Drone device

• US 10,562,625 B2
• Filed: 04/02/2019
• Issued: 02/18/2020
• Est. Priority Date: 02/28/2014
• Status: Active Grant

• Current Assignee: Lucas Myslinski
• Original Assignee: Lucas Myslinski
• Inventors: Myslinski, Lucas J.
• Primary Examiner(s): Anwari, Maceeh
• Application Number:
  US16/372,933
• Publication Number:
  US 20190225336A1
• Time in Patent Office: 322 Days
• Field of Search: 701 3
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64D 47/02 Arrangements or adaptations of signal or lighting devices
• CPC Class Codes: B64D 47/08 Arrangements of cameras
• CPC Class Codes: B64D 5/00 Aircraft transported by aircraft, e.g. for release or reberthing during flight flying units formed by separate aircraft B64C37/02
• CPC Class Codes: B64U 20/10 for stealth, e.g. reduction of cross-section detectable by radars
• CPC Class Codes: B64U 20/50 Foldable or collapsible UAVs with frangible components B64U20/30
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• 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: B64U 80/25 for recharging batteries; for refuelling
• 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 1/0094 involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/104 involving a plurality of aircrafts, e.g. formation flying traffic control systems for aircraft G08G5/00
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0833 Tracking
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 : Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0207 : Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0282 : Rating or review of business operators or products
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: G06Q 50/26 : Government or public services business processes related to the transportation industry G06Q50/40
• CPC Class Codes: G06T 2207/10016 : Video; Image sequence
• CPC Class Codes: G06T 2207/10032 : Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30196 : Human being; Person
• CPC Class Codes: G06T 2207/30232 : Surveillance
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06V 20/40 : in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/52 : Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/10 : Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G08B 21/18 : Status alarms G08B21/02 takes precedence
• CPC Class Codes: H04N 5/77 : between a recording apparatus and a television camera
• CPC Class Codes: H04N 5/772 : the recording apparatus and the television camera being placed in the same enclosure
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H04N 7/188 : Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position signal generation from motion picture films H04N5/253

Abstract

A fact checking system utilizes social networking information and analyzes and determines the factual accuracy of information and/or characterizes the information by comparing the information with source information. The social networking fact checking system automatically monitors information, processes the information, fact checks the information and/or provides a status of the information, including automatically modifying a web page to include the fact check results. The fact checking system is able to be implemented utilizing a drone device.

Claims

1. An aerial drone device comprising:

a device body configured to transition from a folded configuration to an unfolded configuration and from the unfolded configuration to the folded configuration, wherein the device body includes components capable of folding such that the aerial drone device has a smaller storage footprint when in the folded configuration than when in the unfolded configuration, wherein the aerial drone device is configured to fly in the unfolded configuration, wherein while in the folded configuration the aerial drone device looks like another object;

at least one motor coupled to the device body;

at least one propeller coupled to the at least one motor;

at least one camera coupled to the device body, wherein the at least one camera is configured to acquire content;

a plurality of information acquisition components coupled to the device body, wherein a first information acquisition component of the plurality of information acquisition components is positioned in an opposite direction of a second information acquisition component of the plurality of information acquisition components, wherein the plurality of information acquisition components are configured to acquire external information;

at least one power supply configured to provide power to the at least one motor, the at least one camera, and the plurality of information acquisition components;

a memory configured to store an application, the application configured to;

process the content acquired by the at least one camera and the external information acquired by the plurality of information acquisition components;

acquire depth information using the at least one camera, the plurality of information acquisition components, or any combination thereof;

direct the aerial drone device to navigate based on the content from the at least one camera, the external information from the plurality of information acquisition components, or any combination thereof, including avoiding at least one obstacle; and

track at least one object;

generate a depth map using the depth information, wherein the depth map is used to track the at least one object;

predict a trajectory of the at least one object; and

track the at least one object based on the predicted trajectory when a view of the at least one object is obstructed; and

a processor configured to process the application,

wherein the aerial drone device is configured to automatically re-position itself to avoid an obstruction by generating the depth map and utilizing an obstruction threshold, wherein generating the depth map includes using an object size calculation analysis, wherein when an amount of obstruction is above the obstruction threshold, the aerial drone device moves to reduce the amount of obstruction, wherein the aerial drone device continues to re-position itself to find a lowest amount of obstruction.

2. The aerial drone device of claim 1 wherein the plurality of information acquisition components comprise a plurality of sensors.

3. The aerial drone device of claim 1 wherein the plurality of information acquisition components comprise a plurality of cameras.

4. The aerial drone device of claim 1 wherein the application is further configured to detect a plurality of objects simultaneously.

5. The aerial drone device of claim 1 further comprising a global positioning system device configured to determine a location of the aerial drone device.

6. The aerial drone device of claim 1 wherein the application is further configured to direct the aerial drone device to navigate so that the aerial drone device orbits an object.

7. The aerial drone device of claim 1 wherein the application is further configured to direct the aerial drone device to change altitude when tracking the at least one object.

8. The aerial drone device of claim 1 wherein the application includes an invisible route for the aerial drone device to follow.

9. The aerial drone device of claim 1 wherein the application accesses “

do not fly”

zone information to avoid flying in a “

do not fly”

zone.

10. The aerial drone device of claim 1 wherein the aerial drone device is configured to stay within a zone.

11. The aerial drone device of claim 1 further comprising a motion sensor configured to trigger to activate the aerial drone device.

12. The aerial drone device of claim 1 wherein the application is further configured to synchronize the content with an external device.

13. The aerial drone device of claim 1 further comprising a component configured to detect a flying vehicle.

14. The aerial drone device of claim 1 further comprising a thermal sensor configured to detect heat, and a sound sensor configured to detect sound.

15. The aerial drone device of claim 1 further comprising a plurality of interchangeable lenses for the at least one camera.

16. The aerial drone device of claim 1 further comprising a storage mechanism configured to hold an item.

17. A system comprising:

a controller device; and

an aerial drone device configured to acquire content and communicate with the controller device, wherein the aerial drone device includes a body configured to transition from a folded configuration to an unfolded configuration and from the unfolded configuration to the folded configuration, wherein the body includes components capable of folding such that the aerial drone device has a smaller storage footprint when in the folded configuration than when in the unfolded configuration, wherein the aerial drone device is configured to fly in the unfolded configuration, wherein while in the folded configuration the aerial drone device looks like another object, wherein the body is further configured to secure at least one power supply, the at least one power supply configured to supply power to;

at least one motor configured to cause at least one propeller to move,

at least one camera, wherein the at least one camera is configured to acquire the content, and

a plurality of information acquisition components, wherein a first information acquisition component of the plurality of information acquisition components is positioned in a first direction, and a second information acquisition component of the plurality of information acquisition components is positioned in a second direction, wherein the plurality of information acquisition components are configured to acquire external information,

wherein the aerial drone device is further configured to;

process the content acquired by the at least one camera, the external information acquired by the plurality of information acquisition components, or any combination thereof;

acquire depth information using the at least one camera, the plurality of information acquisition components, or any combination thereof;

navigate based on the content from the at least one camera, the external information from the plurality of information acquisition components, or any combination thereof, including avoiding at least one obstacle;

track at least one object using the depth information;

generate a depth map using the depth information, wherein the depth map is used to track the at least one object;

predict a trajectory of the at least one object; and

track the at least one object based on the predicted trajectory when a view of the at least one object is obstructed,

wherein the aerial drone device is configured to automatically re-position itself to avoid an obstruction by generating the depth map and utilizing an obstruction threshold, wherein generating the depth map includes using an object size calculation analysis, wherein when an amount of obstruction is above the obstruction threshold, the aerial drone device moves to reduce the amount of obstruction, wherein the aerial drone device continues to re-position itself to find a lowest amount of obstruction.

18. The system of claim 17 wherein the aerial drone device is further configured to share the content via a social network.

19. The system of claim 17 further comprising a device configured for viewing augmented reality content, virtual reality content or any combination thereof based on the content acquired using the camera.

20. A method comprising:

acquiring content with at least one camera of an aerial drone device;

acquiring external information with a plurality of information acquisition components of the aerial drone device, wherein a first information acquisition component of the plurality of information acquisition components is positioned in an opposite direction of a second information acquisition component of the plurality of information acquisition components;

processing the content acquired by the at least one camera, the external information acquired by the plurality of information acquisition components, or any combination thereof;

determining depth information based on the content acquired by the at least one camera, the external information acquired by the plurality of information acquisition components, or any combination thereof;

navigating the aerial drone device based on the content acquired by the at least one camera, the external information acquired by the plurality of information acquisition components, or any combination thereof, wherein navigating includes tracking at least one object and avoiding at least one obstacle, further wherein navigating includes utilizing at least one motor configured to cause at least one propeller to move;

generating a depth map using the depth information, wherein the depth map is used to track the at least one object;

predicting a trajectory of the at least one object;

tracking the at least one object based on the predicted trajectory when a view of the at least one object is obstructed;

automatically re-positioning the aerial drone device to avoid an obstruction by generating the depth map and utilizing an obstruction threshold, wherein generating the depth map includes using an object size calculation analysis, wherein when an amount of obstruction is above the obstruction threshold, the aerial drone device moves to reduce the amount of obstruction, wherein the aerial drone device continues to re-position itself to find a lowest amount of obstruction; and

transitioning from a folded configuration to an unfolded configuration and from the unfolded configuration to the folded configuration, wherein the aerial drone device includes components capable of folding such that the aerial drone device has a smaller storage footprint when in the folded configuration than when in the unfolded configuration, wherein the aerial drone device is configured to fly in the unfolded configuration, wherein while in the folded configuration the aerial drone device looks like another object.