Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot

TL;DR

The paper introduces SWA, a decentralized approach enabling UAV swarms to maintain accurate state estimation and cohesive behavior despite ego-localization dropouts, outperforming single UAVs in resilience and reliability.

Contribution

We propose a novel decentralized fusion method combining mutual perception and onboard sensors to improve UAV swarm robustness during localization failures.

Findings

SWA maintains swarm cohesion during localization dropouts.

Simulations and experiments confirm improved resilience over single UAVs.

The approach enables velocity consensus despite intermittent localization.

Abstract

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization.