MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles
This work addresses safety and resilience issues for autonomous unmanned aerial vehicles, particularly those constrained by size, weight, and power, and is incremental as it builds on existing anomaly detection methods.
The paper tackles the problem of silent data corruption in micro aerial vehicles by introducing MAVFI, an end-to-end fault analysis framework with anomaly detection and recovery algorithms, achieving recovery of up to all failure cases with a computational overhead of no more than 0.0062%.
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.