1-10 of 72 results

  • Federal Aviation Administration - Aviation Ecosystem Cyber Security Data Science (CSDS)

    PI M. Ilhan Akbas

    CO-I Laxima Niure Kandel

    ​​To address these needs, the FAA NextGen Organization has established the CSDS research program with emphasis on discovery, assessment, adaptation, demonstration and transfer of cyber technology to enhance information cybersecurity for elements of the aviation ecosystem.

    Establishing cyber analytical capabilities that are common between various elements of the aviation ecosystem is an essential capability that needs to be developed and matured to allow efficient and synchronized use of common data sets, analytical tools and communication backbones across the entire aviation ecosystem. To address these needs, the FAA NextGen Organization has established the CSDS research program with emphasis on discovery, assessment, adaptation, demonstration and transfer of cyber technology to enhance information cybersecurity for elements of the aviation ecosystem. The research is focused on Artificial Intelligence and Machine Learning (AI/ML) techniques to address these aviation ecosystem cybersecurity needs using customizable algorithms and tools. Our collaborators in this project include Astronautics, Collins Aerospace, GE Aerospace,  Gulfstream Aerospace, Massachusetts Institute of Technology Lincoln Lab, The Boeing Company, The Port Authority of New York and New Jersey United Airlines.

    Categories: Faculty-Staff

  • Federal Aviation Administration (FAA): A11L.UAS.97: Propose Right-of-Way Rules for Unmanned Aircraft Systems (UAS) Operations and Safety Recommendations

    PI M. Ilhan Akbas

    The overall purpose of this project is to inform rulemaking and standards development regarding potential Right of Way (RoW) concepts for manned and unmanned aircraft in the low altitude environment. 



    The overall purpose of this project is to inform rulemaking and standards development regarding potential Right of Way (RoW) concepts for manned and unmanned aircraft in the low altitude environment. 

    There are various RoW standards, which apply to specific types of UAS. However, there is ambiguity for other UAS and rules have yet to be developed for interactions between two unmanned aircraft or for UAS swarms. RoW rules impact UAS Detect and Avoid (DAA) requirements and the development of industry standards. This research project explores RoW for diverse UAS operations and make safety-based recommendations for consideration by FAA and UAS standards bodies. Our collaborators in this project are University of North Dakota and University of Kansas.

    Categories: Faculty-Staff

  • Using Interpretable Artificial Intelligence (AI) for Validation of Autonomous Vehicle Decision Making in Simulation

    PI M. Ilhan Akbas

    ​Autonomous Vehicle Validation and Verification AV V&V testing produces multi-variate time series data as output, which is evaluated to determine testing coverage.

    Autonomous Vehicle Validation and Verification AV V&V testing produces multi-variate time series data as output, which is evaluated to determine testing coverage. The recent surge in interpretable Artificial Intelligence (AI) research has resulted in Python interfaces for modern interpretable AI implementations. In this project, various modern interpretable AI implementations will be applied to AV V&V testing data to interpret parameter impact, and generate an informative report of AV V&V scenario using data generated from a traffic simulator and AV V&V test scenarios.

    Categories: Faculty-Staff

  • PolyVerif: Open-Source Environment for Autonomous Vehicle Validation and Verification

    PI M. Ilhan Akbas

    ​Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem.

    Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem. PolyVerif is an open-source solution focused on V&V researchers with the objective of accelerating the state-of-the-art for AV V&V research. PolyVerif provides an AI design and verification framework consisting of a digital twin creation process, an open-source AV engine, access to several open-source physics-based simulators, and open-source symbolic test generation engines. PolyVerif’s objective is to arm V&V researchers with a framework which extends the state-of-the-art on any one of the many major axes of interest and use the remainder of the infrastructure to quickly demonstrate the viability of their solution. 

    Categories: Faculty-Staff

  • TurtleTech: Sea Turtle Surveillance By Edge Computing on Unmanned Aerial Vehicles

    PI M. Ilhan Akbas

    To better understand the behavior of multiple sea turtle species along Florida’s Space Coast, we teamed up with Northrop Grumman and the Brevard Zoo to launch a drone-based surveillance effort.

    To better understand the behavior of multiple sea turtle species along Florida’s Space Coast, we teamed up with Northrop Grumman and the Brevard Zoo to launch a drone-based surveillance effort. The Turtle Tech project, leveraging two different unmanned aircraft systems (UAS), aim to provide conservation insights by fine-tuning the operations and computer vision systems for identification of individual sea turtles, including their species, gender and even unique markings.

    Categories: Faculty-Staff

  • Incorporating ANSYS Simulation Tools Into Engineering Programs at Embry-Riddle Aeronautical University

    PI Fady Barsoum

    CO-I Arka Das

    CO-I Heidi Steinhauer

    CO-I William Engblom

    CO-I Chad Rohrbacher

    This project aims to introduce and implement ANSYS computer modeling and simulation tools into the Engineering Programs at Embry-Riddle.

    This project aims to introduce and implement ANSYS computer modeling and simulation tools into the Engineering Programs at Embry-Riddle. Utilizing ANSYS in the undergraduate curriculum significantly enhances learning outcomes. It allows students to visualize complex physical phenomena, providing clarity on theoretical concepts. Additionally, hands-on experience with the software aligns students with industry standards, preparing them for future careers. Project-based learning fosters essential problem-solving skills. Finally, interactive simulations boost student engagement, making engineering topics more appealing.

    Categories: Faculty-Staff

  • Novel Space Science Test via Adaptive Control and Integral Concurrent Learning Leveraging On-Orbit CubeSat Structural Identification

    PI Riccardo Bevilacqua

    The objective of this work is to create the basic science underpinning the structural testing and evaluation framework and control for deployable large spacecraft.

    The objective of this work is to create the basic science underpinning the structural testing and evaluation framework and control for deployable large spacecraft. Large space structures and those with high dimensional ratio between deployed and stowed configurations are extremely difficult to test on the ground. The AFRL’s Space Vehicle Directorate recently opened the new Deployable Structures Laboratory, or DeSeL, as evidence of a renewed interest towards these systems. DeSeL represents the state-of-the-art technology for on-the-ground experimentation of deployable systems. In particular, an active Gravity Off-Load Follower (GOLF) cart system is being currently developed, intended to have three degrees of freedom (attitude motion) which could foreseeably provide the capability for large low-frequency motions. The real capabilities of the GOLF system are yet to be determined, and this research effort will develop in parallel, assist, support and inform the development of this new facility at AFRL.

    New testing and evaluation science to identify these systems’ behavior and control them, that are robust to large uncertainties in the structural dynamics are then needed, and the first time they deploy on orbit is the ultimate test.

    We propose to obtain the objective by combining novel control and learning theory with ad-hoc experimental activities. The culmination of this effort will be a flight demonstration, where a CubeSat previously designed by the Advanced Autonomous Multiple Spacecraft (ADAMUS) laboratory will be modified in its design and perform autonomous on-orbit structural identification, control, and testing.

    The flight demonstration will be based on measuring the natural frequencies, damping ratios and vibration mode shapes via excitation of the spacecraft, using reaction wheels on the main hub and potentially distributed small thrusters on the flexible bodies, emulating the configuration of the AFRL’s Space Solar Power Incremental Demonstrations and Research Project (SSPIDR).

    Categories: Faculty-Staff

  • GNC Efforts in Support of the University of Floridas Research for the NASA Instrument Incubator

    PI Riccardo Bevilacqua



    The following tasks will be performed by one Ph.D. student and Dr. Bevilacqua (PI at ERAU), in support of the University of Florida’s proposal for the NASA’s Instrument Incubator Program (IIP):

    Year 1:

    • Drag-compensation and test mass control design. Adaptive control combined with integral concurrent learning will be investigated to estimate, in real-time, the effects of drag on the spacecraft, to enable precise control of the test mass inside it. The PI has successfully used this technique for drag-based spacecraft formation flight, where online estimation of the ballistic coefficient of an unknown vehicle is critical.
    • Support for drag-compensation thruster mapping. Lyapunov-based thruster selection principles, previously developed by the PI, will be used to simplify the thruster mapping problem, and prevent the use of any numerical iterations, to ease online implementation. An additional step will involve exploring the possibility to use adaptive + ICL control to also estimate the thrust errors and their misalignment.

    Year 2:

    • Spacecraft acceleration estimation based on S-GRS outputs. The test mass position and orientation are measured inside the sensor and the applied forces and torques on the test mass are known. How to use this information to optimally estimate the spacecraft acceleration and angular acceleration due to atmospheric drag remains a challenge. An approach based on a bank of Kalman (or Extended Kalman) Filters will be explored, possibly in iterative form, as previously done for spacecraft relative motion estimation by Dr. Gurfil at Technion and by the PI and one of his former students.

    Year 3:

    • Support for hardware-in-the-loop testing of the control system at UF. The PI and the PhD student will support experimentation at UF, to implement the above algorithms in hardware systems. The PI has over a decade of experience in on-the-ground testing of spacecraft GNC systems.

    Year 1-3:

    • Support for numerical simulation of the closed-loop system. High-fidelity orbital and attitude propagators will be used to test the algorithms developed. STK and NASA’s Spice will also be candidates for comparison.

    Categories: Faculty-Staff

  • CubeSats Hosting Flexible Appendages for On-Orbit Testing of Advanced Control Algorithms

    PI Riccardo Bevilacqua

    ​The objective of this work is to start the assembly of a CubeSat hosting specialized flexible appendages, taking inspiration from a previously designed spacecraft developed by the Advanced Autonomous Multiple Spacecraft (ADAMUS).

    The objective of this work is to start the assembly of a CubeSat hosting specialized flexible appendages, taking inspiration from a previously designed spacecraft developed by the Advanced Autonomous Multiple Spacecraft (ADAMUS). This CubeSat will eventually enable testing of ADAMUS’ developed spacecraft control algorithms on-orbit.

    Relevance to NASA: The innovation proposed herein lies in the ability to autonomously characterize and control complex space structures. This project will directly support NASA’s TA 4: Robotics and Autonomous Systems

    Categories: Faculty-Staff

  • A Machine Learning Based Transfer to Predict Warhead In-Flight Behavior from Static Arena Test Data

    PI Riccardo Bevilacqua

    The objective of this work is to combine high-fidelity numerical models with unique/ad-hoc experimental activities to strengthen basic science underpinning the test and evaluation framework for warhead fragmentation and fragments fly-out.

    Warhead fragmentation predictions are based on either numerical simulations or static arena tests where detonations occur in unrealistic conditions (not flying). The first methodology presents many shortcomings: there is no agreement on the state of the art for simulations, and many tools ignore important aspects such as gravity, aerodynamic forces and moments, and rigid body motion of different shape fragments. Numerical simulations are also lengthy and cannot be used as online/on-the-battlefield tools. The experimental approach is also extremely limited, as it does not reproduce the real-world conditions of a moving warhead.

    The objective of this work is to combine high-fidelity numerical models with unique/ad-hoc experimental activities to strengthen basic science underpinning the test and evaluation framework for warhead fragmentation and fragments fly-out. In particular, we will aim at combining the most advanced simulation capabilities with static experimental data, to obtain a transfer function predicting lethality and collateral damage of a given warhead in real-life conditions. Artificial neural networks and/or other machine learning tools (e.g., Random Forests) will be used to capture the underlying physics governing fragments dispersion under dynamic conditions, coming from NAVAIR’s Spidy software, and eventually combine this knowledge with real warhead characteristics, coming from the static test. This proposal is of high impact because of the existing gap in analytical tools to define and validate warhead fragmentation testing.

    The broader impact (long term) of this work may be a software tool that the warfighter can use on the field to rapidly assess the effects of the arsenal at his disposal. This tool will be equally beneficial to designers and testers within the Air Force and the DoD in general.

    Categories: Faculty-Staff

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