Maneuver-Based Autonomous Navigation of a Small Fixed-Wing UAV

An urban operation of unmanned aerial vehicles (UAVs) demands a high level of autonomy for tasks presented in a cluttered environment. While fixed-wing UAVs have been well suited for long-endurance missions at a high altitude, their navigation inside an urban area brings more challenges in motion planning and control. The inability to hover and low agility in motion cause more difficulties on planning a feasible path in a compact region, and a limited payload allows only low-grade sensors for state estimation and control. We address the problem of achieving autonomous navigation of a small fixed-wing using low-cost sensors such as GPS, IMU and camera. Based on a set of primitive maneuvers integrated with flight controllers, we present a motion planning method that produces dynamically feasible plans in a low dimensional search space. To deal with motion uncertainty due to wind and imperfect control, Markov Decision Process is formulated to compute the most probable path to a target. A minimum target observation time is also imposed on the motion plan to consider a camera's limited field-of-view. In experiments, we demonstrate that a 1m wing-span air-plane achieves an "air slalom" task in which the UAV must search, localize and pass through multiple gates autonomously.