Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision
This work addresses the challenge of autonomous post-stall navigation for fixed-wing UAVs in urban environments, representing an incremental improvement by integrating vision-based mapping into existing NMPC frameworks.
The paper tackled the problem of enabling fixed-wing UAVs to navigate in constrained spaces without prior maps or ground truth state measurements by developing a direct NMPC approach that uses onboard stereo vision with NanoMap for point-cloud mapping, resulting in successful simulation and hardware demonstrations including navigation around a building using a 42-inch UAV.
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). However, this approach has been limited to a priori known maps and ground truth state measurements. In this paper, we present a direct NMPC approach that leverages NanoMap, a light-weight point-cloud mapping framework to generate collision-free trajectories using onboard stereo vision. We first explore our approach in simulation and demonstrate that our algorithm is sufficient to enable vision-based navigation in urban environments. We then demonstrate our approach in hardware using a 42-inch fixed-wing UAV and show that our motion planning algorithm is capable of navigating around a building using a minimalistic set of goal-points. We also show that storing a point-cloud history is important for navigating these types of constrained environments.