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UAV Research at the University of Cincinnati

Explore the UAV MASTER Lab's cutting-edge research in unmanned aerial systems, including intelligent operations, resource allocation, and sUAS swarming.

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UAV Research at the University of Cincinnati

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  1. Dr. Kelly CohenInterim Head & ProfessorDepartment of Aerospace Engineering & Engineering MechanicsNovember 3, 2017 UAV Research at the University of Cincinnati

  2. UAV MASTER Lab Co-Directors: Profs. Kelly Cohen & Manish Kumar • Over 2000 sq. ft. (Indoor Flight Test facility) • 10 Phd, 9 MS graduate students, 6 Undergraduate students • In collaboration with the Ohio Indiana UASC (Unmanned Aerial Systems : • 11 FAA approved COA’s for outdoor flight tests at Wilmington Airpark (6 Sq. Km @ max. altitude 1000 ft.) & West Virginia Forestry - 5 VTOL configurations: single rotor, 2 x quad, hexacopter, Octacopter.

  3. Intelligent Operations Exploiting UAVs for Emergencies Accurate Situational Awareness • Effective, real-time, dynamic assessment of vulnerabilities (potential for trouble)and failure modes • Unmanned cargo aircraft for aerial delivery, transport, operations • Remote intervention using UAVs with robotic arms • Hybrid (air/land), amphibian (water/land) and “Flying Fish” operations • AR/VR for UAV Operation doctrine development & training • Adaptive, bio-inspired, collaborative robotic operations Resource Gathering and Allocation Information Visualization & Dissemination Intelligent & Capable Drone Platforms Doctrine Development & Training

  4. sUAS Swarming: Team & Motivation • Members: Kelly Cohen (lead), Bryan Brown, Manish Kumar, Nathaniel Richards, Anthony Lamping, Nick DeGroote • Motivation: • The effective management of multiple UAVs required enhanced improvement human and system performance, which may be achieved using higher levels of autonomy as well as using a single operator control of multiple unmanned vehicles. • In addition to multiple UAV control as UAVs need integration in the national airspace, adding an ADS-B system • Since this information can also be received by other UAVS, providing situational awareness and allowing self-separation, it is a crucial enabling technology for higher levels of automation and swarming.

  5. Swarming Deliverables • Ability to control multiple UAVs and assign each tasks through one Ground Control Station • Ability to have multiple UAVs avoid other aircraft equipped with ADS-B • End product is 3-6 flight controllers equipped with ADS-B, an onboard computer and intelligent algorithms • Flight controllers can be taken to other vehicles that our Users wish to purchase in the future

  6. Develop • Creating a mission

  7. Evaluate

  8. Hierarchical Fuzzy Systems Courtesy Tim Arnett

  9. Genetic Fuzzy Trees - Strengths Genetic Fuzzy Trees • Deep Learning (Adaptable to untrained scenarios / patients) • Extremely Resilient to Uncertainties / Randomness • Higher Performance (compared to neural networks and many other machine learning methods) • Extreme Computational Efficiency • Easy to design • Final Controllers: Deterministic and Transparent • Able to be Verified and Validated using formal methods! (unlike neural networks) • Nearly Universally Applicable

  10. ALPHA Assessment • Alpha was initially assessed by AFRL Consultant Gene “Geno” Lee • USAF Colonel (retired) • USAF Weapon School graduate, Former USAF Battle Manager and Adversary Tactics Instructor • Has flown in thousands of air-to-air intercepts as a Ground Control Intercept officer, as a Mission Commander on AWACS, and in the cockpit of multiple fighter aircraft • Worked with Psibernetix, teaching us air combat tactics • Geno took manual control of the blue aircraft against previous AI and easily defeated it • However, even after repeated attempts against the more mature version of ALPHA, not only could he not score a kill against it, he was shot out of the air every time after protracted engagements • He described ALPHA as “the most aggressive, responsive, dynamic and credible AI (he’s) seen-to-date” Distribution A: Approved for public release; distribution unlimited. 88ABW Cleared 05/02/2016; 88ABW-2016-2270 Distribution approved for public release; 88ABW Cleared 10/05/2016; 88ABW-2016-4963

  11. Veteran F-16 Pilot & Col. (Ret) Gene Lee who assessed the Genetic Fuzzy based AI describes it as “the most aggressive, responsive, dynamic and credible AI he’s seen to date”

  12. Fuzzy Clustering Approach for Multisensor-Multitarget Environments Nicholas Hanlon, Best PhD Thesis Jackson Award Mentor – Prof. Kelly Cohen

  13. INPUTS ARGOS OUTPUTS • Number of Targets • Unmanned • Manned • Linear • Maneuvering • Density Level • Performance Metrics • Probability of Correct Association • Robustness • Correlated vs Non-correlated tracks • Sensitivity • Sensor uncertainty • Process noise • Comparison: # of sensors to target RMS error Monte Carlo Simulation • Number of Sensors • Probability of Sensor Failure • Probability of Detection • Probability of False Alarms • Measurement Uncertainty • Algorithms • 2-D Assignment Algorithm • S-D Assignment Algorithm • Two-Sensor Likelihood • Sequential Pairwise Likelihood • All Pairwise Likelihood • Generalized Likelihood • Diffuse Prior Likelihood • Hierarchal Clustering • Fuzzy Clustering • Trackers • Single Mode KF • IMM • Scalability Algorithms >

  14. Simulation Framework Environment Size Simulation Length Monte Carlo Runs Preview Sensor Setup Target Setup

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