Amazon Technologies, Inc.

Christopher Aden Maynor

Daniel Buchmueller

Ricky Dean Welsh

Louis LeRoi LeGrand, Iii

Scott Michael Wilcox

Fabian Hensel

Jon Lewis Lindskog

Samuel Park

Ishwarya Ananthabhotla

Sankalp Kishan Bhan

Marco Giovanni Giometto

Wind velocities at positions within an environment are predicted using stochastic models that consider average wind flows at the various positions and calculate random components of the wind flows (e.g., due to gusts) according to autoregressive functions. One or more time series of wind velocity obtained from any sources may be identified, and distribution functions are fit to the time series. A copula or another multivariate cumulative distribution function determined from a correlation matrix and the distribution functions is used to generate coefficients of the autoregressive functions. Wind velocities at the positions are modeled from the average wind flows and the calculated random components. Decisions on safety or reliability of aerial vehicles for performing missions within the environment are determined from the wind velocities.

Data-driven modeling of low-altitude turbulence

This disclosure describes an unmanned aerial vehicle ('UAV') and system that may perform one or more techniques for protecting objects from damage resulting from an unintended or uncontrolled impact by a UAV. As described herein, various implementations utilize a damage avoidance system that detects a risk of damage to an object caused by an impact from a UAV that has lost control and takes steps to reduce or eliminate that risk. For example, the damage avoidance system may detect that the UAV has lost power and/or is falling at a rapid rate of descent such that, upon impact, there is a risk of damage to an object with which the UAV may collide. Upon detecting the risk of damage and prior to impact, the damage avoidance system activates a damage avoidance system having one or more protection elements that work in concert to reduce or prevent damage to the object upon impact by the UAV.

Damage avoidance system for unmanned aerial vehicle using stored energy from descent

Damage avoidance system for unmanned aerial vehicle

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Boston, MA, United States of America

Christopher Aden Maynor