Abstract
Drone light shows have emerged as a popular form of entertainment in recent years. However, several high-profile incidents involving large-scale drone failures -- where multiple drones simultaneously fall from the sky -- have raised safety and reliability concerns. To ensure robustness, we propose a drone parking algorithm designed specifically for multiple drone failures in drone light shows, aimed at mitigating the risk of cascading collisions by drone evacuation and enabling rapid recovery from failures by leveraging strategically placed hidden drones. Our algorithm integrates a Social LSTM model with attention mechanisms to predict the trajectories of failing drones and compute near-optimal evacuation paths that minimize the likelihood of surviving drones being hit by fallen drones. In the recovery node, our system deploys hidden drones (operating with their LED lights turned off) to replace failed drones so that the drone light show can continue. Our experiments showed that our approach can greatly increase the robustness of a multi-drone system by leveraging deep learning to predict the trajectories of fallen drones.
Abstract (translated)
近年来，无人机灯光秀作为一种流行的娱乐形式越来越受欢迎。然而，一些大型的无人机故障事件——即多架无人机同时从空中坠落——引发了安全性和可靠性的担忧。为了确保系统的稳健性，我们提出了一种专为多机故障设计的无人机停放算法，在大规模无人机表演中通过疏散策略来降低连环碰撞的风险，并利用隐藏在特定位置的备用无人机实现快速恢复。我们的算法结合了具有注意力机制的社会LSTM模型来预测坠落无人机的轨迹，并计算出最小化幸存无人机被坠落无人机撞击概率的近似最优疏散路径。在恢复阶段，系统部署备用无人机（关闭LED灯）以替换故障无人机，从而使表演得以继续进行。 实验表明，通过深度学习技术预测坠落无人机的轨迹，我们的方法可以大大提高多机系统的稳健性。
URL
https://arxiv.org/abs/2601.06728
