Multi-level Adaptation for Automatic Landing with Engine Failure under Turbulent Weather

Gu, Haotian, Jafarnejadsani, Hamidreza

arXiv.org Artificial Intelligence 

The unmanned aerial vehicles (UAVs) technology, which is moving towards full autonomous flight, requires operation under uncertainties due to dynamic environments, interaction with humans, system faults, and even malicious cyber attacks. Ensuring security and safety is the first step to making the solutions using such systems certifiable and scalable. In this paper, we introduce an autopilot framework called "Multi-level Adaptive Safety Control" (MASC) for the resilient control of autonomous UAVs under large uncertainties and employ it for engine-out automatic landing under severe weather conditions. A. MASC Architecture In 2009, an Airbus A320 passenger plane (US Airways flight 1549) lost both engines minutes after take-off from LaGuardia airport in New York City due to severe bird strikes [1]. Captain Sullenberger safely landed the plane in the nearby Hudson River. Inspired by this story, we aim to equip UAVs with the capability of human pilots to determine if the current mission is still possible after a severe system failure. If not, the mission is re-planned so that it can be accomplished using the remaining capabilities. This is achieved by the proposed autopilot framework, MASC, which is capable of performing safe maneuvers that are traditionally reserved for human pilots.

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