Wind-Informed Path-Planning for Autonomous Flight in Cities

Autonomous flight research usually focuses on the inner-loop, "local" control of a flier. Relying entirely on on-board sensing and control means the flier can only respond to wind disturbances after being hit by them. We propose incorporating the surrounding wind field into path-planning, such that wind disturbances can be rejected without the flier ever encountering them. This requires an efficient way to predict flight-relevant wind conditions surrounding the flier, possibly on-board and during flight. PARAGRAPH 2. In this work, we present a path-planning approach built on a surrogate model of the mean velocity and turbulent kinetic energy fields of wind around urban geometries. Our deep-learning-based surrogate model is trained on data from a large-scale simulation campaign covering 3D models of cities around the world. Our trained model infers effectively on out-of-sample city geometries orders of magnitude faster than it would take to converge the corresponding simulation, and efficiently enough to run in a laptop or on-board a drone. Our path-planner optimizes a custom measure of "flight challenge" to balance speed and safety given flow estimates from the surrogate model. We test tracking performance experimentally using a fan-array wind tunnel to generate representative wind conditions in real obstacle courses.