Robust Hybrid Control for Autonomous

The operation of an autonomous vehicle in an unknown, dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities, and to react in real time to changes in the operational environment. A possible approach to reduce the computational complexity of the motion planning problem for a nonlinear, high dimensional system, is based on a quantization of the system dynamics, leading to a control architecture based on a hybrid automaton, the states of which represent feasible trajectory primitives for the vehicle. This paper focuses on the feasibility of this approach: the structure of a Robust Hybrid Automaton is deened and its properties are analyzed. Algorithms are presented for time-optimal motion planning in a free workspace, and in the presence of xed or moving obstacles. A case study involving a small autonomous helicopter is presented: a nonlinear control law for maneuver execution is provided, and a robust hybrid automaton is constructed. Simulations showing the eeectiveness of the approach are presented and discussed.