A Scalable Distributed Collision Avoidance Scheme for Multi-agent UAV systems

TL;DR

This paper introduces a scalable distributed collision avoidance method for multi-UAV systems using nonlinear model predictive control, enabling dense aerial swarms to operate safely and efficiently through shared trajectory planning.

Contribution

It presents a novel distributed collision avoidance scheme based on NMPC with an obstacle prioritization and a specialized solver, scalable to multiple UAVs in dense swarms.

Findings

Successfully tested with up to ten UAVs in laboratory experiments.

Achieved collision-free trajectories in dense aerial swarm scenarios.

Demonstrated real-time operation with high-frequency control commands.

Abstract

In this article we propose a distributed collision avoidance scheme for multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive control (NMPC),where other agents in the system are considered as dynamic obstacles with respect to the ego agent. Our control scheme operates at a low level and commands roll, pitch and thrust signals at a high frequency, each agent broadcasts its predicted trajectory to the other ones, and we propose an obstacle prioritization scheme based on the shared trajectories to allow up-scaling of the system. The NMPC problem is solved using an ad hoc solver where PANOC is combined with an augmented Lagrangian method to compute collision-free trajectories. We evaluate the proposed scheme in several challenging laboratory experiments for up to ten aerial agents, in dense aerial swarms.