Fast Swarming of UAVs in GNSS-denied Feature-poor Environments without Explicit Communication

TL;DR

This paper presents a decentralized UAV swarm method for rapid cooperative flight in feature-poor, GNSS-denied environments without external localization or communication, using onboard perception and estimation techniques.

Contribution

It introduces a novel neighborhood model and an enhanced state estimation strategy that enable fast, reliable flocking without explicit communication, validated through real-world experiments.

Findings

Successful UAV swarm flight in GNSS-denied environments

Effective onboard mutual perception and flocking control

Achieved near hardware velocity limits in experiments

Abstract

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to decrease the inter-agent oscillations common in standard reactive swarm models employed in fast collective motion. The novel swarming methodology is supplemented with an enhanced Multi-Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MRSE and the neighborhood model may rely on information exchange between agents, we introduce a communication-less version of the swarming framework based on estimating communicated states to decrease dependence on the often unreliable communication networks of large swarms. The proposed solution has been verified by a set of complex real-world experiments to demonstrate its overall capability in different conditions, including a UAV interception-motivated task with a group velocity reaching the physical limits of the individual hardware platforms.