用AI预测坠机轨迹，自动疏散并补位，提升无人机灯光秀抗故障能力
Robust Evacuation for Multi-Drone Failure in Drone Light Shows
- 用Social LSTM+注意力机制预测坠毁无人机路径
- 可降低幸存无人机被撞击概率，实现快速恢复
- 适合大型无人机表演场景，保障演出安全连续
近年来，无人机灯光秀成为流行娱乐形式。但多起大规模无人机同时坠落事件引发安全与可靠性担忧。为增强系统鲁棒性，本文提出一种专用于多无人机故障的无人机停靠算法，通过无人机疏散避免级联碰撞，并利用预先布置的隐藏无人机（灯关闭）实现故障快速恢复。算法融合Social LSTM与注意力机制，预测故障无人机轨迹，计算近似最优撤离路径，最小化幸存无人机被击中风险。实验表明，该方法通过深度学习预测坠机轨迹，显著提升多无人机系统的容错能力。
原文摘要 · Abstract (English)
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.
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