Unmanned aerial vehicle with neural network for enhanced mission performance
An unmanned aerial vehicle (UAV) or “drone” executes a neural network to assist with inspection, surveillance, reporting, and other missions. The drone inspection neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation to an asset along a preprogrammed flight path and/or during its mission (e.g., as it scans and inspects an asset).
1. An unmanned aerial vehicle (UAV) comprising:
a flight package;
a navigation system;
an image-acquisition device;
a communication facility configured to interact with spaceborne control systems;
a computer memory; and
a computer including a processor and electronically stored instructions, executable by the processor, for:
causing the UAV to autonomously execute a preliminary flight plan around an asset to be inspected,
using data received from the image-acquisition device as input to a neural-network-based predictor that has been computationally trained to identify and classify objects appearing in images acquired by the image-acquisition device during the preliminary flight plan,
during analysis of the data received from the image-acquisition device, detecting a feature requiring an update to the predictor,
requesting and receiving, in real time, an updated predictor computationally trained based on the detected feature, and
based on (a) object classifications made by the predictor during the preliminary flight plan and (b) the updated predictor, computing and autonomously executing a revised flight plan around the asset
wherein:
the computer is configured to analyze the detected feature in real time and classify the detected feature as normal or anomalous; and when the detected feature is classified as anomalous, the computer is configured to request airspace authorization and receive an updated configuration files for additional inspection of the asset.
2. The UAV of claim 1 , wherein the communication facility is configured to interact with terrestrial, airborne, and spaceborne control systems.
3. The UAV of claim 1 , further comprising a database of actions, the computer being configured to select and cause execution of an action from the database in response to a detected object classified by the predictor.
4. The UAV of claim 1 , further comprising a weather-surveillance module for monitoring weather conditions during flight of the UAV.
5. The UAV of claim 1 , wherein the computer is further configured to communicate with spaceborne control systems and to execute flight commands received from the spaceborne control systems.
6. The UAV of claim 1 , wherein the detected feature is an asset type, and the updated predictor comprises weight files, configuration files, and/or convolutional layers specific to the asset type.
7. The UAV of claim 1 , wherein the detected feature is passive intermodulation (PIM) or electromagnetic interference (EMI).
8. The UAV of claim 1 , wherein the computer is further configured to conduct real time quality assurance of data acquired during execution of the revised flight plan and, based thereon, determine wheatear to continue the revised flight plan or acquire additional data.
9. The UAV of claim 1 , wherein the detected feature is classified as an anomaly and wherein the computer is configured to request and receive the updated predictor in response to detection of the feature requiring the update to the predictor.
10. The UAV of claim 5 , wherein the computer is further configured to communicate the revised flight plan to the spaceborne control system for obtaining authorization from air traffic control infrastructure.
11. The UAV of claim 5 , wherein the computer is further configured to communicate the request for the updated predictor to the spaceborne control systems and receive the updated predictor from the spaceborne control system.
12. The UAV of claim 5 , wherein the computer is configured to dynamically adjust computing power of the predictor via communication with spaceborne control systems and/or a cloud neural network module.
13. A method comprising:
at an unmanned aerial vehicle (UAV) including
a flight package;
a navigation system;
an image-acquisition device;
a communication facility configured to interact with spaceborne control systems;
a computer memory; and
a computer including a processor and electronically stored instructions, executable by the processor:
causing the UAV to autonomously execute a preliminary flight plan around an asset to be inspected,
using data received from the image-acquisition device as input to a neural-network-based predictor that has been computationally trained to identify and classify objects appearing in images acquired by the image-acquisition device during the preliminary flight plan,
during analysis of the data received from the image-acquisition device, detecting a feature requiring an update to the predictor,
requesting and receiving, in real time, an updated predictor computationally trained based on the detected feature, and
based on (a) object classifications made by the predictor during the preliminary flight plan and (b) the updated predictor, computing and autonomously executing a revised flight plan around the asset,
wherein the computer is configured to analyze the detected feature in real time and classify the detected feature as normal or anomalous; and when the detected feature is classified as anomalous, the computer is configured to request airspace authorization and receive an updated configuration file for additional inspection of the asset.
14. The method of claim 13 , wherein the communication facility is configured to interact with terrestrial, airborne, and spaceborne control systems.
15. The method of claim 13 , wherein the UAV, further comprises a database of actions, the method further comprising the step of selecting and causing execution of an action from the database in response to a detected object classified by the predictor.
16. The method of claim 13 , the UAV further comprising a weather-surveillance module for monitoring weather conditions during flight of the UAV.
17. The method of claim 13 , further comprising communicating with spaceborne control systems and executing flight commands received from the spaceborne control systems.
18. The method of claim 13 , wherein the detected feature is an asset type, and the updated predictor comprises weight files, configuration files, and/or convolutional layers specific to the asset type.
19. The method of claim 13 , wherein the detected feature is passive intermodulation (PIM) or electromagnetic interference (EMI).
20. The method of claim 13 , further comprising conducting real time quality assurance of data acquired during execution of the revised flight plan and, based thereon, determining whether to continue the revised flight plan or acquire additional data.
21. The method of claim 13 , further comprising, when the detected feature is classified as anomalous, requesting and receiving the updated predictor in response to detection of the feature requiring the update to the predictor.
22. The method of claim 17 , further comprising communicating the revised flight plan to the spaceborne control system for obtaining authorization from air traffic control infrastructure.
23. The method of claim 17 , further comprising communicating the request for the updated predictor to the spaceborne control systems and receive the updated predictor from the spaceborne control system.
24. The method of claim 17 , further comprising dynamically adjusting computing power of the predictor via communication with spaceborne control systems and/or a cloud neural network module.