PhD Defense by Youngjun Choi

Event Details
  • Date/Time:
    • Tuesday June 21, 2016
      1:00 pm - 3:00 pm
  • Location: Weber Space Science and Technology Building (SST-II): Collaborative Visualization Environment (CoVE)
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Summary Sentence: Improvements in Modeling and Simulation of Control, Navigation, and Surveillance for Unmanned Aircraft Separation Assurance

Full Summary: No summary paragraph submitted.

Ph.D. Thesis Defense

 

By

 

Youngjun Choi

(Advisor: Prof. Dimitri N. Mavris)

1:00 PM, Tuesday, June 21, 2016

Weber Space Science and Technology Building (SST-II)

Collaborative Visualization Environment (CoVE)

 

Improvements in Modeling and Simulation of Control, Navigation, and Surveillance for Unmanned Aircraft Separation Assurance

 

ABSTRACT:

 

The integration of Unmanned Aircraft Systems in the National Airspace System (UASNAS) problem has received much attention because of the growing number of variety of mission types and the rapid growth of UAS market. Among the many challenging UASNAS problems, separation assurance is considered to be particularly complex, having many interactions among the elements in different levels of abstraction and coupling effects between the different disciplinary domains. In order to explore the separation assurance problem, an analytic model should capture diverse operational scenarios, vehicle dynamics, and subsystem  functions such as sensor/surveillance, control, navigation and communications. This has major implications on the analytic model requirements, especially in regard to modeling scope, resolution (or fidelity), and computational expense.  

 

The objective of this thesis is to formulate and demonstrate improvements in modeling and simulation of fully integrated UAS to enable systems analysis across the levels of abstraction and multiple disciplines. This work also quantitatively characterizes collision avoidance as a critical element of separation assurance in terms of system behaviors across the levels of abstraction and multiple disciplines. To address these objectives, this thesis contributes to four areas: (1) a statistical gain-scheduling method to improve computational efficiency without a loss of accuracy or fidelity, (2) a hybrid collision avoidance algorithm using a machine learning technique that improves computational runtime as well as optimal trajectory cost, (3) a two-layer obstacle avoidance algorithm for a multi-obstacle environment, (4) a rapid, data-driven and grid-based urban modeling methodology using airborne LiDAR sources.

The proposed modeling and simulation capability provides insights into the interaction between system of systems, systems, and subsystems that cannot be characterized by a conventional modeling and simulation environment. To illustrate the collision avoidance problem, this thesis examines the navigation of a fixed wing UAV in a dense urban environment.

 

COMMITTEE: 

Prof. Dimitri N. Mavris, School of Aerospace Engineering

Prof. Daniel P. Schrage, School of Aerospace Engineering

Prof. Eric Feron, School of Aerospace Engineering

Dr. Hernando Jimenez, School of Aerospace Engineering

Prof. John Valasek, School of Aerospace Engineering (Texas A&M University)

 

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Phd Defense
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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jun 14, 2016 - 9:00am
  • Last Updated: Oct 7, 2016 - 10:18pm