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Successful operation of next-generation unmanned air vehicles will demand a high level of autonomy. Autonomous low-level operation in a complex environment dictates a need for onboard, robust, reliable and efficient trajectory optimization. In this report, we develop and demonstrate an innovative combination of traditional analytical and numerical solution procedures to produce efficient, robust and reliable means for nonlinear flight path optimization in the presence of time-varying obstacles and threats. The trajectory generation problem is first formulated as an optimization problem using reduced-order dynamics that result from the natural time-scale separation that exists in the aircraft dynamics. Terrain information is incorporated directly into the formulation of the reduced-order dynamics, which significantly reduces the computational load and leads to a path planning solution that can be implemented in real-time. Various cases of terrain, pop-up obstacles/threats, and targets are simulated. A representative optimal trajectory is generated with in a high fidelity full-order nonlinear aircraft dynamics and compared with a solution obtained from a reduced-order optimization. The developed algorithm is flight demonstrated with a fixed-wing unmanned aircraft test-bed in which a neural network-based adaptive autopilot is integrated with the on-line trajectory optimization algorithm., Prepared in cooperation with with the Georgia Institute of Technology and the University of Stuttgart. The original document contains color images.

Successful operation of next-generation unmanned air vehicles will demand a high level of autonomy. Autonomous low-level operation in a high-threat environment dictates a need for on-hoard, robust, reliable and efficient trajectory optimization. in this report, we develop and demonstrate an innovative combination of traditional analytical and numerical solution procedures to produce efficient, robust and reliable means for nonlinear Light path optimization in the presence of time-varying obstacles and threats. The solution procedure exploits the natural time-scale separation that exists in the aircraft dynamics using singular perturbation theory. A reduced order problem involving only the kinematics of the position subspace is treated numerically. The nonlinear aircraft dynamics are to be treated analytically in phase II using a boundary layer analysis that results in an optimal feedback guidance solution. The developed algorithms were coupled with a neural network adaptive autopilot and integrated in an existing unmanned test-bed. This report documents the phase I effort, which produced a demonstration of the developed algorithm in near-real-time flight simulation, and included a simple evaluation of tracking computed trajectories on a rotary wing UAV.