X Development LLC

Google Inc.

Nvidia Corporation

Wing Aviation LLC

Joshua John Bialkowski

John Roberts

Abraham Galton Bachrach

David Nister

Nikolai Smolyanskiy

Julia C Ng

Alexey Kamenev

Yizhou Wang

Birgit Henke

Rotem Aviv

Justin Sadowski

Miguel Sainz Serra

Hon Leung Lee

Zhenyi Zhang

Ollin Boer Bohan

Raul Correal Tezanos

Anton Vorontsov

Seungho Lee

Richard Joseeph William Roberts

Hermes Lanker

Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.

Behavior planning for autonomous vehicles

An unmanned aerial vehicle (UAV) including a winch system, wherein the winch system includes a single winch line, wherein a payload is suspended from a first end of the winch line, and the winch system is controllable to vary the rate of ascent of the payload to the UAV, and a control system including a processor and program instructions stored in a non-transitory computer readable medium and executable by the processor to control the winch, the control system configured to (a) receive data regarding oscillations of the payload, and (b) operate the winch system to vary a retraction rate of the winch line to damp oscillations of the payload during ascent of the payload to the UAV.

Method of actively controlling winch swing via modulated uptake and release

Embodiments are described for determining wind by an airborne aerial vehicle without reliance on direct measurements of airspeed by the vehicle. Instead, wind may be computationally estimated using disclosed techniques for utilizing measurements of only ground-speed and heading, or only measurements of forces experienced by the airborne aerial vehicle during flight. In one technique, samples of ground-speed measurements and corresponding heading measurements of an airborne vehicle are used in a mathematical optimization of a wind-driven hypothesis of deviations between the two types of measurement at each of multiple sampling times. In another technique, an aerodynamic model of an aerial vehicle can be used to adjust parameters of a wind hypothesis in order to achieve a best-fit between predicted and measured forces on the aerial vehicle during flight.

Estimating wind from an airborne vehicle

An unmanned aerial vehicle (UAV) including a winch system, wherein the winch system includes a winch line having a first end that is secured to the payload, and wherein the winch system is controllable to vary the rate of descent of the payload, an inertial measurement unit positioned on the payload or on the first end of the winch line, wherein the inertial measurement unit is configured to measure oscillations of the payload, and a control system configured to (a) receive data from the IMU, (b) determine oscillations of the payload based on the data received from the IMU, and (c) operate the winch system to vary the deployment rate of the winch line so to damp oscillations of the payload.

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San Mateo, CA, United States of America

Joshua John Bialkowski