Distributed drone flight path builder system

• US 10,035,593 B2
• Filed: 04/06/2016
• Issued: 07/31/2018
• Est. Priority Date: 04/06/2015
• Status: Active Grant

• Current Assignee: Omniscience Corporation
• Original Assignee: Omniscience Corporation
• Inventors: Shukla, Manu
• Primary Examiner(s): Jeanglaude, Gertrude Arthur
• Application Number:
  US15/092,004
• Publication Number:
  US 20160325832A1
• Time in Patent Office: 846 Days
• Field of Search: 701 3, 348144, 348148
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64U 10/14 with four distinct rotor axes, e.g. quadcopters
• 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 2201/10 autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
• CPC Class Codes: G08G 5/0013 with a ground station
• CPC Class Codes: G08G 5/0026 located on the ground
• CPC Class Codes: G08G 5/0034 Assembly of a flight plan
• CPC Class Codes: G08G 5/006 in accordance with predefined flight zones, e.g. to avoid prohibited zones
• CPC Class Codes: G08G 5/0069 specially adapted for an unmanned aircraft

Abstract

Drones have become ubiquitous in performing risky and labor intensive areal tasks cheaply and safely. To allow them to be autonomous, their flight plan needs to be pre-built for them. Existing works do not precalculate flight paths but instead focus on navigation through camera based image processing techniques, genetic or geometric algorithms to guide the drone during flight. That makes flight navigation complex and risky. We present automated flight plan builder DIFPL which pre-builds flight plans for drones to survey a large area. The flight plans are built for subregions and fed into drones which allow them to navigate autonomously. DIFPL employs distributed paradigm on Hadoop MapReduce framework. Distribution is achieved by processing sections or subregions in parallel. Experiments performed with network and elevation datasets validate the efficiency of DIFPL in building optimal flight plans.

Claims

1. A method to generate a flight plan for an area to be surveyed, the method comprising:

identifying a plurality of subregions of an area to be surveyed;

constructing, based on network line and elevation data associated with the plurality of subregions, a flight plan portion for each of the plurality of subregions; and

storing a first subset of the flight plan portions in a memory of a first drone so as to enable autonomous navigation by the first drone within the area to be surveyed.

2. The method of claim 1, including storing a second subset of the flight plan portions in a memory of a second drone, so as to enable autonomous navigation by the second drone within the area to be surveyed.

3. The method of claim 2, including identifying the first drone within which to store the first subset of flight plan portions based on capabilities of the first drone, and identifying the second run within which to store the second subset of the flight plan portions based on capabilities of the second drone, wherein the capabilities of the first and second drones are different.

4. The method of claim 1, wherein the constructing of the flight plan portion for each of the plurality of subregions is distributed within a MapReduce framework.

5. The method of claim 1, including minimizing the number of subregions by merging boundary regions included in the plurality of subregions after constructing the flight plan portions for the plurality of subregions.

6. A system comprising:

at least one processor; and

at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to;

identify a plurality of subregions of an area to be surveyed;

construct, based on network line and elevation data associated with the plurality of subregions, a flight plan portion for each of the plurality of subregions; and

store a first subset of the flight plan portions in a memory of a first drone so as to enable autonomous navigation by the first drone within the area to be surveyed.

7. The system of claim 6, including storing a second subset of the flight plan portions in a memory of a second drone, so as to enable autonomous navigation by the second drone within the area to be surveyed.

8. The system of claim 7, including identifying the first drone within which to store the first subset of flight plan portions based on capabilities of the first drone, and identifying the second run within which to store the second subset of the flight plan portions based on capabilities of the second drone, wherein the capabilities of the first and second drones are different.

9. The system of claim 6, wherein the constructing of the flight plan portion for each of the plurality of subregions is distributed within a MapReduce framework.

10. The system of claim 6, including minimizing the number of subregions by merging boundary regions included in the plurality of subregions after constructing the flight plan portions for the plurality of subregions.

11. A non-transitory computer-readable storage medium, the computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to:

identify a plurality of subregions of an area to be surveyed;

construct, based on network line and elevation data associated with the plurality of subregions, a flight plan portion for each of the plurality of subregions; and

store a first subset of the flight plan portions in a memory of a first drone so as to enable autonomous navigation by the first drone within the area to be surveyed.

12. The computer-readable storage medium of claim 11, including storing a second subset of the flight plan portions in a memory of a second drone, so as to enable autonomous navigation by the second drone within the area to be surveyed.

13. The computer-readable storage medium of claim 12, including identifying the first drone within which to store the first subset of flight plan portions based on capabilities of the first drone, and identifying the second run within which to store the second subset of the flight plan portions based on capabilities of the second drone, wherein the capabilities of the first and second drones are different.

14. The computer-readable storage medium of claim 11, wherein the constructing of the flight plan portion for each of the plurality of subregions is distributed within a MapReduce framework.

15. The computer-readable storage medium of claim 11, including minimizing the number of subregions by merging boundary regions included in the plurality of subregions after constructing the flight plan portions for the plurality of subregions.