GP-based Runtime Planning, Learning, and Recovery for Safe UAV Operations under Unforeseen Disturbances

Abstract

Autonomous vehicles are typically developed and trained to work under certain system and environmental conditions defined at design time and can fail or perform poorly if unforeseen conditions such as disturbances or changes in model dynamics appear at runtime. In this work, we present a fast online planning, learning, and recovery approach for safe autonomous operations under unknown runtime disturbances. Our approach estimates the behavior of the system with an unknown model and provides safe plans at runtime under previously unseen disturbances by leveraging Gaussian Process regression theory in which a model is continuously trained and adapted using data collected during the autonomous operation. A recovery procedure is event-triggered any time a safety constraint is violated to guarantee safety and enable learning and replanning. The proposed framework is applied and validated both in simulation and experiment on an unmanned aerial vehicle (UAV) delivery case study in which the UAV is tasked to carry an a priori unknown payload to a goal location in a cluttered/constrained environment.