Error field construction method for mobile tracking measurement device and application

Abstract

The present application belongs to the technical field of large-scale visual measurement, and discloses a method for constructing an error field of a mobile tracking measurement device and application, comprising the following steps: (1) constructing a visual measurement field, wherein the visual measurement field is provided with a global measurement positioning system, a static target array and an automatic guided vehicle, the automatic guided vehicle is provided with a tracking measurement device and a dynamic target array; (2) identifying a differential motion vector under the coordinate system of the global measurement positioning system; wherein a linear least square model is constructed based on the invariable relative pose of the tracking measurement device and the automatic guided vehicle in space to obtain the differential motion vector; (3) solving the differential motion transformation and then calculating an error weight factor with the object space pose as the independent variable to construct the error field. The present application organically integrates the micron-level precision into the construction of the error field, and breaks through the technical bottleneck that the positioning precision of the global measurement positioning system is difficult to improve due to the size restriction of the measurement space.

Description

Technical Field

[0001] This invention belongs to the field of large-scale visual measurement technology, and more specifically, relates to a method and application for constructing an error field for a mobile tracking measurement device. Background Technology

[0002] Using industrial robots as the manufacturing equipment actuators and integrating intelligent vision tracking and measurement devices provides a new approach for the efficient and high-precision milling of large, thin-walled, and weakly rigid aerospace components such as horizontal stabilizer skin and mid-fuselage frames. Due to the large size of these parts and the numerous measurement-machining system units, the tracking and measurement equipment needs to perform mobile measurements to ensure the continuity of the robot's end-effector pose tracking. The accuracy of the pose calculation by the tracking and measurement equipment directly affects the precise control of the robot's end-effector pose during machining, ultimately impacting the contour and surface accuracy of the parts to be machined.

[0003] Therefore, many scholars have conducted in-depth theoretical and methodological research on the accurate positioning of tracking measurement equipment in large-scale visual measurement fields. Compared with the serious error accumulation and low measurement efficiency of multiple station transfer measurements, continuous measurement based on positioning base stations has become the mainstream trend of current research. However, it still cannot solve the problem that positioning accuracy is limited by the large size of the measurement space. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and application for constructing an error field for mobile tracking and measurement devices. This method organically integrates the micrometer-level accuracy of the tracking and measurement device into the construction of the error field, overcoming the technical bottleneck of global measurement and positioning systems being constrained by the size of the measurement space, which hinders the improvement of positioning accuracy. It is suitable for real-time and accurate compensation of the pose of mobile tracking and measurement devices in large-scale visual measurement fields. Furthermore, this invention transforms the identification of differential motion vectors into a pseudo-inverse problem based on a linear least squares model, avoiding the problem of decreased computational efficiency caused by multiple iterations of nonlinear models.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for constructing an error field for a mobile tracking and measurement device is provided, the method mainly comprising the following steps:

[0006] (1) Construct a visual measurement field, wherein the visual measurement field is equipped with a global measurement and positioning system, a static target array and an automatic guided vehicle, wherein the automatic guided vehicle is equipped with a tracking measurement device and a dynamic target array;

[0007] (2) The relative pose between the dynamic target array and the static target array is changed multiple times, and the differential motion vector in the coordinate system of the global measurement and positioning system is identified by the tracking measurement device. During the identification process, a linear least squares model is constructed based on the invariance of the relative pose between the tracking measurement device and the automatic guided vehicle in space, and then the differential motion vector in the coordinate system of the global measurement and positioning system is identified by solving the pseudo-inverse.

[0008] (3) Solve the differential motion transformation based on the identified differential motion vector, and then calculate the error weight factor with the spatial pose of the object as the independent variable, and construct the error field based on the obtained error weight factor.

[0009] Furthermore, the measurement accuracy of the tracking and measuring device is at the decimeter level.

[0010] Furthermore, the identification of the differential motion vector includes the following steps:

[0011] (21) Change the relative pose relationship between the dynamic target array and the static target array, and use the tracking measurement device and the global measurement and positioning system to obtain m sets of poses respectively. and i = 1, 2, 3, ..., m;

[0012] (22) The following equation is established with the constraint that the relative pose of the tracking measurement device and the automated guided vehicle in space remains unchanged:

[0013]

[0014] Where vec(·) denotes the vectorization operator, and the vector Δ AGV p LT sum matrix Let position error and attitude error represent the errors respectively, and satisfy the following equation:

[0015]

[0016] In the formula, This represents the antisymmetric matrix corresponding to the translation vector p. Representing rotation matrices respectively The vectors in columns 1, 2, and 3, where J represents a 12x6 Jacobian matrix;

[0017] (23) By shifting terms left and right in the equation, formula (1) can be transformed into:

[0018]

[0019] (24) Establish a linear least squares model AX = b according to formula (3), and identify the differential motion vector in the coordinate system of the global measurement and positioning system by solving the pseudo-inverse. wMPSD = [dδ] T Where A is a known matrix, X and b are column vectors, d is the position differential motion vector, and δ is the attitude differential motion vector.

[0020] Furthermore, the differential motion transformation includes position differential motion transformation Δt and attitude differential motion transformation ΔR, and satisfies the following equation:

[0021]

[0022] Furthermore, the error weighting factors include the position error weighting factor. and attitude error weighting factor And it satisfies the following equation:

[0023]

[0024] In the formula, N represents the power factor.

[0025] Furthermore, the tracking measurement equipment coordinate system {LT}, the global measurement and positioning system coordinate system {wMPS}, the static target array coordinate system {ERS}, the dynamic target array, and the automated guided vehicle coordinate system {AGV} satisfy the following formula:

[0026]

[0027] in, The pose transformation matrix of coordinate system {B} relative to coordinate system {A} is represented by the orthogonal rotation matrix. Translation vector A p B It constitutes, and satisfies the following equation:

[0028]

[0029] Furthermore, the size range of the visual measurement field is ≥5m×5m, and the pose error characteristics of any object in the measurement field are described by the error weight factor as the evaluation index.

[0030] Furthermore, the dynamic target array consists of receivers for receiving signals emitted by the global measurement and positioning system; the static target array consists of target balls or receivers for receiving signals emitted by the tracking measurement device or the global measurement and positioning system, respectively.

[0031] Furthermore, the tracking and measurement device is a laser tracker, and the global measurement and positioning system includes four global positioning base stations.

[0032] According to another aspect of the present invention, an application is provided of an error field for a mobile tracking measurement device constructed using the error field construction method for a mobile tracking measurement device as described above, the error field being used for real-time compensation of the pose of the mobile tracking measurement device.

[0033] In summary, compared with the prior art, the error field construction method and application for mobile tracking measurement equipment provided by the present invention mainly have the following advantages:

[0034] Beneficial effects:

[0035] 1. The construction method organically integrates the millimeter-level accuracy of high-precision tracking and measuring equipment into the construction of the error field, thereby realizing the real-time precise positioning of mobile tracking and measuring equipment.

[0036] 2. This invention transforms the identification of differential motion vectors into a pseudo-inverse problem based on a linear least squares model, thus avoiding the problem of decreased computational efficiency caused by multiple iterations of the nonlinear model.

[0037] 3. This invention can be extended to the construction of error fields in other different measurement spatial environments, and can compensate for the pose of objects in a large-scale visual measurement field in real time according to measurement needs.

[0038] 4. The error field constructed using the construction method of this invention is suitable for real-time and accurate compensation of the pose of mobile tracking measurement equipment in a large-scale visual measurement field. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the error field construction method for a mobile tracking and measurement device provided by the present invention.

[0040] Figure 2 This is a schematic diagram of the multi-unit large-scale visual measurement field involved in the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0042] Please see Figure 1 and Figure 2 The present invention provides a method for constructing an error field for a mobile tracking and measurement device, the method mainly comprising the following steps:

[0043] Step 1: Establish a large-scale visual measurement field with multiple units based on spatial size information.

[0044] like Figure 2 As shown, the size range of the multi-unit large-scale visual measurement field is ≥5m×5m, and the pose error characteristics of any object within the measurement field are described using an error weighting factor as an evaluation index. In this embodiment, the size of the measurement field is 7.5m×7.5m, and it is equipped with a high-precision tracking and measurement device LT (i.e., a laser tracker with a tracking and measurement accuracy of 10μm+5μm / m), a global measurement and positioning system wMPS (i.e., four global positioning base stations with a measurement accuracy of 200μm+10μm / m), a static target array ERS composed of 16 high-precision target spheres, a dynamic target array composed of four receivers, and an automated guided vehicle (AGV). The high-precision tracking and measurement device is a measurement device with a measurement accuracy at the micrometer level.

[0045] The high-precision tracking and measurement device and the dynamic target array are both mounted on an automated guided vehicle (AGV) and can move freely within a large-scale visual measurement field. The global measurement and positioning system and the static target array are fixedly installed within the large-scale visual measurement field. Their relative pose relationships can be pre-calibrated by replacing the four target spheres in the static target array with receivers. The relative poses of the high-precision tracking and measurement device, the global measurement and positioning system, the static target array, the dynamic target array, and the AGV satisfy the following equation:

[0046]

[0047] That is, the relative poses between multiple units such as high-precision tracking and measurement equipment-coordinate system {LT}, global measurement and positioning system-coordinate system {wMPS}, static target array-coordinate system {ERS}, dynamic target array, and automatic guided vehicle-coordinate system {AGV} satisfy formula (1).

[0048] in, The pose transformation matrix of coordinate system {B} relative to coordinate system {A} is represented by the orthogonal rotation matrix. Translation vector A p B It constitutes, and satisfies the following equation:

[0049]

[0050] Step 2: Change the relative pose between the static and dynamic targets in the measurement field multiple times to obtain multiple sets of pose data.

[0051] Specifically, the automated guided vehicle is controlled to move within a large visual measurement field, changing the relative pose relationship between the dynamic target array and the static target array. Simultaneously, m sets of poses are acquired using high-precision tracking and measurement equipment and a global measurement and positioning system. and i = 1, 2, 3, ..., m.

[0052] Step 3: Identify the differential motion vector in the coordinate system of the global measurement and positioning system.

[0053] Specifically, taking the invariance of the relative pose of the high-precision tracking and measuring equipment and the automated guided vehicle in space as a constraint, the following equation is established:

[0054]

[0055] Where vec(·) denotes the vectorization operator, and the vector Δ AGV p LT sum matrix Let position error and attitude error represent the errors respectively, and satisfy the following equation:

[0056]

[0057] In the formula, This represents the antisymmetric matrix corresponding to the translation vector p. Representing rotation matrices respectively The vectors in columns 1, 2, and 3, and J represents a Jacobian matrix of 12 rows and 6 columns.

[0058] By shifting terms left and right in the equation, formula (3) can be transformed into:

[0059]

[0060] Based on formula (5), a linear least squares model AX = b is established, and the differential motion vector in the coordinate system of the global measurement and positioning system is quickly identified by solving the pseudo-inverse. wMPS D = [dδ] T Where A is a known matrix, X and b are column vectors, d is the position differential motion vector, and δ is the attitude differential motion vector.

[0061] Step 4: Solve for the differential motion transformation based on the identified differential motion vector.

[0062] Specifically, the pose differential motion transformation includes position differential motion transformation Δt and attitude differential motion transformation ΔR, and satisfies the following equation:

[0063]

[0064] Step 5: Construct an error field with pose error weighting factors as the evaluation index.

[0065] Specifically, the error weighting factors include the position error weighting factor. and attitude error weighting factor And it satisfies the following equation:

[0066]

[0067] In the formula, N represents the power factor, and here N = 2 is selected based on the size of the visual measurement field.

[0068] Thus, the error field is constructed using the error weight factor in formula (7) as the evaluation index, thereby realizing real-time and accurate compensation of the pose of high-precision tracking measurement equipment in a large-scale visual measurement field.

[0069] The present invention also provides an application of an error field for a mobile tracking measurement device constructed using the error field construction method for a mobile tracking measurement device as described above, wherein the error field is used for real-time compensation of the pose of the mobile tracking measurement device.

[0070] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing an error field for a mobile tracking and measurement device, characterized in that, The method includes the following steps: (1) Construct a visual measurement field, wherein the visual measurement field is equipped with a global measurement and positioning system, a static target array and an automatic guided vehicle, wherein the automatic guided vehicle is equipped with a tracking measurement device and a dynamic target array; (2) The relative pose between the dynamic target array and the static target array is changed multiple times, and the differential motion vector in the coordinate system of the global measurement and positioning system is identified by the tracking measurement device. During the identification process, a linear least squares model is constructed based on the invariance of the relative pose between the tracking measurement device and the automatic guided vehicle in space, and then the differential motion vector in the coordinate system of the global measurement and positioning system is identified by solving the pseudo-inverse. (3) Solve the differential motion transformation based on the identified differential motion vector, and then calculate the error weight factor with the spatial pose of the object as the independent variable, and construct the error field based on the obtained error weight factor.

2. The error field construction method for a mobile tracking measurement device as described in claim 1, characterized in that: The measurement accuracy of the tracking and measuring device is at the micrometer level.

3. The error field construction method for a mobile tracking measurement device as described in claim 1, characterized in that: The identification of differential motion vectors includes the following steps: (21) Change the relative pose relationship between the dynamic target array and the static target array, and use the tracking measurement device and the global measurement and positioning system to obtain m sets of poses respectively. and (22) The following equation is established with the constraint that the relative pose of the tracking measurement device and the automated guided vehicle in space remains unchanged: Where vec(·) denotes the vectorization operator, and the vector Δ AGV p LT sum matrix Let position error and attitude error represent the errors respectively, and satisfy the following equation: In the formula, This represents the antisymmetric matrix corresponding to the translation vector p. Representing rotation matrices respectively The vectors in columns 1, 2, and 3, where J represents a 12x6 Jacobian matrix; (23) By shifting terms left and right in the equation, formula (1) can be transformed into: (24) Establish a linear least squares model AX = b according to formula (3), and identify the differential motion vector in the coordinate system of the global measurement and positioning system by solving the pseudo-inverse. wMPS D = [dδ] T Where A is a known matrix, X and b are column vectors, d is the position differential motion vector, and δ is the attitude differential motion vector.

4. The error field construction method for a mobile tracking measurement device as described in claim 3, characterized in that: The differential motion transformation includes position differential motion transformation Δt and attitude differential motion transformation ΔR, and satisfies the following equation:

5. The error field construction method for a mobile tracking measurement device as described in claim 4, characterized in that: Error weighting factors include position error weighting factors. and attitude error weighting factor And it satisfies the following equation: In the formula, N represents the power factor.

6. The method for constructing an error field for a mobile tracking measurement device as described in any one of claims 1-5, characterized in that: The coordinate system {LT} of the tracking and measuring equipment, the coordinate system {wMPS} of the global measurement and positioning system, the coordinate system {ERS} of the static target array, the coordinate system {dynamic target array}, and the coordinate system {AGV} of the automated guided vehicle satisfy the following formula: in, The pose transformation matrix of coordinate system {B} relative to coordinate system {A} is represented by the orthogonal rotation matrix. Translation vector A p B It constitutes, and satisfies the following equation:

7. The method for constructing an error field for a mobile tracking measurement device as described in any one of claims 1-5, characterized in that: The visual measurement field has a size range of ≥5m×5m, and the pose error characteristics of any object in the measurement field are described by the error weight factor as the evaluation index.

8. The method for constructing an error field for a mobile tracking measurement device as described in any one of claims 1-5, characterized in that: The dynamic target array consists of receivers, which are used to receive signals from the global measurement and positioning system; the static target array consists of target balls or receivers, which are used to receive signals from the tracking measurement device or the global measurement and positioning system, respectively.

9. The error field construction method for a mobile tracking measurement device as described in claim 8, characterized in that: The tracking and measurement device is a laser tracker, and the global measurement and positioning system includes four global positioning base stations.

10. An application of an error measurement field for a mobile tracking measurement device constructed using the error field construction method for a mobile tracking measurement device according to any one of claims 1-9, characterized in that: The error field is used for real-time compensation of the pose of the mobile tracking measurement device.

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