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.

翻译：近年来，无人机灯光秀已成为一种流行的娱乐形式。然而，多起引人注目的大规模无人机故障事件——即多架无人机同时从空中坠落——引发了人们对安全性和可靠性的担忧。为确保鲁棒性，我们提出了一种专门针对无人机灯光秀中多无人机故障设计的无人机停泊算法，旨在通过无人机撤离来降低级联碰撞的风险，并利用战略部署的隐藏无人机实现故障后的快速恢复。我们的算法将Social LSTM模型与注意力机制相结合，以预测故障无人机的轨迹，并计算近乎最优的撤离路径，从而最大限度地降低幸存无人机被坠落无人机击中的可能性。在恢复节点中，我们的系统会部署隐藏无人机（其LED灯处于关闭状态）以替换故障无人机，使无人机灯光秀得以继续进行。实验表明，我们的方法通过利用深度学习预测坠落无人机的轨迹，能够显著提高多无人机系统的鲁棒性。