Method and apparatus for automatically calibrating vehicle parameters

• US 8,548,671 B2
• Filed: 06/06/2011
• Issued: 10/01/2013
• Est. Priority Date: 06/06/2011
• Status: Active Grant

• Current Assignee: Crown Equipment Corporation
• Original Assignee: Crown Equipment Corporation
• Inventors: Graham, Andrew Evan, Goode, Christopher W., Wong, Lisa
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Kiswanto, Nicholas
• Application Number:
  US13/153,743
• Publication Number:
  US 20120310468A1
• Time in Patent Office: 848 Days
• Field of Search: 701/29.1
• US Class Current: 701/29.1
• 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

Abstract

A method and apparatus for automatically calibrating vehicle parameters is described. In one embodiment, the method includes measuring a parameter value during vehicle operation; comparing the measured parameter value to an actual parameter value to identify at least one vehicle parameter bias as an actual measurement error; and modifying at least one vehicle parameter based on the at least one vehicle parameter bias.

Claims

1. A computer implemented method of automatically calibrating a vehicle parameter of an automated industrial vehicle, the method comprising:

operating the automated industrial vehicle over a period of time that is sufficient for a vehicle parameter to change due to an external factor or normal wear of a component of the industrial vehicle, wherein the vehicle parameter represents a physical characteristic related to the industrial vehicle;

analyzing error in a system measurement comprising an actual value y that is not directly observable and an observed value ŷ

, wherein the observed value ŷ

is obtained using noisy sensor readings from two different measurement models ŷ

₁, ŷ

₂, wherein

ŷ

₁=f(s₁, p₁),

ŷ

₂=f(s₂, p₂),

s₁ and s₂ represent first and second sets of noisy sensor readings from two independent measures of the vehicle parameter from disparate data sources, and

p₁ and p₂ represent first and second sets of parameters that relate the first and second sets of noisy sensor readings from the two independent measures of the vehicle parameter to the system measurement;

adjusting the first and second sets of parameters p₁ and p₂ using an observed error represented by a difference between observed values from the two different measurement models ŷ

₁, ŷ

₂; and

associating vehicle operation with the adjusted parameters and operating the industrial vehicle according to the association.

2. The method of claim 1 wherein:

the automated industrial vehicle is operated over a period of time that is sufficient for a difference between observed values from the two different measurement models ŷ

₁, ŷ

₂, to yield a non-zero mean observed error ε

=ŷ

₁−

ŷ

₂; and

the first and second sets of parameters p₁ and p₂ are adjusted to return the observed error ε

=ŷ

₁−

ŷ

₂ to zero mean.

3. The method of claim 2, wherein the non-zero mean observed error ε

represents a vehicle parameter bias that comprises at least one of a wheel diameter bias, a wheel slip bias, sensor offset, vehicle height variation, or steering alignment variation.

4. The method of claim 1, wherein the vehicle parameter is selected from at least one of wheel diameter, sensor offset, vehicle height, steering alignment, and wheel track.

5. The method of claim 1 wherein the automated industrial vehicle is operated over a period of time that is sufficient for the first and second sets of noisy sensor readings from the two independent measures of the vehicle parameter to comprise sufficient data to observe a trend in the data.

6. The method of claim 5 wherein:

the method comprises characterizing the observed trend in the data as a drift or a jump; and

correlating the drift or jump with one of a number of vehicle parameters to identify an error source.

7. The method of claim 6 wherein the method comprises:

correlating a drift in the data with vehicle wear as the error source; and

correlating a jump in the data with data sensor displacement as the error source.

8. The method of claim 7 wherein the vehicle wear comprises a change in wheel diameter and the data sensor displacement comprises a change in laser pose.

9. The method of claim 1 wherein:

the automated industrial vehicle is operated over a period of time that is sufficient to permit selection of appropriate analysis data from the first and second sets of noisy sensor readings; and

the first and second sets of parameters p₁ and p₂ are adjusted using a selection of appropriate analysis data from the first and second sets of noisy sensor readings.

10. The method of claim 9 wherein the appropriate analysis data is selected from the first and second sets of noisy sensor readings to represent data collected when the industrial vehicle is driving in a straight line.

11. The method of claim 1 wherein the first and second sets of noisy sensor readings are taken from a sensor array comprising one or more cameras.

12. The method of claim 11 wherein the one or more cameras are mounted externally on the industrial vehicle.

13. The method of claim 11 wherein the sensor array further comprises one or more laser scanners, one or more encoders coupled to one or more wheels of the industrial vehicle, a fork height sensor, or a steering angle sensor.

14. The method of claim 1 further comprising navigating the industrial vehicle using the adjusted parameters.