Event-triggered and distributed model predictive control for guaranteed collision avoidance in UAV swarms

TL;DR

This paper introduces an event-triggered distributed model predictive control approach for UAV swarms that offloads computations to ground units, reducing onboard weight and power while ensuring collision-free trajectories.

Contribution

It develops a novel event-triggered DMPC framework that selects relevant trajectories for replanning, enhancing robustness and efficiency in UAV swarm path planning.

Findings

Reduces network traffic by 60% in simulations.

Guarantees feasible, collision-free trajectories for UAVs.

Demonstrates effectiveness in hardware-in-the-loop experiments.

Abstract

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.