Drone-based administration of remotely located instruments and gadgets

Patent metadata

Field	Value
Publication number	US-11978023-B2
Application number	US-202117467823-A
Country	US
Kind code	B2
Filing date	Sep 7, 2021
Priority date	Sep 11, 2020
Publication date	May 7, 2024
Grant date	May 7, 2024

Abstract

The present disclosure describes various embodiments of systems, apparatuses, and methods for drone-based administration of remotely located devices. One such method comprises deploying an unmanned aerial vehicle from a base station, wherein the base station assigns a maintenance order to the unmanned aerial vehicle for servicing of a remote device, traveling, by the unmanned aerial vehicle, to the location of the remote device, authenticating, by the unmanned aerial vehicle, a valid identification of the remote device; upon the remote device being authenticated by the unmanned aerial vehicle, servicing the remote device by at least charging a power supply of the remote device and transferring contents of a device log to the unmanned aerial vehicle; and after completing the servicing of the remote device; returning to the base station and transferring contents of the device log to the base station.

First claim

The invention claimed is:

1. A method comprising:
deploying, from a base station, an unmanned aerial vehicle from a group of unmanned aerial vehicles, wherein the base station assigns a maintenance order to the unmanned aerial vehicle for servicing of a remote device, wherein the maintenance order includes a location for the remote device;
traveling, by the unmanned aerial vehicle, to the location of the remote device, wherein the unmanned aerial vehicle self-navigates to the location of the remote device;
authenticating, by the unmanned aerial vehicle, a valid identification of the remote device;
upon the remote device being authenticated by the unmanned aerial vehicle, servicing the remote device by at least charging a power supply of the remote device and transferring contents of a device log to the unmanned aerial vehicle;
predicting, by the unmanned aerial vehicle using an artificial intelligence model, that the transferred device log shows that remote device has a maintenance issue that is not currently scheduled to be serviced by the base station;
servicing, by the unmanned aerial vehicle, the maintenance issue before returning to the base station; and
after completing the servicing of the remote device, returning to the base station and transferring contents of the device log to the base station.

2. The method of claim 1 , further comprising after returning to the base station, charging a power supply of the unmanned aerial vehicle.

3. The method of claim 1 , wherein the unmanned aerial vehicle is equipped with a camera, the method further comprising using the camera to gather information on the remote device and identify a maintenance configuration for accessing a charge port and a device log port of the remote device to enable charging of the power supply from the unmanned aerial vehicle and transferring of the device log of the remote device.

4. The method of claim 1 , wherein the unmanned aerial vehicle is equipped with a camera and a robotic arm, the method further comprising using the camera to gather information on the remote device and identify a maintenance configuration for accessing a swappable battery and a swappable memory card containing the device log, wherein the unmanned aerial vehicle utilizes the robotic arm to physically swap out the swappable battery with a replacement battery and the swappable memory card with a replacement memory card on the remote device.

5. The method of claim 4 , further comprising:
assessing, by the unmanned aerial vehicle, whether it is safe to land near the remote device; and
if the unmanned aerial vehicle determines that it is not safe to land near the remote device, detaching a maintenance platform that is secured to a terrain next to the remote device using the robotic arm, wherein the maintenance platform carries a power source for charging or replacing the power source of the remote device and a data transfer storage medium for storing the contents of the device log, wherein the maintenance platform is configured to carry out servicing of the remote device while the unmanned aerial vehicle returns to the base station or services another remote device.

6. The method of claim 1 , further comprising before servicing the remote device, performing, by the unmanned aerial vehicle, a pre-maintenance routine to check a health of the remote device.

7. The method of claim 1 , wherein the remote device authenticates an identification of the unmanned aerial vehicle to be valid before allowing a transfer of the device log to the unmanned aerial vehicle.

8. The method of claim 1 , further comprising predicting, by the base station using an artificial intelligence model, a maintenance issue involving the remote device based on a plurality of device logs returned from a plurality of unmanned aerial vehicles that service a plurality of remote devices.

9. The method of claim 8 , wherein the unmanned aerial vehicle and the base station collectively utilize an artificial intelligence for modeling.

10. The method of claim 8 , wherein the base station authenticates an identification of the unmanned aerial vehicle to be valid before allowing a transfer of the device log from the unmanned aerial vehicle to the base station.

11. The method of claim 1 , wherein the remote device comprises a home automation device, a security device, a sensor device, or a medical device.

12. The method of claim 1 , further comprising:
detecting, by the unmanned aerial vehicle, a distress signal issued by a different remote device that is not being serviced by the unmanned aerial vehicle; and
relaying, by the unmanned aerial vehicle, the distress signal to another unmanned aerial vehicle, another remote device, or the base station, wherein the distress signal is relayed until the distress signal is transmitted to the base station.

13. A system comprising:
a base station; and
an unmanned aerial vehicle configured to:
receive a maintenance order for servicing of a remote device from the base station, wherein the maintenance order includes a location for the remote device;
travel to the location of the remote device, wherein the unmanned aerial vehicle self-navigates to the location of the remote device;
authenticate a valid identification of the remote device;
upon the remote device being authenticated by the unmanned aerial vehicle, service the remote device by at least charging a power supply of the remote device and transferring contents from a device, including its log, to the unmanned aerial vehicle; and
after completing the servicing of the remote device, return to the base station and transfer contents from a device, including its log, to the base station,
wherein the unmanned aerial vehicle is configured to predict, using an artificial intelligence model, that the transferred device log show that remote device has a maintenance issue that is not currently scheduled to be serviced by the base station.

14. The system of claim 13 , wherein the unmanned aerial vehicle is equipped with a camera, wherein the unmanned aerial vehicle is configured to gather information on the remote device using the camera and identify a maintenance configuration for accessing a charge port and a device log port of the remote device to enable charging of the power supply from the unmanned aerial vehicle and transferring of the device log of the remote device.

15. The system of claim 13 , wherein the unmanned aerial vehicle is equipped with a camera and a robotic arm, wherein the unmanned aerial vehicle is configured to gather information on the remote device using the camera and identify a maintenance configuration for accessing a swappable battery and a swappable memory card containing the device log, wherein the unmanned aerial vehicle is configured to connect a maintenance platform carrying a replacement battery and a replacement memory card to the remote device and utilize the robotic arm to physically swap out the swappable battery with the replacement battery and the swappable memory card with the replacement memory card on the remote device.

16. The system of claim 13 , wherein the base station is configured to predict, using an artificial intelligence model, a maintenance issue involving the remote device based on a plurality of device logs returned from a plurality of unmanned aerial vehicles that service a plurality of remote devices, wherein the plurality of unmanned aerial vehicles comprise the unmanned aerial vehicle.

17. The system of claim 16 , wherein the base station is configured to authenticate an identification of the unmanned aerial vehicle to be v

Assignees

• Univ Florida

Inventors

• Bhunia Swarup
• Chakraborty Prabuddha
• Dizon Reiner
• Difuntorum Parker
• Vega Christopher
• Sristi Lakshmiprasanna Sriramakumara Patanjali

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• B64U2201/10

  autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS] · CPC title

• B64U10/13

  Flying platforms · CPC title

• B64U50/37

  Charging when not in flight · CPC title