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This research presents a decentralized control algorithm for UAV swarms capable of operating without GPS or external communication links, relying instead on onboard sensors and relative measurements like Kalman filters. The system autonomously coordinates to encircle hostile targets in denied environments, validating the approach with real robots rather than simulation alone. While framed within robotics, the core contribution lies in advanced machine learning-inspired coordination logic for autonomous agents functioning under severe constraints.
Decentralized UAV Swarms for Ground Target Protection in GPS- and Communication-Denied Environments
arXiv:2607.20710v1 Announce Type: new Abstract: The presence of UAVs in military operations has recently increased, also increasing the demand for defense systems against UAV attacks. UAVs can also be used as countermeasures. Most available methods rely on UAV-to-UAV communication and global positioning. However, such resources may not be available in modern warfare scenarios. To address these limitations, we propose a pipeline for ground-target protection against UAV attacks that employs autonomous swarms of UAVs. We assume a communication- and GPS-denied environment in which the UAVs use onboard sensors to track the target and coordinate as a swarm. We developed Kalman filters to estimate the states of unknown targets and the positions of UAVs in the swarm using only relative measurements. Also, our strategy is to encircle the target of interest to maximize coverage. To achieve that, we propose a decentralized swarm encirclement technique that adapts to the target's motion. Our approach was extensively validated using real robots, demonstrating its effectiveness in detecting, encircling, and intercepting hostile UAVs.
Abstract:The presence of UAVs in military operations has recently increased, also increasing the demand for defense systems against UAV attacks. UAVs can also be used as countermeasures. Most available methods rely on UAV-to-UAV communication and global positioning. However, such resources may not be available in modern warfare scenarios. To address these limitations, we propose a pipeline for ground-target protection against UAV attacks that employs autonomous swarms of UAVs. We assume a communication- and GPS-denied environment in which the UAVs use onboard sensors to track the target and coordinate as a swarm. We developed Kalman filters to estimate the states of unknown targets and the positions of UAVs in the swarm using only relative measurements. Also, our strategy is to encircle the target of interest to maximize coverage. To achieve that, we propose a decentralized swarm encirclement technique that adapts to the target's motion. Our approach was extensively validated using real robots, demonstrating its effectiveness in detecting, encircling, and intercepting hostile UAVs.
Robotics (cs.RO)
arXiv:2607.20710 [cs.RO]
(or arXiv:2607.20710v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20710 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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