  This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework
exploiting a Deep Neural Network for processing onboard-captured depth images
for collision avoidance in trajectory-tracking tasks with UAVs. The network is
trained on simulated depth images to output a collision score for queried 3D
points within the sensor field of view. Then, this network is translated into
an algebraic symbolic equation and included in the N-MPC, explicitly
constraining predicted positions to be collision-free throughout the receding
horizon. The N-MPC achieves real time control of a UAV with a control frequency
of 100Hz. The proposed framework is validated through statistical analysis of
the collision classifier network, as well as Gazebo simulations and real
experiments to assess the resulting capabilities of the N-MPC to effectively
avoid collisions in cluttered environments. The associated code is released
open-source along with the training images.
