Hybrid control for aggressive maneuvering of autonomous aerial vehicles

New advances in control theory are required to enable aggressive maneuvering of autonomous vehicles, while adapting in real time to changes in the operational environment. A hybrid control architecture, the states of which represent feasible trajectory primitives, is constructed to reduce the complexity of the motion-planning problem for a nonlinear, high-dimensional system such as an aerial vehicle. Any feasible trajectories in the primitive list are available to the automatic control system; these may include a complete set of transitions between pairs of trim trajectories in addition to pilot-inspired behaviors recorded during manual flight tests with a human pilot. This paper describes the structure of a hybrid automaton that solves a time-optimal motion-planning problem by sequencing maneuvers in real time from such a primitive list. The algorithm can be used in a free workspace, or in the presence of fixed or moving obstacles. We present simulation results showing the effectiveness of this approach for a behavior library generated by a combination of analysis and live flight tests with a small remote-controlled helicopter