无GPS和通信环境下，无人机群自主围捕目标
Decentralized UAV Swarms for Ground Target Protection in GPS- and Communication-Denied Environments
- 基于相对测量的自适应围捕算法，无需全局定位
- 真实机器人验证，可有效检测并拦截敌方无人机
- 适合战场等极端环境，对通信与定位依赖低
无人机在军事行动中的应用日益增多，也推动了防御系统的发展。现有方法多依赖无人机间通信和全球定位，但在现代战争中这些资源可能不可用。为此，本文提出一种针对地面目标的无人机群自主保护方案，在无通信与GPS环境中，利用机载传感器实现目标追踪与群组协同。通过开发卡尔曼滤波器，仅基于相对测量估计未知目标状态和无人机位置。策略上采用动态围捕，以最大化覆盖范围。所提去中心化围捕技术可随目标运动自适应调整。该方法通过真实机器人系统广泛验证，成功实现对敌方无人机的检测、围捕与拦截。
原文摘要 · Abstract (English)
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.
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