On-Line Trajectory Optimization for Autonomous Air Vehicles

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-board, robust, reliable and efficient trajectory optimization. The proposed effort will 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 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 will be treated numerically. The nonlinear aircraft dynamics will be treated analytically in a boundary layer analysis that results in an optimal feedback guidance solution. The developed algorithms will be coupled with a neural network adaptive autopilot and integrated in an existing unmanned testbed. Phase I produced a demonstration of the developed algorithm in near-real-time flight simulation. Phase II will expand the scope of mission scenarios that can be addressed, will produce flight code validated in real-time hardware-in-the-loop simulation, and culminate in close range flight demonstration of the developed technology on a fixed-wing unmanned aircraft testbed.