  We present a distributed model predictive control (DMPC) algorithm to
generate trajectories in real-time for multiple robots. We adopted the
\textit{on-demand collision avoidance} method presented in previous work to
efficiently compute non-colliding trajectories in transition tasks. An
event-triggered replanning strategy is proposed to account for disturbances.
Our simulation results show that the proposed collision avoidance method can
reduce, on average, around 50% of the travel time required to complete a
multi-agent point-to-point transition when compared to the well-studied
Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success
rate in transition tasks with a high density of agents, with more than 90%
success rate with 30 palm-sized quadrotor agents in a 18 m^3 arena. The
approach was experimentally validated with a swarm of up to 20 drones flying in
close proximity.
