MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles
MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles
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DOI：
10.23919/date56975.2023.10137246
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发表时间：
2021-05
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通讯作者：
Yu-Shun Hsiao;Zishen Wan;Tianyu Jia;Radhika Ghosal;A. Raychowdhury;D. Brooks;Gu-Yeon Wei;V. Reddi
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作者：
Yu-Shun Hsiao;Zishen Wan;Tianyu Jia;Radhika Ghosal;A. Raychowdhury;D. Brooks;Gu-Yeon Wei;V. Reddi
安全性和弹性对于自主无人机（UAV）至关重要。本文介绍了微型飞行器（MAVs）弹性分析方法MAVFI，用于评估静默数据损坏（SDC）对无人机使命指标（如飞行时间和成功率）的影响，以准确测量系统弹性。为了提高机器人系统的安全性和弹性，由大小，重量和功率（SWaP）约束，我们提供了两个低开销的基于异常的SDC检测和恢复算法的基础上高斯统计模型和自编码器神经网络。我们的异常错误保护技术在许多模拟环境中得到验证。我们证明了基于自动编码器的技术可以恢复到所有的故障情况下，在我们研究的情况下，计算开销不超过0.0062%。我们的应用程序感知弹性分析框架MAVFI可用于全面测试其他基于机器人操作系统（ROS）的应用程序的弹性，并可在https://github.com/harvard-edge/MAVBench/tree/mavfi上公开获取。
Safety and resilience are critical for autonomous unmanned aerial vehicles (UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience analysis methodology to assess the effect of silent data corruption (SDC) on UAVs' mission metrics, such as flight time and success rate, for accurately measuring system resilience. To enhance the safety and resilience of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a compu-tational overhead of no more than 0.0062%. Our application-aware resilience analysis framework, MAVFI, can be utilized to comprehensively test the resilience of other Robot Operating System (ROS)-based applications and is publicly available at https://github.com/harvard-edge/MAVBench/tree/mavfi.