🤖 AI Summary
This work proposes a fully decentralized multi-UAV autonomous coordination system to counter the threat posed by adversarial drones to ground targets in GPS-denied and communication-constrained battlefield environments. Relying solely on onboard sensors for relative observations, the system jointly estimates both target states and ego-positions through a relative-measurement-based Kalman filter. A distributed encirclement control strategy is developed that requires neither global positioning nor inter-vehicle communication, enabling dynamic adaptation to target motion while maintaining effective coverage. Real-robot experiments demonstrate the feasibility and robustness of the approach in detection, encirclement, and interception tasks, highlighting its operational potential under extreme conditions.
📝 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.