Systems and methods for materials handling vehicle odometry calibration

• US 9,921,067 B2
• Filed: 05/19/2016
• Issued: 03/20/2018
• Est. Priority Date: 05/26/2015
• Status: Active Grant

• Current Assignee: Crown Equipment Corporation
• Original Assignee: Crown Equipment Corporation
• Inventors: Fanselow, Timothy William, Thode, Justin Forbes, Estep, Ryan Michaels
• Primary Examiner(s): MELTON, TODD M
• Application Number:
  US15/158,896
• Publication Number:
  US 20160349061A1
• Time in Patent Office: 670 Days
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 22/02 by conversion into electric waveforms and subsequent integration, e.g. using tachometer generator G01C22/002, G01C22/004, G01C22/006 take precedence
• CPC Class Codes: G01C 25/00 Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass testing, calibrating or compensating compasses G01C17/38
• CPC Class Codes: G05D 1/021 specially adapted to land vehicles

Abstract

Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Claims

1. A materials handling vehicle comprising materials handling hardware, a wheel, an odometer, a positioning system, and a vehicle computing device, wherein the vehicle computing device stores logic that when executed by a processor, causes the materials handling vehicle to perform at least the following:

determine a current location of the materials handling vehicle within a warehouse;

determine, via the odometer, an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of the wheel and a circumference of the wheel;

determine, via the positioning system, a positioning system distance from the current location of the materials handling vehicle to the destination;

compare the odometry distance with the positioning system distance to calculate a scaling factor;

apply the scaling factor to a slow alpha filter to achieve a slow filter result, the slow alpha filter representing a slow change to the odometry distance, based on wear of the wheel;

apply the scaling factor to a fast alpha filter to achieve a fast filter result, the fast alpha filter representing a fast change to the odometry distance, based on changing of the wheel;

compare the slow filter result with the fast filter result;

in response to determining that a difference between the slow filter result and the fast filter result is within a predetermined tolerance, apply the slow filter result to the scaling factor to smooth noise;

in response to determining that a difference between the slow filter result and the fast filter result is not within the predetermined tolerance, apply the fast filter result to the scaling factor to smooth noise;

calculate an updated odometry distance utilizing the scaling factor; and

utilize the updated odometry distance to operate the positioning system.

2. The materials handling vehicle of claim 1, wherein the slow alpha filter includes calculating the following:

FV₁=FV₀(α

)+MV (1−

α

), wherein FV₁ is a new filter value, FV₀ is a prior filter value, MV is a measured value, and α

is an alpha value of the slow alpha filter, and wherein the alpha value is equal to about 0.99.

3. The materials handling vehicle of claim 1, wherein the fast alpha filter includes calculating the following:

FV₁=FV₀(α

)+MV (1−

α

), wherein FV₁ is a new filter value, FV₀ is a prior filter value, MV is a measured value, and α

is an alpha value of the fast alpha filter, and wherein the alpha value is equal to about 0.9.

4. The materials handling vehicle of claim 1, wherein the predetermined tolerance is equal to about 0.075.

5. The materials handling vehicle of claim 1, wherein the logic further causes the materials handling vehicle to apply a hysteresis value to switch from the fast alpha filter to the slow alpha filter, wherein in response to determining that a difference between the slow filter result and the fast filter result is less than the hysteresis value, the logic causes the materials handling vehicle to switch from the fast alpha filter to the slow alpha filter.

6. The materials handling vehicle of claim 5, wherein the hysteresis value is about 0.0025.

7. The materials handling vehicle of claim 5, wherein the predetermined tolerance and the hysteresis value are determined by logging a raw scaling factor measurement on the materials handling vehicle.

8. The materials handling vehicle of claim 1, wherein the logic further causes the materials handling vehicle to determine whether a change in the scaling factor is a result of at least one of the following:

wheel wear and wheel change.

9. The materials handling vehicle of claim 1, wherein the current location of the materials handling vehicle is received from at least one of the following:

a user input and triggering of a sensor.

10. The materials handling vehicle of claim 1, further comprising an image capture device that is coupled to the vehicle computing device, wherein the image capture device captures images for identifying the positioning system distance.

11. The materials handling vehicle of claim 1, wherein the logic causes the materials handling vehicle to traverse a predetermined route to reach the destination.

12. The materials handling vehicle of claim 1, wherein calculating the scaling factor comprises dividing the positioning system distance by the odometry distance.

13. The materials handling vehicle of claim 1, wherein logic causes the materials handling vehicle to define a travel distance of that is dependent on a difference between the odometry distance and the positioning system distance.

14. The materials handling vehicle of claim 1, wherein a remote computing device sends data related to a vehicle position to the vehicle computing device.

15. A materials handling vehicle comprising a wheel and a vehicle computing device, wherein:

the vehicle computing device stores logic that when executed by a processor, causes the materials handling vehicle to perform at least the following;

determine a current location of the materials handling vehicle within a warehouse;

determine an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of the wheel and a circumference of the wheel;

determine a positioning system distance from the current location to the destination;

compare the odometry distance with the positioning system distance to calculate a scaling factor;

apply the scaling factor to a slow alpha filter to achieve a slow filter result;

apply the scaling factor to a fast alpha filter to achieve a fast filter result;

in response to determining that the slow filter result is within a predetermined tolerance of the fast filter result, apply the slow alpha filter to the scaling factor to smooth noise;

calculate an updated odometry distance utilizing the scaling factor; and

utilize the updated odometry distance to operate a positioning system.

16. The materials handling vehicle of claim 15, wherein calculating the scaling factor comprises dividing the positioning system distance by the odometry distance.

17. The materials handling vehicle of claim 15, wherein, in response to determining that the slow filter result is not within the predetermined tolerance of the fast filter result, the logic further causes the materials handling vehicle to apply the fast alpha filter to the scaling factor to smooth noise.

18. A method for calibrating odometry of a materials handling vehicle comprising:

determining a current location of the materials handling vehicle within a warehouse;

determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel;

determining a positioning system distance from the current location to the destination;

comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, wherein calculating the scaling factor comprises dividing the positioning system distance by the odometry distance;

applying the scaling factor to a fast alpha filter to achieve a fast filter result;

applying the scaling factor to a slow alpha filter to achieve a slow filter result;

in response to determining that the slow filter result is not within a predetermined tolerance of the fast filter result, applying the fast alpha filter to the scaling factor to smooth noise;

calculating an updated odometry distance utilizing the scaling factor; and

utilizing the updated odometry distance to operate a positioning system.

19. The method of claim 18, further comprising, in response to determining that the slow filter result is within the predetermined tolerance of the fast filter result, apply the slow alpha filter to the scaling factor to smooth noise.

20. The method of claim 18, wherein applying the scaling factor to the slow alpha filter includes calculating the following:

FV₁=FV₀(α

)+MV (1−

α

), wherein FV₁ is a new filter value, FV₀ is a prior filter value, MV is a measured value, and α

is an alpha value of the slow alpha filter, wherein the alpha value is equal to about 0.99, and wherein applying the scaling factor to the fast alpha filter includes calculating the following;

FV₁=FV₀(α

)+MV (1−

α

), wherein FV₁ is a new filter value, FV₀ is a prior filter value, MV is the measured value, and α

is the alpha value of the fast alpha filter, and wherein the alpha value is equal to about 0.9.