in some cases an application-specific, cost-effective solution for autonomous UAS by utilizing existing results already published in literature. Accordingly, in this project, we plan to develop an autonomous UAS to search and track a shoreline. This research will achieve a technological advancement that can help researchers in many fields who collect data from such environment for their study. The system can also be used for autonomous video surveillance.
Problem Description: A shoreline lies along the edge of an ocean that separates land from water. The separation of land from water can be detected from aerial imagery as evident from some existing works that deal with similar situations. However, there exist some additional challenges that need to be addressed in this problem due to the fact that a shoreline is very dynamic, always changing due to waves, currents, winds, and tides. We plan to investigate how to detect a shoreline using a UAS in real time so that the UAS can follow the detected shoreline as a guide to fly itself over the coastline. The research to develop autonomous unmanned aerial systems for shoreline tracking can benefit other fields of research such as environmental science and ecology. In addition, it has many other civilian, governmental, and commercial applications.
Student Research Activities:
Subsequently, the REU participants will develop and integrate the shoreline detection system with the navigation system and test its effectiveness in real-time environments.
Student Background: Basic computing knowledge and introductory programming skills such Java or C++ is recommended.
Background: Coastal management requires accurate and timely information about the state of coastal landforms (e.g., beaches) and how they are evolving. Small-unmanned aircraft systems (UAS) equipped with digital cameras provide a new paradigm for surveying of coastal environments. Compared to traditional field surveying methods or manned aircraft surveys, UAS provides certain advantages: rapid deploy capabilities, temporal flexibility, and hyperspatial image acquisition. When coupled with repeat observations, these data can be used to derive high definition 3D digital terrain models and to characterize spatial-temporal changes of built and natural terrain in coastal zones. This newly captured information can be used to improve management practices, engineering design, and scientific decision making as it relates to coastal resiliency. For example, UAS topography data can be used to improve model calibration and forecasting of coastal hazards such as hurricane impacts or oil spill distribution.
Problem Description: We plan to utilize UAS equipped with multispectral sensors for imaging of coastal landforms within the region: beaches, dunes, wetlands. The acquired image sequences will be processed using structure-from-motion (SfM) photogrammetry to derive 3D measurements (x, y, z point cloud data) for digital modeling of coastal terrain. Accordingly, 3D data processing and spatial analysis will be implemented using geospatial computing techniques and software to characterize coastal landforms and processes captured in the repeat UAS observations. For analyzing patterns in coastal landform evolution, open-source GIS software and spatial modeling approaches, such as space-time cube (STC) representation, will be applied to the time series of hyperspatial UAS point cloud and raster products. Image classification methods using the UAS multispectral imagery will be explored to develop automated approaches for landcover mapping. Finally, through these UAS-based analyses, students will explore coastal problems of immediate relevance and importance to the region, which is an area prone to erosion and coastal hazards.
Student Research Activities:
Student Background: Basic computing knowledge and introductory programming skills such in C++ or Python or MATLAB is recommended.
Background: Seagrass meadows are important natural resources that act as habitat for numerous marine species, absorb carbon dioxide from the atmosphere, and limit shoreline erosion. Conservation of these seagrasses are of major concern as human and natural activity has destroyed over 70% of seagrasses along the US coastline. Accurate information of the location and extent of meadows is important for coastal management and scientific studies. An airboat has been created for operating in shallow water. By placing the propellers above the boat, there is nothing extending into the water that would damage the seagrasses or get the vehicle stuck. This research is to develop a system to automatically classify underwater images to generate maps of the nearshore environment.
Problem description: Remote control will be used to cover an area to record underwater RGB video, while logging the GPS coordinates. A GoPro will be used for the camera. RGB cameras can operate in extremely shallow water. However, there are a variety of challenges when working with RGB underwater: Light is scattered dramatically with increasing depth, making light-based imaging difficult by obscuring color. Also, the turbidity of the water in many regions makes the imagery cloudy from the occlusions by dense floating particles. Students will explore image processing techniques for resolving some of these issues as well as machine learning techniques for image classification. Numerous machine learning algorithms are available and students will compare the accuracy of various techniques, as well as varying the features extracted from the images. Finally, the classifications will be used with the GPS coordinates to create a raster maps.Student Research Activities:
Student Background: Basic computing knowledge and introductory programming skills in C++ or Python is recommended.
Background: Metaheuristic search algorithms have been successfully used to solve a variety of optimization problem with huge search space. These search algorithms have a high capability to find, produce, or choose a heuristic solution for a diversity of single and multi-function optimization problems. Providing a solution is quite challenge when their exits multiple objectives to achieve. UAV Mission Planning (MP) problem is one of these critical applications. In various military applications, we may use UAV for complex and high-risk missions to avoid human risk and increase efficiency. UAVs need to be sufficiently smart to autonomously follow a safe path to a pre-defined target behind enemy lines, avoid obstacles such as other aircraft or enemy missiles. UAV need to optimize a number of conflicting goals such as minimizing the distance of a path and achieving a high level of safety. In this project, the goal is to develop an optimal trajectory for a UAV mission. This optimal trajectory shall allow the UAV to navigate over bumpy terrain. Nature-inspired metaheuristic search algorithms have been used with high performance to tackle the above described missions.
Problem Description: The aim of this project is to allow students to get a hand experience on Unmanned Aerial Vehicle (UAV) routing and mission planning. To achieve this goal, 1) we plan to explore various metaheuristic search algorithms for function optimization; 2) learn how to program metaheuristic search algorithms using Matlab; 3) design a path planning evaluation function with set of constraints that include minimization of time to goal, total mission accomplishment time, and avoidance of enemy detection systems such as radar; 4) solve the problem of multi-objective optimization using metaheuristic search algorithms (i.e., Genetic Algorithms, Particle Swarm Optimization) to create an optimal mission path. An ultimate goal of this research is to allow students to develop a new routing (i.e., path planning) software for the UAV to accomplish a pre-programmed mission. Students will learn how to develop a graphical user interface for a software system to better utilize the system for various users.Student Research Activities: The undergraduate researchers will perform the following tasks: