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.
@article{arxiv.2105.12882,
title = {MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles},
author = {Yu-Shun Hsiao and Zishen Wan and Tianyu Jia and Radhika Ghosal and Abdulrahman Mahmoud and Arijit Raychowdhury and David Brooks and Gu-Yeon Wei and Vijay Janapa Reddi},
journal= {arXiv preprint arXiv:2105.12882},
year = {2023}
}
Comments
6 pages, 9 figures; The first two authors have equal contributions; Accepted as a conference paper in DATE 2023