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

arXiv: https://arxiv.org/abs/2206.11020 · HTML: https://mlsanity.com/paper/2206.11020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2022.

## Built on by (most cited first, 3 of 3)

- [Distributed Model Predictive Control with Adaptive Safety Zones for Multi-Fleet Drone Operations](https://mlsanity.com/paper/2606.20651/) (2026)
- [Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization](https://mlsanity.com/paper/2510.09204/) (2025)
- [Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control](https://mlsanity.com/paper/2609.13270/) (2026)

## Abstract (arXiv)

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.