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Title page for ETD etd-07292005-161842

Type of Document Master's Thesis
Author Clifton, Charles Ali
Author's Email Address chipclifton@yahoo.com
URN etd-07292005-161842
Title Hybrid System Based Design for the Coordination and Control of Multiple Autonomous Vehicles
Degree Master of Science
Department Electrical Engineering
Advisory Committee
Advisor Name Title
T. John Koo Committee Chair
George E. Cook Committee Co-Chair
  • hybrid system model-based
Date of Defense 2005-08-12
Availability unrestricted
Hybrid System Based Design for the Coordination and Control of Multiple Autonomous Vehicles

Charles A. Clifton

Thesis under the direction of Professor Takkuen John Koo

In recent years, the use of unmanned aerial vehicles (UAVs) has gained considerable attention for applications where manned operation is considered dangerous or infeasible. As the number of UAVs in operation rises, it will become necessary to coordinate these vehicles. It can be shown that a real-time system can be modeled using a hybrid automaton provided that certain guarantees can be made about the temporal properties. By using the hybrid automata to model the system composed of a multi-modal dispatcher and waypoint/motion controller in addition to a real-time UAV controller, we show that the hybrid system can bisimulate a timed automata model created using a tool called UppAal, which can verify specifications about a given system. We thereby coordinate multi-robot movement while ensuring that certain constraints have not been violated.

In this thesis, we present a methodology for developing autonomous vehicle controllers using a model-based approach and a hybrid automata to represent the control system, which features both linear state feedback control and nonlinear control. We derive a linear model of the physical system by performing system identification and employ a Kalman Filter to obtain state estimates for feedback purposes. Furthermore, we explain how this can be constructed from a system containing both discrete-time linear and continuous-time nonlinear subsystems. In addition, we present the Vanderbilt Embedded Computing Platform for Autonomous Vehicles (VECPAV), an end-to-end design platform for the rapid development and deployment of control and motion planning solutions for autonomous vehicles. The automated development platform greatly speeds the controller and system development and deployment phases by reducing the programming and compilation burden on the lab researchers, and eliminating the risks associated with translating code manually.

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