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Division of Research

FAU Autonomous Systems Research & Education

The use of unmanned autonomous vehicles whether in the air, on the water or under it, has grown exponentially over the last decade. Researchers across FAU’s six campuses are using these advance technologies as a tool to conduct research and teach. They’re also working on drone innovations that will allow scientists to conduct even more cutting-edge work in disciplines that touch our daily lives, from biomedicine to infrastructure safety and security and from water quality to the Internet of Things.

For more information on the Autonomous Systems Research Interest Group, contact Karin Scarpinato, Ph.D.

Here’s a sampling of some of the projects our faculty are working on:

Click on faculty member's name or title to view project information.

Laurent Chérubin

Recent seagrass die offs, harmful algal blooms, and morbidity and mortality in marine mammals and fish in the Indian River Lagoon (IRL) underscore the need for an improved understanding of toxic water fate and clean water exchanges between the IRL and the coastal ocean. Throughout the entire IRL, only three inlets allow for water renewal from the coastal sea. Understanding the ventilation rates of the IRL through these inlets is critical.

To expand our capacity to observe and monitor the coastal environment by providing operational capability to deploy both surface and aerial sensors for real-time event monitoring in Florida, FAU HBOI Associate Professor L. Chérubin acquired a Tempest™ unmanned aircraft system (UAS) equipped with a 6-channel multispectral visible/NIR camera (mini-MCA from TetraCam) and filters to retrieve concentrations of chlorophyll-a fluorescence (chl-a), total suspended matter (TSM), and possibly chromophoric dissolved organic matter (CDOM), as well as benthic substrate types with spatial resolution of less than 1 m to assess water mass distribution and respective optical properties.

Our major objective is to evaluate residence and flushing times, and water renewal processes, whether driven by tides or wind or rainfall and runoff by combining both in-situ and aerial measurements. Our method has the advantage of being applicable to similar estuarine/lagoonal systems in Florida and around the world. The study will help to develop a general strategy for investigating exchanges between semi-enclosed basins and coastal seas.

By collecting in-situ inherent optical property data we will validate the aerial multispectral water-leaving radiance data to enable algorithm development specific to the IRL. The aerial imagery from our aerial surveys (Less than 1 m spatial resolution) will be used to provide the water-leaving radiance data that will be converted using our specific algorithm to chl-a, TSM, and possibly CDOM.

Several graduate students in the FAU’s Department of Geosciences are involved in this project. Their participation includes field mission planning, drone piloting, image analysis, in-situ field surveys and data analysis. Our students are certified FAA drone pilots and operate the drone within the area approved in the CoA (2015-ESA-104-COA).

drone support art
Figure 1 Mosaicked images of Lakewood Regional Park from UASUSA Tempest. The green triangles show individual images along with their elevation, angle, and trajectory.

1 The Certificate of Authorization (COA) has been received on May 08 2017.

drone support art
drone support art

Sudhagar Nagarajan, Ph.D.

The recent advancement in drone technology has encouraged the engineering and scientific community to experiment UAV (Unmanned Aerial Vehicles) for 3D mapping applications.


There are variety of off-the-shelf UAVs equipped with cameras (visible, multispectral and hyperspectral) are available in the market for an affordable price.


There are also various software options available in the market to process and produce results that are acceptable for the projects where accuracy requirements are low. However, for mapping of vegetated areas and low contrast beaches, cameras are not sufficient. Due to challenges in performing image matching, 3D models derived for such terrains are coarse and unreliable.

LiDAR sensors

Hence the option of using LiDAR sensors in UAV platforms will be inevitable in the future. The challenges in using LiDAR sensors in UAV platforms include payload, cost and need of a direct geo-referencing procedure. Unlike frame based cameras, geo-referencing of LiDAR point cloud cannot be achieved with control points or 1 or 2 HZ of GPS data alone. Instead a direct geo-referencing technique that uses high-accuracy GPS and Inertial Measurement Unit (IMU) are required. This presentation will talk about our experience in developing a UAV based LiDAR Mapping System along with its applications in 3D infrastructure mapping and monitoring, environment monitoring and coastal erosion.

Imad Mahgoub, Ph.D.

Current work involves Wireless Access in Vehicular Environment (WAVE) compliant UAV platform and its integration with our exiting WAVE compliant vehicular network platform. [The development of our existing platform was supported by a National Science Foundation grant (NSF MRI Grant#1229616)].
This integrated platform supports the deployment of a vehicular network where nodes can be ground vehicles or UAVs. The integrated platform will take drone utilization to the next level and will increase the utility of vehicular networking technologies.

Tecore Networks

It will be used by our Smart Drive research team at Tecore Networks Lab to develop and validate new smart and adaptive communication protocols and applications for vehicular traffic management, road safety enhancement, and environment monitoring, to mention a few.

Learning experience

The platform will also become part of the students’ learning experience in the engineering design process. We plan to demo the concepts taught in both undergraduate and graduate classes in the following areas.
  • Computer Engineering
  • Electrical Engineering
  • Computer Science and conduct experiments on Transmission Protocols
  •     Mobile Swarm Communications
  •     Resource Management
  •     Autonomous Vehicle Communication, and more

Target courses include:

  • CNT 4104: Data Communications
  • CNT 6517: Mobile Computing
  • CNT 6528: Vehicular Networks

Fraser Dalgleish, Ph.D.

Currently hold a COA with FAA for Tempest (3m fixed-wing fully autonomous UAS) transects between Ft Pierce and Sebastian Inlets in collaboration with Cherubin.
Also operate a DJI Phantom IV for research purposes.
Sensor research in Lidar and active/passive hyperspectral sensors for UAS and AUV applications. Co-PI on AFOSR grant with Ouyang for compressive sensing lidar imager for UAS applications. Recent journal paper published in JARS.

Marianne Porter, Ph.D.

This project aims to gain a better understanding of one of the most massive shark migrations in the Western Atlantic. Blacktip sharks exhibit a well-known, but poorly studied, seasonal migration along the US eastern seaboard. In their overwintering grounds in Southeast Florida, they form massive aggregations in nearshore waters from January-April. An aerial drone will be used to capture high resolution video footage of blacktip sharks swimming individually, in small groups, and in large aggregations in the clear waters off Palm Beach County in Southeast Florida. The blacktip sharks are found within a few meters of the shoreline and surface distortion on calm days is minimal. A frame-by-frame video analysis will provide data on tail beat frequency and amplitude, body curvature, velocity, and acceleration for both straight swimming and turning maneuvers. This will be the first time that volitional swimming will be quantified in the wild.

Jiannan Zhai, Ph.D.

I-SENSE developing UAV end-to-end monitoring and control

Recent developments in wireless sensor networks have made real-time UAV monitoring, control and data collection possible.
The Institute for Sensing and Embedded Network Systems Engineering at FAU has developed an end-to-end monitoring and control system to support UAV research and management of UAV resources.

The system architecture spans four layers:

  • A wireless sensing framework for integrating sensing instruments into UAVs;
  • A multi-tier sensing fabric based on WiFi, cellular, and mesh-networking technologies;
  • Real-time streaming middleware for processing, annotation, and archival of UAV sensor data;
  • Front-end applications for monitoring and management UAV sensor observations and system health. This end-to-end system exhibits features suitable for real-time monitoring and management of UAV systems and can be easily adapted by UAV applications.

drone support art

Sergio Gonzalez

Uses a Parrot Bebop 2 to collect data related to his dissertation which focuses on aquatic community structure as it responds to seasonal changes in habitat structure. Currently using an off-the-shelf, ready-to-fly sUAS for the purposes of mapping pond and wetland perimeters at Jonathan Dickinson State Park. The Parrot Bebop 2 is compact, fits in a backpack, and is easy to operate. These features are crucial for a biologist in the woods. The FPV glasses are worn to avoid glare issues in the bright sunlight and for a better perspective when avoiding trees.

Elan Barenholtz, Ph.D.

Current work involves AI for autonomous UAV navigation and deep learning for computer vision and olfaction for onboard detection of environmental hazards and events.

Jason Hallstrom, Ph.D.

Team is interested in the use and augmentation of drones and their supporting infrastructure for indoor and outdoor sensing.

aphipps@fau.edu   |  561.297.3970

Allan Phipps

  • Flying in to Code project - using Parrot mini-drones, looking at student interest in computer science after learning how to program simple indoor drone flight.
  • MAST project (Marine Autonomous Survey Technology) - developing a tethered drone to support shark surveys and research conducted on shark acoustic response
  • FAUUAV drone programming challenge
  • Drone curriculum development, particularly as it relates to autonomous flight - Drone sensing
  • Roboboat competition an its autonomous drone component - Drones and image processing

Joshua Voss, Ph.D.

Aerial mapping of coral reef ecosystems has been consistently highlighted as a priority for research, leading to the development of many remote sensing and unmanned aerial vehicle (UAV) technologies. In support of ongoing monitoring and experimental work at St. Lucie Reef in Southeast Florida, the Voss Lab at FAU Harbor Branch acquired a DJI Inspire 1 aerial quadcopter for high definition imaging of freshwater discharge events in the St. Lucie Inlet and adjacent reef areas.

Recognizing the versatility of this UAV platform, we have expanded beyond freshwater tracking and completed georeferenced aerial surveys in 5 discrete habitat types: salt marsh, oyster reefs, dragline ditch restoration sites, estuary/inlets, and coral reefs. In addition to the data generation capacity of UAVs, we’ve expanded use of video and storytelling to expand the education outreach and impact of our research.

To date we have successfully completed 6 outreach videos which have been posted on YouTube and distributed through social media, ultimately generating more than 10000 online views. These videos have capture media attention and influenced passage of three bills in the 2017 Florida Congressional session designed to help improve water quality on coral reefs in South Florida. Similarly, one of these videos has been used by the Flower Garden Banks National Marine Sanctuary to support a proposed expansion of the sanctuary boundaries off the coast of Texas. The videos can be viewed here.

Meeting & Events

May 19, 2017
The Steering Committee for UAV Research & Education met for the first time.

June 16, 2017
This Division of Research event featured presentations, education initiatives, discussions and more focused on Autonomous Systems research at FAU. To view the agenda, click here.

 Last Modified 10/24/17