多无人机故障下的鲁棒疏散
Robust Evacuation for Multi-Drone Failure in Drone Light Shows
AI总结：
本研究提出了一种针对多无人机故障的鲁棒疏散算法，通过预测故障无人机轨迹并利用隐藏无人机实现快速恢复，提高无人机灯光秀的可靠性。
摘要：
无人机灯光秀近年来已成为一种受欢迎的娱乐形式。然而，几个涉及大规模无人机故障的高profile事件——多个无人机同时从天空坠落——引发了安全性和可靠性问题。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的无人机停车算法，旨在通过无人机疏散来降低连锁碰撞的风险，并利用战略性放置的隐藏无人机实现快速恢复。我们的算法结合了社交LSTM模型和注意力机制，以预测故障无人机的轨迹并计算近最优的疏散路径，以最小化存活无人机被坠落无人机击中的可能性。在恢复节点，我们的系统部署隐藏无人机（其LED灯关闭）以取代故障无人机，使无人机灯光秀得以继续。我们的实验表明，通过深度学习预测坠落无人机的轨迹，我们的方法可以显著提高多无人机系统的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
