Navigation system for a drone

• US 10,198,011 B2
• Filed: 07/06/2018
• Issued: 02/05/2019
• Est. Priority Date: 07/06/2017
• Status: Active Grant

• Current Assignee: Istari Digital Incorporated
• Original Assignee: Top Flight Technologies, Inc.
• Inventors: DeBitetto, Paul A., Phan, Long N., Rasmussen, Scott, Sarma, Sanjay
• Primary Examiner(s): Ingram, Thomas
• Application Number:
  US16/029,383
• Publication Number:
  US 20190011934A1
• Time in Patent Office: 214 Days
• CPC Class Codes: B64D 27/02 Aircraft characterised by the type or position of power plants fuselages specially adapted for mounting power plants B64C1/16; wings specially adapted for power plants B64C3/32
• CPC Class Codes: B64D 27/026 comprising different types of power plants, e.g. combination of a piston engine and a gas-turbine Hybrid electric aircraft B64D27/33
• 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 2101/31 for surveillance
• CPC Class Codes: B64U 2101/64 for parcel delivery or retrieval
• CPC Class Codes: B64U 2201/104 using satellite radio beacon positioning systems, e.g. GPS
• CPC Class Codes: B64U 2201/20 Remote controls
• CPC Class Codes: B64U 50/11 using internal combustion piston engines
• CPC Class Codes: B64U 50/19 using electrically powered motors
• CPC Class Codes: G01C 21/005 with correlation of navigation data from several sources, e.g. map or contour matching G01C21/30 takes precedence
• CPC Class Codes: G05D 1/102 specially adapted for vertical take-off of aircraft
• 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: G06N 3/08 Learning methods
• CPC Class Codes: Y02T 50/60 Efficient propulsion technologies, e.g. for aircraft

Abstract

This document describes an unmanned aerial vehicle (UAV) configured to navigate an unmanned aerial vehicle highway. The UAV includes a navigation system that includes a sensor, configured to gather environmental data, and a computing system configured to navigate the UAV. The computing system compares the environmental data to a specified data signature in the one or more spectra and determines a position of the unmanned aerial vehicle in the unmanned aerial vehicle highway. The UAV includes a hybrid generator system including an engine configured to generate mechanical energy and a generator motor coupled to the engine and configured to generate electrical energy from the mechanical energy generated by the engine. The UAV includes a rotor motor configured to drive a propeller to rotate. The navigation system is powered by the electrical energy generated by the generator motor.

Claims

1. An unmanned aerial vehicle comprising:

a navigation system comprising;

a sensor configured to gather environmental data in one or more spectra;

a computing system configured to;

compare the environmental data to a specified data signature in the one or more spectra, the specified data signature being associated with an unmanned aerial vehicle highway; and

determine, based on comparing the environmental data to the specified data signature, a position of the unmanned aerial vehicle in the unmanned aerial vehicle highway; and

a hybrid generator system comprising;

an engine configured to generate mechanical energy; and

a generator motor coupled to the engine and configured to generate electrical energy from the mechanical energy generated by the engine; and

at least one rotor motor configured to drive at least one propeller of the unmanned aerial vehicle to rotate, wherein the at least one rotor motor is powered by the electrical energy generated by the generator motor,

wherein the navigation system is powered by the electrical energy generated by the generator motor.

2. The unmanned aerial vehicle of claim 1, wherein the sensor of the navigation system comprises a thermal sensor, and wherein the one or more spectra comprise thermal emissions of the unmanned aerial vehicle highway.

3. The unmanned aerial vehicle of claim 1, wherein the navigation system comprises one or more of a camera, a ranging sensor, an infrared sensor, and an accelerometer.

4. The unmanned aerial vehicle of claim 1, wherein the computing system of the navigation system is further configured to:

store a digital elevation model representing at least a portion of a navigation environment in a storage of the navigation system;

determine that the environmental data correspond to at least a portion of the digital elevation model;

determine one or more of an elevation of the unmanned aerial vehicle and a location of the unmanned aerial vehicle relative to a reference point in the digital elevation model; and

map the position of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on i) the one or more of the elevation of the unmanned aerial vehicle and the location of the unmanned aerial vehicle relative to the reference point and ii) a position of the reference point in the unmanned aerial vehicle highway.

5. The unmanned aerial vehicle of claim 1, wherein the unmanned aerial vehicle highway comprises a map of predefined paths.

6. The unmanned aerial vehicle of claim 5, wherein the map of predefined paths corresponds to a network of power transmission lines and transmission towers, and wherein the specified data signature comprises data indicative of one of a transmission tower and a power transmission line.

7. The unmanned aerial vehicle of claim 5, wherein the map of predefined paths corresponds to a road network, and wherein the specified data signature comprises data indicative of at least a portion of a road of the road network.

8. The unmanned aerial vehicle of claim 5, wherein the map of predefined paths corresponds to a pipeline network, and wherein the specified data signature comprises data indicative of at least a portion of a pipeline of the pipeline network.

9. The unmanned aerial vehicle of claim 1, wherein one or more spectra comprises a plurality of spectra, a respective data signature being associated with each spectrum, and wherein the computing system of the navigation system is configured to:

generate a map that;

designates a first position in the map of a first data signature as being associated with a first spectrum of the spectra; and

designates a second position of a second data signature as being associated with a second spectrum of the spectra; and

determine the position of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on the generated map designating the first position and the second position.

10. The unmanned aerial vehicle of claim 1, wherein the specified data signature is programmed into a synapse of a neural network, and wherein comparing the environmental data to the specified data signature comprises inputting the environmental data into the neural network.

11. The unmanned aerial vehicle of claim 1, wherein the specified data signature is indicative of a landmark for a localization and mapping navigation algorithm.

12. The unmanned aerial vehicle of claim 11, wherein the computing system of the navigation system is configured to generate a factor graph, and wherein the landmark is represented by a node in the factor graph.

13. The unmanned aerial vehicle of claim 1, wherein the computing system of the navigation system implements a neural network that is trained using a plurality of data signatures from a feature database;

and

wherein the computing system of the navigation system is configured to update the feature database with the gathered environmental data.

14. The unmanned aerial vehicle of claim 1, wherein the computing system of the navigation system is configured to:

execute a simulation concurrently with gathering the environmental data to predict values of subsequently gathered environmental data; and

compare the predicted values of the subsequently gathered environmental data to measured values of subsequently gathered environmental data; and

updating the simulation based on a difference between the predicted values and the measured values.

15. The unmanned aerial vehicle of claim 1, wherein the one or more spectra comprise a magnetic field, and wherein the specified data signature comprises a change in a magnitude of the magnetic field.

16. The unmanned aerial vehicle of claim 1, wherein the computing system of the navigation system is configured to:

receive data indicative of a congested portion of the unmanned aerial vehicle highway; and

plan a path of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on the received data to at least partially avoid the congested portion.

17. The unmanned aerial vehicle of claim 1, wherein the specified data signature is indicative of a beacon of the unmanned aerial vehicle highway.

18. The unmanned aerial vehicle of claim 1, further comprising a battery, the battery configured to be charged by the electrical energy generated by the generator motor, wherein the battery provides electrical power to the navigation system to power the navigation system.

19. The unmanned aerial vehicle of claim 1, wherein the navigation system comprises a global positioning system (GPS) receiver, and wherein the specified data signature includes GPS coordinates.

20. The unmanned aerial vehicle of claim 1, wherein the specified data signature includes a tower identifier for a cellular network.

21. A method for navigating an unmanned aerial vehicle along an unmanned aerial vehicle highway, the method comprising:

gathering, by one or sensors of a navigation system, environmental data in one or more spectra;

comparing, by a computing system of the navigation system, the environmental data to a specified data signature in the one or more spectra, the specified data signature being associated with an unmanned aerial vehicle highway;

determining, by the computing system of the navigation system and based on comparing the environmental data to the specified data signature, a position of the unmanned aerial vehicle in the unmanned aerial vehicle highway; and

providing power to the navigation system by a hybrid generator system configured to;

generate mechanical energy by an engine of the hybrid generator system; and

generate electrical energy, by a generator motor coupled to the engine, from the mechanical energy generated by the engine, the electrical energy configured to power the navigation system.

22. The method of claim 21, further comprising:

storing, in a storage of the navigations system, at least a portion of a digital elevation model representing at least a portion of a navigation environment;

determine that the environmental data correspond to the portion of the digital elevation model;

determine one or more of an elevation of the unmanned aerial vehicle and a location of the unmanned aerial vehicle relative to a reference point in the digital elevation model, the reference point of the digital elevation model being a specified position in the unmanned aerial vehicle highway; and

map the position of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on i) the one or more of the elevation of the unmanned aerial vehicle and the location of the unmanned aerial vehicle relative to the reference point and ii) the specified position of the reference point in the unmanned aerial vehicle highway.

23. The method of claim 21, wherein one or more spectra comprises a plurality of spectra, and wherein the method further comprises:

generating, by the computing system, a map that;

designates a first position in the map of a first data signature as being associated with a first spectrum of the spectra; and

designates a second position of a second data signature as being associated with a second spectrum of the spectra; and

determining the position of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on the generated map designating the first position and the second position.

24. The method of claim 21, wherein the specified data signature is programmed into a synapse of a neural network, and wherein comparing the environmental data to the specified data signature comprises inputting the environmental data into the neural network.

25. The method of claim 21, wherein the computing system of the navigation system implements a neural network that is trained using a plurality of data signatures from a feature database;

and

wherein the navigation system is configured to update the feature database with the gathered environmental data.

26. The method of claim 21, further comprising:

executing, by the computing system, a simulation concurrently with gathering the environmental data to predict values of subsequently gathered environmental data; and

comparing, by the computing system, the predicted values of the subsequently gathered environmental data to measured values of subsequently gathered environmental data; and

updating, by the computing system, the simulation based on a difference between the predicted values and the measured values.

27. The method of claim 21, further comprising:

receiving data indicative of a congested portion of the unmanned aerial vehicle highway; and

planning a path of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on the received data to at least partially avoid the congested portion.

28. A non-transitory computer readable medium storing instructions that are executable by computing system configured to perform operations comprising:

receiving, from one or sensors of a navigation system, environmental data in one or more spectra;

comparing the environmental data to a specified data signature in the one or more spectra, the specified data signature being associated with an unmanned aerial vehicle highway; and

determining, based on comparing the environmental data to the specified data signature, a position of the unmanned aerial vehicle in the unmanned aerial vehicle highway.

29. The non-transitory computer readable medium of claim 28, wherein one or more spectra comprises a plurality of spectra, and wherein the operations further comprise:

generating, by the computing system, a map that;

designates a first position in the map of a first data signature as being associated with a first spectrum of the spectra; and

designates a second position of a second data signature as being associated with a second spectrum of the spectra; and

determining the position of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on the generated map designating the first position and the second position.

30. The non-transitory computer readable medium of claim 28, wherein the operations further comprise:

receiving data indicative of a congested portion of the unmanned aerial vehicle highway; and

planning a path of the unmanned aerial vehicle in the unmanned aerial vehicle highway based on the received data to at least partially avoid the congested portion.