  In this article we propose a distributed collision avoidance scheme for
multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive
control (NMPC),where other agents in the system are considered as dynamic
obstacles with respect to the ego agent. Our control scheme operates at a low
level and commands roll, pitch and thrust signals at a high frequency, each
agent broadcasts its predicted trajectory to the other ones, and we propose an
obstacle prioritization scheme based on the shared trajectories to allow
up-scaling of the system. The NMPC problem is solved using an ad hoc solver
where PANOC is combined with an augmented Lagrangian method to compute
collision-free trajectories. We evaluate the proposed scheme in several
challenging laboratory experiments for up to ten aerial agents, in dense aerial
swarms.
