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

2025-08-22

AI Summary

This work addresses state estimation and cooperative stability for multi-UAV swarms operating under global positioning failure—relying solely on relative sensing. Method: We propose a decentralized robust state fusion framework that constructs an anchor-free relative coordinate system, integrating onboard sensor measurements with robust mutual perception data to achieve state decoupling and synchronization of double-integrator dynamics; this design suppresses heterogeneous disturbances while retaining only controllable translational drift. Contribution/Results: Experimental results demonstrate that the method maintains lateral position stability, velocity consensus, and formation coherence even under GPS-denied conditions or primary localization system failure. Compared to conventional absolute-reference-dependent approaches, it significantly enhances the resilience, reliability, and environmental adaptability of multi-UAV systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Maintain UAV swarm state estimation during localization dropouts
• Fuse decentralized sensing with mutual perception for stability
• Achieve velocity consensus despite intermittent localization failures

Innovation

• Decentralized state estimation fusion
• Robust mutual perception integration
• Onboard sensor data utilization