The UF/IFAS UAS Research Group can assist researchers at the University of Florida and in partnering organizations to apply UAS technology to their own data collection efforts. Our operational experience and broad multidisciplinary resources allow us to transform your project specifications into a suitable UAS mission. We also provide extensive logistical support in obtaining appropriate authorization to fly, for example filing for Certificate of Authorization (CoA) with the FAA. We serve as the primary contact for COA applications at the University of Florida. Developing a mission plan with us allows you to utilize our centralized UAS resources, reducing the costs associated with deploying the technology. A realistic budget for UAS operations to include in your research proposals and grants is also provided.
Mission planning and in-flight guidance must account for environmental variables such as wind speed and direction, as well as the movement, geometry, and spectral properties of the target. The ability to generate robust flight plans using waypoint-based guidance systems that account for these factors is an active area of research. The collection of geospatial data from unmanned platforms depends on the reliable execution of a flight plan that ensures complete coverage of the target area as well as robust and blunder-free sensor data. Failure to acquire sufficient data in an otherwise adequate preplanned flight can be caused by a multitude of sources; environmental disturbance or other physical interruption of the flight plan, failure of the guidance and navigation systems such as loss of GPS lock, or through sensor payload malfunctions.
The staff and students who are dedicated to developing this technology are also available for deployment on a wide variety of missions. This provides our Research Group the opportunity to investigate new applications for the technology and gain field experience. All of the necessary equipment and safety gear for field deployment, including the UAS, ground station, and field tools are available. We can also coordinate logistical support for transportation to the site, including both vehicles and and watercraft.
The FAA regulations associated with UAS restrict their operation to persons who meet certain criteria, including a Class II medical clearance. Having skilled pilots and experienced ground station operators to safely conduct the mission reduces the chances for expensive and dangerous mishaps. More information about the current regulations can be found in our UF UAS Regulatory Guide [PDF].
A critical aspect of UAS technology is the need for post-mission data processing. The raw sensor data from the plane has limited utility - a typical mission will collect over a thousand individual images. For example, most researchers require a georegistered mosaic of the study area for in a GIS or Google Earth. This facilitates change analysis and the integration of the data with other sources.
Data processing involves a significant amount of computation and typically requires specialized software. We can provide these resources as well as students who are trained in the procedure to reduce your costs. A typical mission report [PDF] provides information regarding the attributes and accuracy of the data set. This information is valuable in analyzing the data and useful in explaining the data in research papers.
The specific objective of this research is to allow the unmanned system operator to designate a target area by generating a simple polygon over a pre-existing geospatial reference. The mission is then dynamically generated and continuously updates a waypoint-based flight plan incorporating data product specifications such as optical sensor resolution and accuracy. Updates from the algorithm will use sensory feedback data from navigation, imaging, and other integrated sensors to detect anomalous behavior and automatically develop corrective maneuvers.
Improving the design of sUAS for biological and environmental studies continues to be a central research topic, with five generations of airframe design over the past decade. Current efforts are geared toward optimizing the efficiency of the data collection process, primarily by improving aerodynamic efficiency at cruise. Improving the efficiency is challenging due to the constraints of cost, simplicity, robustness, and the specialized features required for ecological applications.
The current airframe version is the Mako, a 2.7 m wingspan airframe specifically designed to be operated from unimproved or wetlands areas. To overcome the inherent challenges involved with operating in these areas, the Mako was designed to be hand-launched and water-landable, eliminating the traditional runway or catapult requirements for UAVs in this size class. Building on experience gained from four previous generations of UAVs, the design goals focused on providing a stable amphibious platform that maximized flight endurance and ground coverage per flight. Providing these capabilities is a waterproof fuselage with a high-wing, v-tail configuration designed to protect the wing and tail surfaces from water immersion and impacts with brush upon landing in unimproved areas. The Mako was designed with a bolt-on three-piece wing and removable v-tail to enable compact transportation, with tool-free fasteners allowing for rapid field assembly and breakdown.
The primary mission of the UAS is the collection of high-resolution, directly georeferenced, visible-spectrum imagery. The sensor suite and payload controller developed for this platform has evolved to meet these goals as technology has improved. The following factors have been identified as critical to the design of the payload.
- Unified Payload Controller. Control of the payload in-flight is. This implies the use of a device that has direct control over all sensors and subsystems.
- Physical Constraints. The platform specifications given above limited the weight, size, and power consumption of the payload.
- Platform Agnostic. The payload and avionics of the plane should be mutually independent. That is, it should be possible to fly the plane without the payload and operate the payload without the plane. This is particularly important during design and testing phases, and also eliminates the need to engineer the payload to the level reliability required for avionics.
- Consumer Off-the-shelf Hardware. Modularity, extensibility, ease of replacement, and minimization and of development and cost all deterred the use of custom components.
- Remote (wireless) and direct (cabled) interfaces. It is an operational necessity to have remote access for command and communication with the payload, both for sensor control and status updates. The large data sets generated by the payload, however, preclude the sole use of wireless interfaces, so hardwired interfaces for post-misison data transfer and payload troubleshooting is necessary.
A core advantage of small UASs is their ability to survey inaccessible or dangerous territory with minimal deployment effort. From a technological standpoint, this capability is driven by direct georeferencing. This refers to the ability of the payload to use GPS and other navigation systems to remotely locate and accurately map the target without limited prior information about the survey site. This is a central component in the broader objective of an autonomic data collection system to complete missions without human intervention. Traditional remote sensing methods require ground control, or pre-identified points with known locations, that will be included in the survey. The ground control necessity eliminates the advantages of direct georeferencing.
The clear advantages of direct georeferencing are accompanied by the limitation that little or no confirmation of the accuracy of the remotely sensed data is available. That is, each dataset can report an estimate of the precision of the map generated, but that estimate is based solely on the data collected by the UAV itself. This implies that errors inherent to the UAVs measurement system are not easily detectable, which has important implications where the spatial measurements derived from the data set are used for engineering projects, or where the USACE is liable for the accuracy of the information. As a result, a central goal of the autonomic algorithm is to evaluate whether the data that is being collected will produce the desired output product.
The precision and accuracy of the sensor system must be fully characterized to provide a baseline against which the knowledge system constructed by the intermediate control layer can be evaluated. The accuracy of directly georeferenced products is dependent on the accuracy and reliability of the sensors and processing methods used to generate them. Careful assessment of the measurement system can provide confidence in the accuracy of the output product even when independent verification is not available. Understanding the errors and error bounds of the measurement system is imperative to the adoption of this technology for the USACE.