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 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.