  This paper introduces a novel algorithm for multiagent offline trajectory
generation based on distributed model predictive control. Central to the
algorithm's scalability and success is the development of an on-demand
collision avoidance strategy. By predicting future states and sharing this
information with their neighbors, the agents are able to detect and avoid
collisions while moving toward their goals. The proposed algorithm can be
implemented in a distributed fashion and reduces the computation time by more
than 85% compared to previous optimization approaches based on sequential
convex programming, while only having a small impact on the optimality of the
plans. The approach was validated both through extensive simulations and
experimentally with teams of up to 25 quadrotors flying in confined indoor
spaces.
