论文标题
板载深度学习的无人机故障导致检测和识别
On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification
论文作者
论文摘要
随着无人机（UAV）/无人机使用的使用增加，从潜在的类似撞车事件或事件后的取证分析中检测和确定实时恢复失败的原因很重要。崩溃的原因可能是传感器/执行器系统中的故障，物理伤害/攻击或对无人机软件的网络攻击。在本文中，我们提出了基于深度卷积和长期短期记忆神经网络（CNN和LSTMS）的新型体系结构，以检测（通过自动编码器），并根据传感器数据对无人机误操作进行分类。所提出的体系结构能够从原始传感器数据中自动学习高级功能，并学习传感器数据中的空间和时间动力学。我们通过模拟和实验在真实的无人机上验证了所提出的深度学习架构。经验结果表明，我们的解决方案能够以超过90％的精度检测并对各种类型的无人机误操作进行分类（具有约99％的精度（仿真数据）和88％的精度（实验数据））。
With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).
