Navigation system for a drone

1. An unmanned aerial vehicle comprising:

an energy source;

a navigation system powered by energy provided by the energy source, the navigation system configured to:

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

determine, based on the comparison, a position of the unmanned aerial vehicle in the unmanned aerial vehicle highway.

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

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

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

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 representation of predefined paths that 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.

6. The unmanned aerial vehicle of claim 1 , wherein the unmanned aerial vehicle highway comprises a representation of predefined paths that 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.

7. The unmanned aerial vehicle of claim 1 , wherein the unmanned aerial vehicle highway comprises a representation of predefined paths that 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.

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

generate a representation that designates a first position 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 designated first position and second position.

9. The unmanned aerial vehicle of claim 1 , wherein 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.

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

execute a simulation 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.

11. The unmanned aerial vehicle of claim 1 , wherein 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.

12. The unmanned aerial vehicle of claim 1 , wherein the energy source comprises a battery.

13. The unmanned aerial vehicle of claim 12 , wherein the battery is configured to be charged by energy generated by an energy generation component, wherein the battery provides electrical energy to the navigation system.

14. The unmanned aerial vehicle of claim 1 , wherein one of the energy providing components of the energy system comprises a generator coupled to an engine, the generator configured to generate electrical energy from mechanical energy generated by the engine.

15. 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.

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

gathering, by one or sensors of the unmanned aerial vehicle, environmental data in one or more spectra;

comparing, by a navigation system of the unmanned aerial vehicle, the environmental data to a specified data signature, the specified data signature being associated with an unmanned aerial vehicle highway; and

determining, by 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.

17. The method of claim 16 , comprising:

storing at least a portion of a digital elevation model representing at least a portion of a navigation environment;

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

determining 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

mapping 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.

18. The method of claim 16 , wherein one or more spectra comprise multiple spectra, and wherein the method comprises:

generating a representation that designates a first position a first data signature as being associated with a first spectrum of the spectra, and 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 designated first position and second position.

19. The method of claim 16 , wherein 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.

20. The method of claim 16 , comprising:

executing a simulation to predict values of subsequently gathered environmental data; and

comparing 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.

21. The method of claim 16 , 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.