Wheel aligner with advanced diagnostics and no-stop positioning

• US 10,072,926 B2
• Filed: 10/06/2016
• Issued: 09/11/2018
• Est. Priority Date: 10/06/2015
• Status: Expired due to Fees

• Current Assignee: Snap-On Incorporated
• Original Assignee: Snap-On Incorporated
• Inventors: Rogers, Steven W., Jackson, David A., Lewis, Bradley, Brown, Adam C., D'Agostino, Robert J., Sellers, Eric R.
• Primary Examiner(s): Safaipour, Bobbak
• Application Number:
  US15/287,528
• Publication Number:
  US 20170097229A1
• Time in Patent Office: 705 Days
• CPC Class Codes: B60R 11/04 Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
• CPC Class Codes: B60S 5/00 Servicing, maintaining, repairing, or refitting of vehicles vehicles adapted to carry a workshop for servicing or maintenance B60P3/14; servicing rail locomotives B61K
• CPC Class Codes: B62D 17/00 Means on vehicles for adjusting camber, castor, or toe-in
• CPC Class Codes: G01B 11/275 for testing wheel alignment
• CPC Class Codes: G01B 11/2755 using photoelectric detection means
• CPC Class Codes: G01B 2210/12 Method or fixture for calibrating the wheel aligner
• CPC Class Codes: G01B 2210/14 One or more cameras or other optical devices capable of acquiring a two-dimensional image
• CPC Class Codes: G01B 2210/26 Algorithms, instructions, databases, computerized methods and graphical user interfaces employed by a user in conjunction with the wheel aligner
• CPC Class Codes: G01B 2210/30 Reference markings, reflector, scale or other passive device
• CPC Class Codes: G01M 17/013 Wheels
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30164 Workpiece; Machine component
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/60 Analysis of geometric attributes
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 20/00 Scenes; Scene-specific elements control of digital cameras H04N23/60
• CPC Class Codes: G08B 5/22 using electric transmission; using electromagnetic transmission
• CPC Class Codes: H04N 23/90 Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

A vehicle wheel alignment system has a plurality of cameras, each camera for viewing a respective target disposed at a respective wheel of the vehicle and capturing image data of the target as the wheel and target are continuously rotated a number of degrees of rotation without a pause. The image data is used to calculate a minimum number of poses of the target of at least one pose for every five degrees of rotation as the wheel and target are continuously rotated the number of degrees of rotation without a pause. At least one of the cameras comprises a data processor for performing the steps of preprocessing the image data, and calculating an alignment parameter for the vehicle based on the preprocessed image data.

Claims

1. A vehicle wheel alignment system comprising:

a plurality of cameras, each camera for viewing a respective target disposed at a respective wheel of the vehicle and capturing image data of the target as the wheel and target are continuously rotated a number of degrees of rotation without a pause,

wherein the image data is used to calculate a minimum number of poses of the target;

wherein the minimum number of poses of the target comprises at least one pose for every five degrees of rotation captured by each camera as the wheel and target are continuously rotated the number of degrees of rotation without a pause; and

wherein at least one of the cameras comprises a data processor for performing the steps of;

preprocessing the image data; and

calculating an alignment parameter for the vehicle based on the preprocessed image data.

2. The system of claim 1, wherein the data processor of one of the cameras is for serving a user interface.

3. The system of claim 1, wherein the data processor is for calculating a change in an alignment parameter for each vehicle wheel based on the preprocessed image data, and for analyzing the change in the alignment parameter to detect an error.

4. The system of claim 3, where the detected error includes at least one of (a) vehicle wheel axis precession;

(b) surface flatness errors of a surface of an alignment rack on which the vehicle wheels and targets rotate;

(c) plate slipping errors of a plate which comprises part of the surface of the alignment rack; and

(d) stress in a suspension of the vehicle beyond predetermined rest conditions.

5. The system of claim 4, wherein the data processor is for attempting to correct or compensate for detected problems (a)-(d).

6. The system of claim 5, wherein the data processor is for alerting a user of the system if the processor cannot correct the detected problems.

7. The system of claim 1, wherein the data processor is for:

detecting, at least in part based on the image data, at least one of (e) instability of one or more of the cameras; and

(f) excessive vehicle thrust angle changes.

8. The system of claim 7, wherein the data processor is for attempting to correct or compensate for detected problems (e)-(f).

9. The system of claim 8, wherein the data processor is for alerting a user of the system if the processor cannot correct the detected problems.

10. The system of claim 1, wherein calculating the poses comprises (g) acquiring, as a first wheel is rotating, a first pose of the target at the first wheel, (h) storing the first pose when the first pose is different from previously stored target poses of the first wheel to a predetermined degree, (i) repeating steps (g) and (h) when the first wheel has been rotated less than the minimum number of degrees of rotation;

and (j) performing steps (g) through (i) for all the wheels of the vehicle having a target.

11. The system of claim 5, wherein detecting surface flatness errors of the surface of the alignment rack comprises calculating the center point of at least one of the vehicle wheels for each of the captured target poses of that wheel, computing a best fit line through all the computed wheel center points, and determining whether a deviation from the best fit line is below a predetermined error threshold.

12. The system of claim 11, wherein when the deviation from the best fit line is above the error threshold, the step of attempting to correct or compensate for the surface flatness error comprises (k) removing the wheel center point with the largest deviation from the best fit line, (l) recomputing the deviation, (m) determining whether the deviation is below the error threshold, (n) repeating steps (k) through (m) until the deviation from the best fit line is below the error threshold or until less than a predetermined minimum number of data points remain.

13. The system of claim 12, wherein the data processor is for alerting the user the processor cannot correct the surface flatness error if less than the minimum number of data points remain.

14. The system of claim 5, wherein detecting plate slipping errors comprises detecting translation of the center of one of the vehicle wheels without a predetermined amount of angular rotation of that wheel.

15. The system of claim 14, wherein detecting plate slipping errors comprises calculating the center point of the one of the vehicle wheels for each of the captured target poses of that wheel, determining an elapsed linear travel distance of the center point and a corresponding elapsed angle of wheel rotation, computing a best fit line of elapsed linear travel distance versus elapsed angle of wheel rotation, and determining whether a deviation from the best fit line is below a predetermined error threshold.

16. The system of claim 15, wherein when the deviation from the best fit line is above the error threshold, the step of attempting to correct or compensate for the plate slipping error comprises (o) removing the wheel center point with the largest deviation from the best fit line, (p) recomputing the deviation, (q) determining whether the deviation is below the error threshold, (r) repeating steps (o) through (q) until the deviation from the best fit line is below the error threshold or until less than a predetermined minimum number of data points remain.

17. The system of claim 16, wherein the data processor is for alerting the user the processor cannot correct the plate slipping error if less than the minimum number of data points remain.

18. The system of claim 5, wherein detecting wheel axis precession comprises calculating the wheel axis of rotation vectors of one of the vehicle wheels for each of the captured target poses of that wheel, calculating an axis of precession based on the calculated wheel axes of rotation, calculating a wheel wobble angle between the axis of precession and each of the calculated wheel axes of rotation, calculating a standard deviation of the calculated wobble angles, and determining whether the standard deviation of the wobble angles is below a predetermined error threshold.

19. The system of claim 18, wherein when the standard deviation of the wobble angles is below the error threshold, the step of attempting to correct or compensate for wheel axis precession comprises assigning the axis of precession to be the true wheel axis for a following alignment step.

20. The system of claim 19, wherein when the standard deviation of the wobble angles is above the error threshold, alerting the user the processor cannot correct for wheel axis precession.

21. The system of claim 18, wherein computing the axis of precession comprises computing a best fit plane through the tips of the calculated wheel axis vectors, and determining whether a deviation from the best fit plane is below a predetermined root mean square (RMS) error threshold;

wherein when the deviation from the best fit plane is above the RMS error threshold, the step of attempting to correct or compensate for wheel axis precession comprises (s) removing the wheel axis of rotation vector with the largest deviation from the best fit plane, (t) recomputing the best fit plane, (u) determining whether the deviation is below the RMS error threshold, (v) repeating steps (s) through (u) until the RMS deviation from the best fit plane is below the error threshold or until less than a predetermined minimum number of data points remain.

22. The system of claim 21, wherein the processor is for alerting the user the processor cannot correct for wheel axis precession if less than the minimum number of data points remain, or assigning the normal of the best fit plane to be the axis of precession if the deviation is below the RMS error threshold and more than the minimum number of data points remain.

23. The system of claim 7, comprising first and second camera pods;

wherein the first camera pod comprises a first one of the plurality of cameras for viewing one of the respective targets disposed at a respective wheel of the vehicle, and a calibration target disposed in a known relationship to the first camera;

wherein the second camera pod comprises a second one of the plurality of cameras for viewing one of the respective targets disposed at a respective wheel of the vehicle, and a calibration camera disposed in a known relationship to the second camera for capturing image data of the calibration target as the wheels and targets of the vehicle are rotated the number of degrees of rotation, wherein the image data of the calibration target is usable to calculate a plurality of poses of the calibration target;

wherein detecting instability of one or more of the first and second cameras comprises comparing an initial calibration target pose, acquired at the beginning of the rotation of the wheels, with each succeeding one of the calibration target poses, to determine whether one of the succeeding calibration target poses deviates from the initial calibration target pose more than a threshold amount.

24. The system of claim 23, wherein the user is alerted that the first and second cameras are unstable when one of the succeeding calibration target poses deviates from the initial calibration target pose more than the threshold amount.

25. The system of claim 7, comprising a reference target fixedly mounted within a field of view of one of the plurality of cameras;

wherein the one of the plurality of cameras is for capturing image data of the reference target as the wheels and targets of the vehicle are rotated the number of degrees of rotation, wherein the image data of the reference target is used to calculate a plurality of poses of the reference target;

wherein detecting instability of the one of the plurality of cameras comprises comparing an initial reference target pose, acquired at the beginning of the rotation of the wheels, with each succeeding one of the reference target poses, to determine whether one of the succeeding reference target poses deviates from the initial reference target pose more than a threshold amount.

26. The system of claim 25, wherein the user is alerted that the one of the plurality of cameras is unstable when one of the succeeding reference target poses deviates from the initial reference target pose more than the threshold amount.

27. The system of claim 5, wherein detecting stress in the suspension of the vehicle comprises computing at least one wheel alignment parameter for each of the vehicle wheels having a target for each of the captured target poses of that wheel, calculating changes in the wheel alignment parameter for successive captured target poses, and determining whether the changes are below a predetermined change threshold.

28. The system of claim 26, wherein when one of the changes is above the change threshold, a suspension stress exists, and the step of attempting to compensate for the suspension stress comprises comparing the change to an empirical reference value or using the change in an empirical formula to predict a value for the alignment parameter when the suspension is settled, and using the predicted value in the subsequent alignment step.

29. The system of claim 7, wherein the plurality of cameras are for viewing a respective target on each of the rear wheels of the vehicle;

and wherein detecting vehicle thrust angle changes comprises computing a toe angle for each of the vehicle rear wheels for each of the captured target poses of that wheel, computing an initial reference thrust angle based on the toe angles derived from the first captured target poses acquired at the beginning of the rotation of the wheels or at the beginning of an alignment adjustment, computing successive thrust angles based on the toe angles derived from the remainder of the captured target poses of the rear wheels, comparing the reference thrust angle with each successive thrust angle to determine whether one of the successive thrust angles deviates from the reference thrust angle more than a threshold amount.

30. The system of claim 29, wherein the user is alerted that the vehicle thrust angle has changed excessively when one of the succeeding thrust angles deviates from the reference thrust angle more than the threshold amount.

31. The system of claim 1, wherein the processor is for computing a wheel axis for each of the vehicle wheels having a target, based at least in part on the captured image data of each respective target.

32. The system of claim 31, wherein computing the wheel axis comprises comparing pairs of the plurality of poses of the target of the wheel, wherein an angle of rotation between the poses of each pair of poses exceeds a predetermined angle.

33. The system of claim 1, wherein the plurality of cameras are for viewing a respective target disposed on each of the steerable front wheels of the vehicle and capturing image data of the target as the wheel and target are continuously turned a number of degrees to the left and to the right of center without a pause, wherein the image data is used to calculate a minimum plurality of poses of the target;

wherein the minimum number of poses of the target comprises at least one pose for every five degrees of turning captured by each camera as the wheel and target are continuously turned the number of degrees of rotation without a pause; and

wherein the processor is for computing a caster angle for each of the front wheels based at least in part on the image data captured as the wheel and target are continuously turned.

34. The system of claim 33, wherein the plurality of cameras are for viewing a respective target on each of the rear wheels of the vehicle;

and

wherein the data processor is for detecting excessive vehicle thrust angle changes by computing a toe angle for each of the vehicle rear wheels for each of the captured target poses of that wheel, computing an initial reference thrust angle based on the toe angles derived from the first captured target poses acquired at the beginning of the turning of the front wheels, computing successive thrust angles based on the toe angles derived from the remainder of the captured target poses of the rear wheels, comparing the reference thrust angle with each successive thrust angle to determine whether one of the successive thrust angles deviates from the reference thrust angle more than a threshold amount.

35. The system of claim 34, wherein the user is alerted that the vehicle thrust angle has changed excessively when one of the succeeding thrust angles deviates from the reference thrust angle more than the threshold amount.