Minimum Time Trajectory Generation for Bounding Flight: Combining Posture Control and Thrust Vectoring

Abstract

Biological fliers such as birds are known for their bounding flight maneuvers during which they fold their wings under their bodies to soar intermittently, or manipulate their inertial body dynamics to achieve challenging trajectories. This combination of thrust vectoring and body control allows biological fliers to optimize for a wide number of objectives - ranging from aerodynamic drag minimization to maneuverability. However, combined posture control and thrust vectoring still remains largely unexplored in the aerial robotics community. In this paper, we use a dynamical model of an aerial robot with articulated thrusters to generate minimum time trajectories under spatially varying constraints. To this end, we formulate an optimal control problem that is solved numerically using trapezoidal collocation. Our results indicate that combining posture control and thrust vectoring can enable flying through narrow and spatially varying geometries as well as decreasing maneuver time by careful manipulation of shape inputs.