Navigating an unmanned aerial vehicle

• US 10,474,148 B2
• Filed: 07/27/2016
• Issued: 11/12/2019
• Est. Priority Date: 07/27/2016
• Status: Active Grant

• Current Assignee: GE Aviation Systems LLC (GE Aerospace)
• Original Assignee: General Electric Company
• Inventors: Clynne, Thomas
• Primary Examiner(s): Cass, Jean Paul
• Application Number:
  US15/220,729
• Publication Number:
  US 20180032074A1
• Time in Patent Office: 1,203 Days
• Field of Search: 701 3
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• 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: G01C 21/1656 with passive imaging devices, e.g. cameras
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/106 Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/20201 Motion blur correction
• CPC Class Codes: G06T 2207/30181 Earth observation
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G08G 5/0021 located in the aircraft
• CPC Class Codes: G08G 5/0069 specially adapted for an unmanned aircraft
• CPC Class Codes: G08G 5/04 Anti-collision systems
• CPC Class Codes: G08G 5/045 Navigation or guidance aids, e.g. determination of anti-collision manoeuvers radar or analogous systems specially adapted for anti-collision between aircraft G01S13/933
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

Systems and methods for navigating an unmanned aerial vehicle are provided. One example aspect of the present disclosure is directed to a method for navigating an unmanned aerial vehicle. The method includes capturing, by one or more processors associated with a flight management system of an unmanned aerial vehicle, one or more images. The method includes assessing, by the one or more processors, signals from an inertial measurement unit (IMU). The method includes processing, by the one or more processors, the one or more captured images based on the assessed signals to generate one or more corrected images. The method includes processing, by the one or more processors, the one or more generated corrected images to approximate position data. The method includes causing, by the one or more processors, the unmanned aerial vehicle to be controlled based on the position data.

Claims

1. A method for controlling an unmanned aerial vehicle comprising:

capturing, by one or more processors associated with a flight management system of the unmanned aerial vehicle, more than one images, the capturing further comprising;

capturing a first image by a first optical device at a first time, the first image including an obstacle; and

capturing a second image by a second optical device at the first time, the second image including the obstacle, and the second optical device being situated a predetermined distance from the first optical device;

assessing, by the one or more processors, signals from an inertial measurement unit (TM U);

processing, by the one or more processors, the more than one images based on the assessed signals to generate one or more corrected images;

processing, by the one or more processors, the more than one generated corrected images to approximate a distance between the obstacle and the unmanned aerial vehicle, and

causing, by the one or more processors, the unmanned aerial vehicle to be controlled based on the approximated distance between the obstacle and the unmanned aerial vehicle, wherein processing to approximate the distance between the obstacle and the unmanned aerial vehicle is based, at least in part, on the predetermined distance and a pixel shift between the obstacle as included in the first image and the obstacle as included in the second image;

capturing a third image by the first optical device at a second time; and

capturing a fourth image by the second optical device at the second time,

estimating a first position of the obstacle at the first time;

estimating a second position of the obstacle at the second time;

estimating a velocity of the obstacle based on a difference between the first position and the second position and a difference between the first time and the second time;

capturing a fifth image by the first optical device at a third time;

capturing a sixth image by the second optical device at the third time;

estimating a third position of the obstacle at the third time;

estimating a second velocity of the obstacle based on a difference between the second position and the third position and a difference between the second time and the third time; and

estimating an acceleration of the obstacle based, at least in part, on the first estimated velocity and the second estimated velocity, wherein processing the one or more captured images comprises stabilizing the one or more captured images.

2. The method of claim 1, wherein the first optical device captures a first video stream, and wherein the second optical device captures a second video stream.

3. The method of claim 1, wherein the IMU comprises an accelerometer, and wherein processing the one or more images comprises reducing blur.

4. The method of claim 1, wherein the IMU comprises a microelectromechanical systems (MEMS) gyroscope.

5. The method of claim 1, further including controlling the unmanned aerial vehicle based on the distance between the obstacle and the unmanned aerial vehicle, and wherein the controlling comprises causing, by the processor, the unmanned aerial vehicle to perform an avoidance maneuver.

6. The method of claim 1, wherein estimating the distance between the obstacle and the unmanned aerial vehicle is further based on an instantaneous field of view (IFOV) associated with the first optical device.

7. The method of claim 1, wherein estimating the distance between the obstacle and the unmanned aerial vehicle is further based on an instantaneous field of view (IFOV) associated with the second optical device.

8. The method of claim 6, wherein the instantaneous field of view includes an amount of angular arc subtended by each pixel of the one or more images.

9. The method of claim 7, wherein the instantaneous field of view includes an amount of angular arc subtended by each pixel of the one or more images.

10. The method of claim 5, further comprising causing, by the processor, the unmanned aerial vehicle to return to a planned course after performing the avoidance maneuver.