  This article proposes a Novel Nonlinear Model Predictive Control (NMPC) for
navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV). The
proposed NMPC formulation allows for a fully parametric obstacle trajectory,
while in this article we apply a classification scheme to differentiate between
different kinds of trajectories to predict future obstacle positions. The
trajectory calculation is done from an initial condition, and fed to the NMPC
as an additional input. The solver used is the nonlinear, non-convex solver
Proximal Averaged Newton for Optimal Control (PANOC) and its associated
software OpEn (Optimization Engine), in which we apply a penalty method to
properly consider the obstacles and other constraints during navigation. The
proposed NMPC scheme allows for real-time solutions using a sampling time of 50
ms and a two second prediction of both the obstacle trajectory and the NMPC
problem, which implies that the scheme can be considered as a local
path-planner. This paper will present the NMPC cost function and constraint
formulation, as well as the methodology of dealing with the dynamic obstacles.
We include multiple laboratory experiments to demonstrate the efficacy of the
proposed control architecture, and to show that the proposed method delivers
fast and computationally stable solutions to the dynamic obstacle avoidance
scenarios.
