  Distributed model predictive control (DMPC) is often used to tackle path
planning for unmanned aerial vehicle (UAV) swarms. However, it requires
considerable computations on-board the UAV, leading to increased weight and
power consumption. In this work, we propose to offload path planning
computations to multiple ground-based computation units. As simultaneously
communicating and recomputing all trajectories is not feasible for a large
swarm with tight timing requirements, we develop a novel event-triggered DMPC
that selects a subset of most relevant UAV trajectories to be replanned. The
resulting architecture reduces UAV weight and power consumption, while the
active redundancy provides robustness against computation unit failures.
Moreover, the DMPC guarantees feasible and collision-free trajectories for UAVs
with linear dynamics. In simulations, we demonstrate that our method can
reliably plan trajectories, while saving 60% of network traffic and required
computational power. Hardware-in-the-loop experiments show that it is suitable
to control real quadcopter swarms.
