System for incremental trajectory estimation based on real time inertial sensing

• US 10,408,622 B2
• Filed: 11/28/2017
• Issued: 09/10/2019
• Est. Priority Date: 11/29/2016
• Status: Active Grant

• Current Assignee: HRL Laboratories LLC (The Boeing Co.)
• Original Assignee: HRL Laboratories LLC (The Boeing Co.)
• Inventors: Wang, Shuoqin, Sorenson, Logan D., Nguyen, Hung, Chang, David, Perahia, Raviv
• Primary Examiner(s): Whittington, Jess
• Application Number:
  US15/825,067
• Publication Number:
  US 20180149480A1
• Time in Patent Office: 651 Days
• CPC Class Codes: E21B 44/005 Below-ground automatic control systems
• CPC Class Codes: E21B 47/09 Locating or determining the position of objects in boreholes or wells , e.g. the position of an extending arm; Identifying the free or blocked portions of pipes
• CPC Class Codes: E21B 7/04 Directional drilling
• CPC Class Codes: G01C 21/08 involving use of the magnetic field of the earth
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G01C 21/18 Stabilised platforms, e.g. by gyroscope
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence

Abstract

Described is a system for incremental trajectory estimation of an implement. During operation, the system determines a time span of each stationary period of the implement based on accelerometer and gyroscopic data. Gyroscopic bias is then estimated based on the time span and gyroscopic data. An attitude of the implement is then estimated at each time step based on the estimated gyroscopic bias and gyroscopic data. Further, a traveling distance of the implement is estimated. Finally, a trajectory of the implement is estimated based on the estimated attitude and traveling distance. Given the trajectory estimate, an implement (e.g., drilling platform, vehicle, etc.) can be caused to alter its direction based on the trajectory estimate.

Claims

1. A system for incremental trajectory estimation of an implement, the system comprising:

one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of;

determining a time span of each stationary period of the implement based on accelerometer and gyroscopic data;

estimating gyroscopic bias based on the time span and gyroscopic data;

estimating attitude at each time step based on the estimated gyroscopic bias, accelerometer and gyroscopic data, wherein each time step is a time measurement between stationary periods;

estimating a traveling distance of the implement at each time step;

estimating at least one of a first trajectory of the implement based on the estimated attitude and traveling distance, a second trajectory of the implement, a weighted combined trajectory of the implement, and an optimized trajectory of the implement; and

causing the implement to alter direction based on at least one of the first trajectory, the second trajectory, the weighted combined trajectory, and the optimized trajectory.

2. The system as set forth in claim 1, wherein in estimating attitude at each time step, the attitude is further based on a minimization of a difference between estimated and measured magnetometer readings.

3. The system as set forth in claim 2, wherein estimating the weighted combined trajectory includes combining the estimated first and second trajectories using a weight parameter to generate the weighted combined trajectory.

4. The system as set forth in claim 3, wherein estimating the optimized trajectory further includes generating the optimized trajectory estimate based on Kalman filtering of the weighted combined trajectory.

5. The system as set forth in claim 4, further comprising an operation of optimizing the estimated traveling distance at each time step through weighting of initial travelling distance estimation and a traveling distance measurement.

6. The system as set forth in claim 1, wherein the implement is selected from a group consisting of drilling platform and a vehicle.

7. The system as set forth in claim 1, wherein estimating the traveling distance, the traveling distance is determined based on a velocity distribution over time.

8. The system as set forth in claim 1, wherein estimating the traveling distance, the traveling distance is calculated using a sine wave approximation between two stations as a function of time.

9. A computer program product for incremental trajectory estimation of an implement, the computer program product comprising:

a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions by one or more processors, the one or more processors perform operations of;

determining a time span of each stationary period of the implement based on accelerometer and gyroscopic data;

estimating gyroscopic bias based on the time span and gyroscopic data;

estimating attitude at each time step based on the estimated gyroscopic bias, accelerometer and gyroscopic data, wherein each time step is a time measurement between stationary periods;

estimating a traveling distance of the implement at each time step;

estimating a first trajectory of the implement based on the estimated attitude and traveling distance; and

causing the implement to alter direction based on the first trajectory estimate.

10. The computer program product as set forth in claim 9, wherein in estimating attitude at each time step, the attitude is further based on a minimization of a difference between estimated and measured magnetometer readings.

11. The computer program product as set forth in claim 10, wherein estimating the weighted combined trajectory includes combining the estimated first and second trajectories using a weight parameter to generate the weighted combined trajectory.

12. The computer program product as set forth in claim 11, wherein estimating the optimized trajectory further includes generating the optimized trajectory estimate based on Kalman filtering of the weighted combined trajectory.

13. The computer program product as set forth in claim 12, further comprising an operation of optimizing the estimated traveling distance at each time step through weighting of initial travelling distance estimation and a traveling distance measurement.

14. The computer program product as set forth in claim 9, wherein the implement is selected from a group consisting of drilling platform and a vehicle.

15. The computer program product as set forth in claim 9, wherein estimating the traveling distance, the traveling distance is determined based on a velocity distribution over time.

16. The computer program product as set forth in claim 9, wherein estimating the traveling distance, the traveling distance is calculated using a sine wave approximation between two stations as a function of time.

17. A computer implemented method for incremental trajectory estimation of an implement, the method comprising an act of:

causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of;

determining a time span of each stationary period of the implement based on accelerometer and gyroscopic data;

estimating gyroscopic bias based on the time span and gyroscopic data;

estimating attitude at each time step based on the estimated gyroscopic bias, accelerometer and gyroscopic data, wherein each time step is a time measurement between stationary periods;

estimating a traveling distance of the implement at each time step;

estimating a first trajectory of the implement based on the estimated attitude and traveling distance; and

causing the implement to alter direction based on the first trajectory estimate.

18. The computer implemented method as set forth in claim 17, wherein in estimating attitude at each time step, the attitude is further based on a minimization of a difference between estimated and measured magnetometer readings.

19. The computer implemented method as set forth in claim 18, wherein estimating the weighted combined trajectory includes combining the estimated first and second trajectories using a weight parameter to generate the weighted combined trajectory.

20. The computer implemented method as set forth in claim 19, wherein estimating the optimized trajectory further includes generating the optimized trajectory estimate based on Kalman filtering of the weighted combined trajectory.

21. The computer implemented method as set forth in claim 20, further comprising an operation of optimizing the estimated traveling distance at each time step through weighting of initial travelling distance estimation and a traveling distance measurement.

22. The computer implemented method as set forth in claim 17, wherein the implement is selected from a group consisting of drilling platform and a vehicle.

23. The computer implemented method as set forth in claim 17, wherein estimating the traveling distance, the traveling distance is determined based on a velocity distribution over time.

24. The computer implemented method as set forth in claim 17, wherein estimating the traveling distance, the traveling distance is calculated using a sine wave approximation between two stations as a function of time.