Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs

Abstract

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited with regards to maneuverability. In this paper, we present a nonlinear control approach for enabling aerobatic fixed-wing UAVs to maneuver in constrained spaces. Our approach utilizes full-state direct trajectory optimization and a minimalistic, but representative, nonlinear aircraft model to plan aggressive fixed-wing trajectories in real-time at 5 Hz across high angles-of-attack. Randomized motion planning is used to avoid local minima and local-linear feedback is used to compensate for model inaccuracies between updates. We demonstrate our method in hardware and show that both local-linear feedback and re-planning are necessary for successful navigation of a complex environment in the presence of model uncertainty.

Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

“…In some cases, especially in the presence of high winds, NMPC has been utilized to control fixed-wing UAVs [27]. In [1], a receding-horizon NMPC approach was demonstrated that enabled fixed-wing UAVs to execute poststall maneuvers to navigate constrained environments.…”

“…Here, we build on the work done in [1] and develop an NMPC-based navigation approach capable of running in realtime using stereo vision. Our method is distinct from other approaches in that it allows seamless exploitation of the full flight envelope for navigation via NMPC, leverages a pointcloud map to maintain a history of sensor data, and provides a means for handling uncertainty in sensor data and state estimates.…”

“…Traditionally fixedwing UAVs have been hindered by limited maneuverability and restricted to large open spaces. Recent research has shown that aerobatic fixed-wing UAVs are capable of navigating through complex environments by exploiting the full flight envelope [4], [1]. By operating beyond the conventional flight regimes, there is an opportunity to achieve both range and maneuverability in a single vehicle.…”

“…In [1], the authors demonstrated that a fixed-wing UAV could navigate a narrow corridor by executing post-stall turns via nonlinear model predictive control (NMPC). While this approach proved a promising means to increase fixedwing maneuverability, it required an a priori map of the environment.…”

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Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to maneuver in constrained spaces through the use of direct nonlinear model predictive control (NMPC) [1]. However, this approach has been limited to a priori known maps and ground truth state measurements.

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