MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles

TL;DR

MAVFI is a comprehensive framework for analyzing and improving the resilience of micro aerial vehicles against silent data corruption, using anomaly detection and recovery techniques validated in simulations.

Contribution

Introduces MAVFI, a novel end-to-end resilience analysis framework with low-overhead anomaly detection and recovery algorithms for UAVs, applicable to ROS-based systems.

Findings

Autoencoder-based recovery achieves up to 100% success in studied scenarios.

Recovery overhead is minimal, at no more than 0.0062%.

Framework is validated in simulated environments and publicly available.

Abstract

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 computational 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.