Annular area reinforcing mesh hole positioning test device and method based on 3D vision

Abstract

The invention belongs to the technical field of constructional engineering equipment, and particularly relates to an annular area reinforcing mesh hole positioning test device and method based on 3D vision. The method mainly comprises the following steps: driving a 3D sensor to scan a reinforcing mesh through a rotating mechanism to obtain a reinforcing mesh point cloud image; binarization processing is performed on the reinforcing mesh image, so that subsequent image analysis is facilitated; performing skeletization processing on the reinforcing mesh image by adopting a 3D refining algorithm, and extracting a reinforcing mesh skeleton; through a neighborhood statistical algorithm, searching a reinforcing steel bar intersection point set; a least square sphere fitting algorithm is adopted to fit and position reinforcing steel bar binding points; and a central hole of the reinforcing mesh is positioned through a Valley parallelogram method. According to the method, manpower consumption is reduced, the construction efficiency is improved, and automation and intelligentization of reinforcing mesh domain concrete construction are facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction engineering equipment, and in particular relates to a 3D vision-based annular area steel mesh hole positioning test device and method. Background Art

[0002] In construction projects, properly positioning steel mesh not only ensures that the rebar is positioned as designed but also optimizes subsequent pouring and vibration. Proper positioning prevents mesh shifting during pouring, ensuring that concrete is evenly distributed around the rebar and avoiding honeycombing or voids. This also facilitates precise vibration and adequate degassing during the vibration process, resulting in dense concrete and ensuring the strength and durability of the structure.

[0003] The positioning of steel mesh is crucial to construction quality and structural safety. Traditional methods for positioning steel mesh rely heavily on manual visual positioning and marking, which is not only inefficient but also prone to inaccuracies due to human error. Accurately and efficiently locating the center hole of the steel mesh is a pressing issue, especially in complex construction environments. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a 3D vision-based annular area steel mesh hole positioning test device and method, which realizes the precise positioning of the center hole of the steel mesh and subsequent construction operations; the present invention can accurately identify the skeleton structure and hole position of the steel mesh, with high positioning accuracy while reducing manual intervention and improving construction efficiency; the present invention is suitable for annular area steel meshes of different specifications and shapes; the present invention significantly improves the recognition stability of complex spatial structures, and overcomes the industry problems of low manual detection efficiency and large two-dimensional visual positioning deviation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for locating holes in annular steel mesh based on 3D vision, the method comprising the following steps:

[0007] Step 1: Install the rotating mechanism and fix the 3D camera under the rotating arm of the rotating mechanism;

[0008] Step 2: Start the 3D camera and servo motor, and the rotating mechanism cooperates with the 3D sensor to scan the steel mesh and collect the 3D point cloud data and pixel value distribution information of the steel mesh;

[0009] Step 3: The collected image data is transmitted to the processing unit for binarization and skeletonization to extract the skeleton structure of the steel mesh;

[0010] Step 4: Identify the intersection points of the steel mesh using the neighborhood statistics algorithm, and use the least squares method to fit the intersection point set to determine the coordinates of the binding points;

[0011] Step 5: Connect adjacent lashing points and calculate the geometric center of the quadrilateral using the Varigno parallelogram method to obtain the center hole position of the steel mesh in the annular area;

[0012] Step 6: Feedback the center hole position information to the construction equipment to complete the positioning of the center hole of the steel mesh.

[0013] Furthermore, the steel mesh image captured by the 3D camera is binarized to facilitate subsequent image analysis; the steel mesh image is skeletonized using a 3D thinning algorithm, and the skeleton structure of the steel mesh is extracted by constructing an octree data structure and an iterative thinning process; any point in the skeletonized image is used as the center of a sphere, and a search is performed within a certain radius, and a set of steel bar intersection points is determined by a neighborhood statistics algorithm; the least squares method is used to fit and locate the binding points, and a sphere with each set of point sets as the edge is fitted using a least squares circle fitting algorithm, and the center of the fitted sphere is determined as the binding point; after the binding points are mapped to a two-dimensional plane, the center hole position of the steel mesh in the annular area is located using the Varignon parallelogram method.

[0014] Furthermore, the binarization process is to set the steel bar value to 1 and the background value to 0.

[0015] Furthermore, the 3D thinning algorithm includes the following steps:

[0016] Initialization: load the 3D binary image into memory;

[0017] Build an octree: Use a 3×3×3 octree data structure to check the 26 neighborhoods of each pixel;

[0018] Iterative refinement: Identify and remove boundary pixels, generate a candidate list for removal, and sequentially recheck the pixels in the candidate list to maintain image connectivity;

[0019] Termination condition: When the image no longer changes, stop the iteration;

[0020] Post-processing: Eliminate noise and stray points to obtain a smoother and more centered skeleton.

[0021] Furthermore, the neighborhood statistics calculation method is: taking a point in the skeletonized image as the center of a sphere, searching within a certain radius, and counting the number of pixel points with a pixel value of 1 found within the range. If the number of points counted is greater than a certain threshold, it is determined to be an intersection point.

[0022] Furthermore, the least squares fitting method for locating the lashing point is as follows: a least squares sphere fitting algorithm is used to fit a sphere with each set of intersection points as the edge, and finally the center of the fitted sphere is determined as the lashing point; wherein the standard equation of the sphere is:

[0023] (xa) 2 +(yb) 2 +(zc) 2 =r 2 ;

[0024] Where (x, y, z) is a point on the edge of the sphere, (a, b, c) are the coordinates of the center of the sphere, and r is the radius of the sphere. Expanding the equation and shifting the terms yields:

[0025] x 2 +y 2 +z 2 -2ax-2by-2cz+(a 2 +b 2 +c 2 -r 2 )=0

[0026] Define a new variable d=a 2 +b 2 +c 2 -r 2 , then the above formula can be simplified to:

[0027] -2ax-2by-2cz+d=-(x 2 +y 2 +z 2 )

[0028] This is further transformed into a standard linear form:

[0029] Ax+By+Cz+D=E

[0030] Where A=-2a,B=-2b,C=-2c,D=d,E=-(x 2 +y 2 +z 2 );

[0031] For a given n points (x i ,y i ,z i ), we can write n such equations to form a matrix form:

[0032] M·p=E;

[0033] in:

[0034] M is an n×4 matrix of the form:

[0035] p=[A,B,C,D] T is the parameter vector to be determined;

[0036] E is an n×1 vector of the form:

[0037] E=[-(x1 2 +y1 2 +z1 2 ),-(x2 2 +y2 2 +z2 2 ),...,-(x n 2 +y n 2 +z n 2 )] T

[0038] Solve the equation M·p=E using the least squares method and get p=[A,B,V,D] T ;

[0039] The matrix can be used to solve the formula:

[0040] p=(M T M) -1 M T E

[0041] From A = -2a, B = -2b, C = -2c, we can get the coordinates of the sphere center, i.e. the coordinates of the binding point:

[0042] a=-(A) / 2, b=-(B) / 2, c=-(C) / 2.

[0043] Furthermore, the Varignon parallelogram method for locating the holes in the steel mesh is as follows: the midpoints of adjacent binding points are calculated, and the intersection of the midpoints of the connecting lines is the center hole position of the steel mesh in the annular area.

[0044] The present invention also claims protection for a test device for the method of locating holes in an annular area steel mesh based on the above-mentioned 3D vision, including a rotating mechanism and a 3D sensor, the rotating mechanism including a rotating base, a small gear, a large gear, a bearing, a rotating rod, and a servo motor; the rotating base is fixed on the support platform and is engaged with the rotating rod through a bearing; the servo motor is fixed on the rotating rod, and the servo motor drives the small gear to cooperate with the large gear on the rotating base to drive the rotating rod to move circumferentially.

[0045] Furthermore: a second servo motor is installed at the end of the rotating rod, and the 3D sensor is fixed on the moving block of the ball screw. The second servo motor drives the moving block and the 3D sensor to move radially along the direction of the ball screw.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The present invention maps the binding points onto a two-dimensional plane. The midpoints of adjacent binding points are taken, and straight lines are drawn through their respective midpoints. The intersection of these straight lines is the center hole position of the annular reinforcement mesh. The center hole position information is fed back to the construction equipment via a control unit, enabling precise positioning of the center hole of the reinforcement mesh and subsequent construction operations.

[0048] (2) The present invention can accurately identify the skeleton structure and hole position of the steel mesh through the high-precision scanning and image processing algorithm of the 3D camera, and has high positioning accuracy; combined with the rotation mechanism and automatic control technology, it can realize the comprehensive scanning and positioning of the annular area steel mesh, reduce manual intervention, and improve construction efficiency; it is suitable for annular area steel meshes of different specifications and shapes, and has broad engineering application prospects.

[0049] (3) The present invention achieves high-precision, adaptive hole positioning through three-dimensional refinement algorithm, least squares ball fitting and Variignon parallelogram geometry calculation, and uses skeletonization processing and neighborhood statistics to effectively filter out noise and accurately extract steel bar intersections. It combines dynamic scanning of the rotating mechanism to generate dense point clouds, significantly improving the recognition stability of complex spatial structures. At the same time, the multi-degree-of-freedom scanning mechanism driven by dual servo motors breaks through the traditional viewing angle limitation, and the gear set and ball screw work together to achieve omnidirectional coverage and stable movement, ensuring the integrity of data collection. The two work together to build a closed-loop feedback system, and through the three-dimensional spatial coupling optimization algorithm efficiency and fault tolerance, it overcomes the industry problems of low manual inspection efficiency and large two-dimensional visual positioning deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a three-dimensional schematic diagram of the overall structure of the test device of the present invention;

[0051] Figure 2 A top view of the test device of the present invention;

[0052] Figure 3 A three-dimensional schematic diagram of the disassembled rotating mechanism of the present invention;

[0053] Figure 4 Flowchart of the steel mesh hole positioning method of the present invention.

[0054] The reference numerals are as follows:

[0055] 1. Rotating mechanism; 2. 3D sensor; 1-1. Rotating base; 1-2. Small gear; 1-3. Large gear; 1-4. Bearing; 1-5. Rotating rod; 1-6. Servo motor; 1-8. Servo motor 2. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the embodiments. Of course, the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0057] Although the steps in the present invention are arranged with numbers, they are not intended to limit the order of the steps. Unless the order of the steps is clearly stated or the execution of a step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" used herein refers to and covers any and all possible combinations of one or more of the associated listed items.

[0058] Example

[0059] like Figures 1 to 4 As shown, when using, take the following steps:

[0060] 1) Install a rotating mechanism in the construction area and fix the 3D camera under the rotating rod of the rotating mechanism.

[0061] 2) Start the 3D camera and servo motor, and the rotating mechanism cooperates with the 3D sensor to scan the steel mesh and collect the three-dimensional point cloud data and pixel value distribution information of the steel mesh.

[0062] 3) The collected image data is transmitted to the processing unit for binarization and skeletonization to extract the skeleton structure of the steel mesh.

[0063] 4) The intersection points of the steel mesh are identified through the neighborhood statistical algorithm, and the least squares method is used to fit the intersection point set to determine the coordinates of the binding points.

[0064] 5) Connect adjacent binding points and calculate the geometric center of the quadrilateral using the Varigno parallelogram method to obtain the center hole position of the steel mesh in the annular area.

[0065] 6) Feedback the center hole position information to the construction equipment to complete the positioning of the center hole of the steel mesh.

[0066] The 3D vision-based annular area steel mesh hole positioning method of the present invention can achieve rapid and accurate positioning of the center hole of the annular area steel mesh, improve construction efficiency and quality, reduce manpower consumption, and has important engineering application value.

[0067] The present invention achieves high-precision, adaptive hole positioning through a three-dimensional refinement algorithm, least-squares spherical fitting, and Variñon parallelogram geometry calculation. Skeletonization processing and neighborhood statistics are used to effectively filter out noise and accurately extract steel bar intersections. Combined with dynamic scanning of a rotating mechanism, a dense point cloud is generated, significantly improving the recognition stability of complex spatial structures. This will be described in detail later.

[0068] The 3D vision-based ring-shaped steel mesh hole location test device includes a rotating mechanism 1, a 3D sensor 2, and a steel mesh hole location method. The device is characterized in that the 3D sensor 2 is installed below the rotating mechanism 1, and the steel mesh hole location method locates steel mesh holes based on a three-dimensional steel mesh model.

[0069] Furthermore, the rotating mechanism 1 includes a rotating base 1-1, a small gear 1-2, a large gear 1-3, a bearing 1-4, a rotating rod 1-5, and a servo motor 1-6. The rotating base 1-1 is fixed to the support platform and engages with the rotating rod 1-5 via the bearing 1-4. The servo motor 1-6 is fixed to the rotating rod 1-5, which drives the small gear 1-2 to rotate in conjunction with the large gear 1-3 on the rotating base 1-1, thereby driving the rotating rod 1-5 in a circumferential motion.

[0070] Furthermore: the 3D sensor 2 is installed below the rotating rods 1-5, and the data interface and lens meet the current mainstream models, ensuring that the monitoring range effectively covers the steel mesh.

[0071] Furthermore: a servo motor 2 1-8 is installed at the end of the rotating rod 1-5, and the 3D sensor 2 is fixed on the moving block of the ball screw 2-7. The servo motor 2 1-8 drives the moving block and the 3D sensor 2 to move radially along the direction of the ball screw 2-7.

[0072] The present invention breaks through the traditional viewing angle limitation through the multi-degree-of-freedom scanning mechanism driven by dual servo motors, and realizes omnidirectional coverage and stable movement through the coordination of gear sets and ball screws to ensure the integrity of data collection. The two work together to build a closed-loop feedback system, and optimize the efficiency and fault tolerance of the algorithm through three-dimensional spatial coupling, thus overcoming the industry problems of low manual inspection efficiency and large two-dimensional visual positioning deviation.

[0073] Furthermore: the steel mesh hole locating method includes binarizing the steel mesh image captured by the 3D camera to facilitate subsequent image analysis; skeletonizing the steel mesh image using a 3D refinement algorithm, and extracting the skeleton structure of the steel mesh by constructing an octree data structure and an iterative refinement process; using any point of the skeletonized image as the center of a sphere, searching within a certain radius, and determining the set of steel bar intersection points through a neighborhood statistics algorithm; using the least squares method to fit and locate the binding points, fitting a sphere with each set of point sets as the edge through a least squares circle fitting algorithm, and determining the center of the fitted sphere as the binding point; after mapping the binding points to a two-dimensional plane, locating the center hole position of the steel mesh in the annular area through the Varignon parallelogram method.

[0074] Furthermore, the binarization process is to set the steel bar value to 1 and the background value to 0.

[0075] Furthermore, the 3D thinning algorithm includes the following steps:

[0076] Initialization: load the 3D binary image into memory;

[0077] Build an octree: Use a 3×3×3 octree data structure to check the 26 neighborhoods of each pixel;

[0078] Iterative refinement: Identify and remove boundary pixels, generate a candidate list for removal, and sequentially recheck the pixels in the candidate list to maintain image connectivity;

[0079] Termination condition: When the image no longer changes, stop the iteration;

[0080] Post-processing: Eliminate noise and stray points to obtain a smoother and more centered skeleton.

[0081] Furthermore, the neighborhood statistics calculation method is:

[0082] With a point in the skeletonized image as the center of the sphere, a search is performed within a certain radius, and the number of pixel points with a pixel value of 1 found within the range is counted. If the number of points counted is greater than a certain threshold, it is determined to be an intersection point.

[0083] Furthermore, the least squares fitting method for positioning the lashing points is as follows: a least squares sphere fitting algorithm is used to fit a sphere with each set of intersection points as the edge, and finally the center of the fitted sphere is determined as the lashing point.

[0084] The standard equation of a sphere is:

[0085] (xa) 2 +(yb) 2 +(zc) 2 =r 2

[0086] Where (x, y, z) is a point on the edge of the sphere, (a, b, c) are the coordinates of the sphere's center, and r is the radius of the sphere. Expanding the equation and shifting the terms yields:

[0087] x 2 +y 2 +z 2 -2ax-2by-2cz+(a 2 +b 2 +c 2 -r 2 )=0

[0088] Define a new variable d=a 2 +b 2 +c 2 -r 2 , then the above formula can be simplified to:

[0089] -2ax-2by-2cz+d=-(x 2 +y 2 +z 2 )

[0090] This is further transformed into a standard linear form:

[0091] Ax+By+Cz+D=E

[0092] Where A=-2a,B=-2b,C=-2c,D=d,E=-(x 2 +y 2 +z 2 ).

[0093] For a given n points (x i ,y i ,z i ), we can write n such equations to form a matrix form:

[0094] M·p=E;

[0095] in:

[0096] M is an n×4 matrix of the form:

[0097] p=[A,B,C,D] T is the parameter vector to be found.

[0098] E is an n×1 vector of the form:

[0099] E=[-(x1 2 +y1 2 +z1 2 ),-(x2 2 +y2 2 +z2 2 ),...,-(x n 2 +y n 2 +z n 2 )] T

[0100] Solve the equation M·p=E using the least squares method and get p=[A,B,V,D] T .

[0101] The matrix can be used to solve the formula:

[0102] p=(M T M) -1 M T E

[0103] From A = -2a, B = -2b, C = -2c, we can get the coordinates of the sphere center, i.e. the coordinates of the binding point:

[0104] a=-(A) / 2, b=-(B) / 2, c=-(C) / 2.

[0105] Furthermore, the Varigno parallelogram method for locating the holes in the steel mesh is as follows: the midpoints of adjacent binding points are calculated, and the intersection of the midpoints of the connecting lines is the center hole position of the steel mesh in the annular area.

[0106] The present invention maps the binding points onto a two-dimensional plane, takes the midpoints of adjacent binding points, and draws straight lines passing through their respective midpoints. The intersection of these lines represents the center hole position of the annular reinforcement mesh. This center hole position information is fed back to the construction equipment via a control unit, enabling precise positioning of the mesh's center hole and subsequent construction operations. High-precision scanning and image processing algorithms using a 3D camera accurately identify the mesh's skeleton structure and hole locations, achieving high positioning accuracy. Combined with a rotary mechanism and automated control technology, this method allows for comprehensive scanning and positioning of the annular reinforcement mesh, reducing manual intervention and improving construction efficiency. The invention is applicable to annular reinforcement meshes of varying specifications and shapes, and has broad engineering application prospects.

[0107] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this field, several improvements and changes can be made without departing from the creative concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A method for locating holes in annular steel mesh based on 3D vision, characterized in that: The method comprises the following steps: Step 1: Install the rotating mechanism and fix the 3D camera under the rotating arm of the rotating mechanism; Step 2: Start the 3D camera and servo motor, and the rotating mechanism cooperates with the 3D sensor to scan the steel mesh and collect the 3D point cloud data and pixel value distribution information of the steel mesh; Step 3: The collected image data is transmitted to the processing unit for binarization and skeletonization to extract the skeleton structure of the steel mesh; Step 4: Identify the intersection points of the steel mesh using the neighborhood statistics algorithm, and use the least squares method to fit the intersection point set to determine the coordinates of the binding points; Step 5: Connect adjacent lashing points and calculate the geometric center of the quadrilateral using the Varigno parallelogram method to obtain the center hole position of the steel mesh in the annular area; Step 6: Feedback the center hole position information to the construction equipment to complete the positioning of the center hole of the steel mesh.

2. The method for locating holes in annular steel mesh based on 3D vision according to claim 1, characterized in that: The steel mesh image captured by the 3D camera is binarized to facilitate subsequent image analysis; the steel mesh image is skeletonized using a 3D thinning algorithm, and the skeleton structure of the steel mesh is extracted by constructing an octree data structure and an iterative thinning process; any point in the skeletonized image is used as the center of a sphere, and a search is performed within a certain radius, and a set of steel bar intersection points is determined using a neighborhood statistics algorithm; the binding points are located using the least squares fitting method, and a sphere with each set of point sets as the edge is fitted using the least squares circle fitting algorithm, and the center of the fitted sphere is determined as the binding point; after the binding points are mapped to a two-dimensional plane, the center hole position of the steel mesh in the annular area is located using the Varignon parallelogram method.

3. The method for locating holes in annular steel mesh based on 3D vision according to claim 2, characterized in that: The binarization process is to set the steel bar value to 1 and the background value to 0.

4. The method for locating holes in annular steel mesh based on 3D vision according to claim 2, characterized in that: The 3D thinning algorithm includes the following steps: Initialization: load the 3D binary image into memory; Build an octree: Use a 3×3×3 octree data structure to check the 26 neighborhoods of each pixel; Iterative refinement: Identify and remove boundary pixels, generate a candidate list for removal, and sequentially recheck the pixels in the candidate list to maintain image connectivity; Termination condition: When the image no longer changes, stop the iteration; Post-processing: Eliminate noise and stray points to obtain a smoother and more centered skeleton.

5. The method for locating holes in annular steel mesh based on 3D vision according to claim 2, characterized in that: The neighborhood statistics calculation method is as follows: taking a point in the skeletonized image as the center of a sphere, searching within a certain radius, and counting the number of pixel points with a pixel value of 1 found within the range. If the number of points counted is greater than a certain threshold, it is determined to be an intersection point.

6. The method for locating holes in annular steel mesh based on 3D vision according to claim 2, characterized in that: The least squares fitting method for locating the lashing point is as follows: a least squares sphere fitting algorithm is used to fit a sphere with each set of intersection points as the edge, and finally the center of the fitted sphere is determined as the lashing point; The standard equation of a sphere is: (x-a) 2 +(y-b) 2 +(z-c) 2 =r 2 ; Where (x, y, z) is a point on the edge of the sphere, (a, b, c) are the coordinates of the center of the sphere, and r is the radius of the sphere. Expanding the equation and shifting the terms yields: x 2 +y 2 +z 2 -2ax-2by-2cz+(a 2 +b 2 +c 2 -r 2 )=0 Define a new variable d=a 2 +b 2 +c 2 -r 2 , then the above formula can be simplified to: -2ax-2by-2cz+d=-(x 2 +y 2 +z 2 ) This is further transformed into a standard linear form: Ax+By+Cz+D=E Where A=-2a,B=-2b,C=-2c,D=d,E=-(x 2 +y 2 +z 2 ); For a given n points (x i ,y i ,z i ), we can write n such equations to form a matrix form: M·p=E; in: M is an n×4 matrix of the form: p=[A,B,C,D] T is the parameter vector to be determined; e is an n×1 vector of the form: E=[−(x1 2 +y1 2 +z1 2 ), −(x2 2 +y2 2 +z2 2 ),…,-(x n 2 +y n 2 +z n 2 )] T Solve the equation M·p=E using the least squares method and get p=[A,B,C,D] T ; The matrix can be used to solve the formula: p=(M T M) -1 M T E From A = -2a, B = -2b, C = -2c, we can get the coordinates of the sphere center, i.e. the coordinates of the binding point: a=-(A) / 2, b=-(B) / 2, c=-(C) / 2.

7. The method for locating holes in annular steel mesh based on 3D vision according to claim 2, characterized in that: The Varigno parallelogram method for locating the holes in the steel mesh is as follows: the midpoints of adjacent binding points are calculated, and the intersection of the midpoints of the connecting lines is the center hole position of the steel mesh in the annular area.

8. A test device for the method for locating holes in annular steel mesh based on 3D vision according to any one of claims 1 to 7, characterized in that: The invention comprises a rotating mechanism (1) and a 3D sensor (2). The rotating mechanism (1) comprises a rotating base (1-1), a small gear (1-2), a large gear (1-3), a bearing (1-4), a rotating rod (1-5), and a servo motor (1-6). The rotating base (1-1) is fixed on a supporting platform and is engaged with the rotating rod (1-5) through the bearing (1-4). The servo motor (1-6) is fixed on the rotating rod (1-5). The servo motor (1-6) drives the small gear (1-2) to cooperate with the large gear (1-3) on the rotating base (1-1) to drive the rotating rod (1-5) to move circumferentially.

9. The test device according to claim 8, characterized in that A second servo motor (1-8) is installed at the end of the rotating rod (1-5), and the 3D sensor (2) is fixed on the moving block of the ball screw (2-7). The second servo motor (1-8) drives the moving block and the 3D sensor (2) to move radially along the direction of the ball screw (2-7).

Images