Estimating wind from an airborne vehicle

• US 10,023,323 B1
• Filed: 04/29/2015
• Issued: 07/17/2018
• Est. Priority Date: 04/29/2015
• Status: Active Grant

• Current Assignee: Wing Aviation LLC (Alphabet Inc.)
• Original Assignee: X Development LLC (Alphabet Inc.)
• Inventors: Roberts, Richard Joseeph William, Bialkowski, Joshua John, Sadowski, Justin, Roberts, John
• Primary Examiner(s): O'Hara, Brian M
• Assistant Examiner(s): Dixon, Keith L
• Application Number:
  US14/699,058
• Time in Patent Office: 1,175 Days
• CPC Class Codes: B64D 43/00 Arrangements or adaptations of instruments arrangements of cameras B64D47/08; aeronautical measuring instruments per se G01C
• CPC Class Codes: B64U 10/10 Rotorcrafts
• CPC Class Codes: B64U 10/13 Flying platforms
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• CPC Class Codes: B64U 2101/20 for use as communications relays, e.g. high-altitude platforms
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• CPC Class Codes: B64U 2101/55 for life-saving or rescue operations; for medical use
• CPC Class Codes: B64U 2101/64 for parcel delivery or retrieval
• CPC Class Codes: B64U 2201/10 autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
• CPC Class Codes: B64U 2201/20 Remote controls
• CPC Class Codes: G05D 1/0204 to counteract a sudden perturbation, e.g. cross-wind, gust

Abstract

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.

Claims

1. An unmanned aerial vehicle (UAV) comprising:

a network interface;

one or more sensors;

one or more processors; and

a computer-readable medium having stored therein instructions that are executable by the one or more processors to cause the UAV to carry out operations including;

while flying along a flight path, determining from onboard sensor measurements a first time series of ground-speed vectors with respect to a ground-based coordinate system, wherein the first time series has a set of time steps,

while flying along the flight path, determining from onboard sensor measurements a second time series of flight-heading vectors with respect to the ground-based coordinate system, wherein the second time series has the same set of time steps as the first time series, and

computationally estimating a wind vector with respect to the ground-based coordinate system by optimizing an analytical model of wind-driven deviations between the ground-speed vectors and the flight-heading vectors at like time steps, wherein the optimization is carried out over all data elements of the first and second time series,

wherein each ground-speed vector comprises three orthogonal components of the airborne UAV'"'"'s velocity vector at each corresponding time step with respect to the ground-based coordinate system,

wherein each flight-heading is a unit vector comprising three orthogonal components of the airborne UAV'"'"'s forward-pointing direction at each corresponding time step with respect to the ground-based coordinate system,

wherein the analytical model of wind-driven deviations between the ground-speed vectors and the flight-heading vectors at like time steps comprises a vector deviation at each respective time step between the ground-speed vector at the respective time step and a sum of (i) a scale factor multiplied by the flight-heading vector at the respective time step plus (ii) a wind hypothesis,

and wherein optimizing the analytical model comprises determining a wind hypothesis that minimizes a sum of the vector deviation over all the time steps.

2. The UAV of claim 1, wherein the one or more sensors are at least one of an inertial measurement unit (IMU), or a Global Positioning Satellite (GPS) system receiver.

3. The UAV of claim 1, wherein the scale factor is an airspeed estimate at each time step, and the airspeed estimate multiplied by the flight-heading vector at each time step corresponds to an estimate of an airspeed vector at each time step,

and wherein optimizing the analytical model further comprises algebraically formulating the analytical model to marginalize out the airspeed estimate at all of the time steps over which the optimization is performed.

4. The UAV of claim 1, wherein the operations further include, based at least in part on the computationally-estimated wind vector, adjusting at least one aspect of a flight-control function of the UAV while flying along the flight path.

5. The UAV of claim 4, wherein adjusting the at least one aspect of the flight-control function of the airborne UAV base at least in part on the computationally-estimated wind vector comprises refraining from carrying out an airborne maneuver.

12. The UAV of claim 3, wherein the analytical model further comprises a cost function that accounts for a deviation between a nominal expected air speed airspeed and an airspeed estimate at each time step.

6. An unmanned aerial vehicle (UAV) comprising:

a network interface;

one or more sensors;

one or more processors; and

a computer-readable medium having stored therein instructions that are executable by the one or more processors to cause the UAV to carry out operations including;

while flying along a flight path, determining from onboard sensor measurements a first time series of observed acceleration vectors, wherein the first time series has a set of time steps,

computing a second time series of predicted acceleration vectors based on a wind hypothesis applied to an analytical aerodynamic model of the UAV, wherein the second time series has the same set of time steps as the first time series, and

computationally estimating a wind vector with respect to a ground-based coordinate system by iteratively adjusting parameters of the wind hypothesis applied to the analytical aerodynamic model of the UAV so as to achieve optimal agreement between the predicted acceleration vectors and the observed acceleration vectors according to a least-squares minimization of deviations between the predicted acceleration vectors and the observed acceleration vectors.

7. The UAV of claim 6, wherein the one or more sensors are at least one of an inertial measurement unit (IMU), or a Global Positioning Satellite (GPS) system receiver.

8. The UAV of claim 6, wherein the analytical aerodynamic model of the UAV is configured for computing acceleration of the UAV based at least on (i) settings of coefficients of the aerodynamic model, (ii) real-time settings of UAV state variables, and (iii) settings of the parameters of the hypothesized wind vector applied to the aerodynamic model,

wherein the settings of the coefficients of the aerodynamic model are determined based on one or more previous, controlled test-flights of the UAV,

and wherein the UAV state variables comprise one or more state variables of one or more flight-control functions, and the real-time settings of the real-time UAV state variables are determined during flight of the UAV along the flight path.

9. The UAV of claim 6, wherein iteratively adjusting the parameters of the wind hypothesis applied to the analytical aerodynamic model of the UAV so as to achieve optimal agreement between the predicted acceleration vectors and the observed acceleration vectors according to a least-squares minimization of deviations between the predicted acceleration vectors and the observed acceleration vectors comprises:

iteratively computing the second time series of predicted acceleration vectors;

during each iteration, computing a least-squares minimization of a difference between the predicted acceleration vectors and the observed acceleration vectors; and

based on the computed least-squares minimization during each iteration, adjusting the parameters of the wind hypothesis in preparation for application of wind hypothesis on a next iteration.

10. The UAV of claim 6, wherein the operations further include, based at least in part on the computationally-estimated wind vector, adjusting at least one aspect of a flight-control function of the UAV while flying along the flight path.

11. The UAV of claim 10, wherein adjusting the at least one aspect of the flight-control function of the airborne UAV base at least in part on the computationally-estimated wind vector comprises refraining from carrying out an airborne maneuver.