Curve path adaptive planning method and system, three-coordinate measuring instrument and equipment

Abstract

The invention relates to a curve path adaptive planning method and system, a three-coordinate measuring instrument and computer equipment, and the method comprises the steps: determining a curve measurement route, and carrying out the sampling based on a maximum sampling distance, and obtaining a plurality of measurement points; performing iterative detection based on the interval and the deflection angle between the adjacent measurement points, and inserting a newly added measurement point in the detection process so as to obtain a discrete measurement point sequence; a curve path is determined based on the sequence of measurement points. Therefore, the change of the geometric characteristics of the curve can be adaptively matched, so that measurement points are added in a key geometric characteristic region, and the measurement precision is ensured; and meanwhile, the deflection angle is used as an encryption criterion, so that the calculation complexity is effectively reduced, and the measurement efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of workpiece measurement technology, and in particular to a curve path adaptive planning method, system, coordinate measuring machine, and computer equipment. Background Technology

[0002] In the field of modern precision manufacturing, the coordinate measuring machine (CMM) is a core device for evaluating workpiece dimensions and geometric tolerances. Its measurement accuracy and efficiency directly affect product quality control. Currently, the measurement of complex workpieces is usually based on a theoretical CAD model, which is discretized into a series of dense measurement points, and the scanning measurement path of the probe is planned accordingly.

[0003] However, in the process of acquiring measurement points, traditional sampling methods mainly include equal-interval sampling and curvature-based adaptive sampling. While equal-interval sampling is simple to implement and computationally efficient, it fails to consider the differences in curve geometry and struggles to adjust point density according to curvature changes. This results in insufficient sampling in areas of high curvature and redundant sampling in areas of low curvature, affecting measurement accuracy and efficiency. Curvature-based adaptive sampling can dynamically adjust the point spacing based on the local curvature of the curve, but it relies on calculating the second derivative of the curve, resulting in high algorithm complexity, high computational resource consumption, and low efficiency in practical applications. Summary of the Invention

[0004] Therefore, it is necessary to provide a curve path adaptive planning method, system, coordinate measuring machine, and computer equipment that can achieve fast, adaptive sampling and meet the requirements of high-precision measurement, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a curve path adaptive planning method applied to a coordinate measuring machine (CMM). The CMM includes a measuring machine with a probe and a motion control device. The motion control device performs curve path planning and coordinated control for the measuring machine based on acquired measurement points. The probe measures the workpiece to be measured along the set curve path. The curve path adaptive planning method includes: acquiring a curve measurement path on the workpiece to be measured; determining multiple measurement points from the curve measurement path based on a preset maximum sampling distance; and performing curve path planning and coordinated control of the multiple measurement points along the curve measurement path. The measurement points are iteratively detected to obtain a discrete sequence of measurement points; the curve path is determined based on the sequence of measurement points; wherein, the iterative detection includes: determining whether the interval between two adjacent measurement points is greater than a preset minimum sampling distance; if it is greater than the minimum sampling distance, determining whether the deflection angle at the location of the two measurement points is greater than a preset angle threshold; if the deflection angle is greater than the angle threshold, inserting a new measurement point in the area where the two measurement points are located; repeating the above steps until the interval between any two adjacent measurement points is not greater than the minimum sampling distance, or the deflection angle is not greater than the angle threshold.

[0006] In one embodiment, the deflection angle is defined as the deflection angle between the tangent directions at the locations of two adjacent measurement points on the curve measurement route, or the deflection angle formed by the lines connecting the selected measurement point to the adjacent measurement points on both sides.

[0007] In one embodiment, determining multiple measurement points from the curve measurement route based on a preset maximum sampling distance includes: determining a sampling step size based on the maximum sampling distance to sample and acquire multiple measurement points from the curve measurement route.

[0008] In one embodiment, the plurality of measurement points are distributed along the curve measurement route with equal arc lengths.

[0009] In one embodiment, the area where the two measurement points are located includes the area between each of the two measurement points and the measurement points on the adjacent sides.

[0010] In one embodiment, the spacing between adjacent measurement points in the region where the newly added measurement point is inserted is smoothly and gradually changed.

[0011] In one embodiment, the minimum allowable interval between adjacent measurement points is the minimum sampling distance.

[0012] Secondly, this application also provides a curve path adaptive planning system applied to a coordinate measuring machine (CMM). The CMM includes a measuring machine with a probe and a motion control device. The motion control device performs curve path planning and coordinated control for the measuring machine based on acquired measurement points. The probe measures the workpiece to be measured along the set curve path. The curve path adaptive planning system includes: a curve determination unit for acquiring the measurement route on the workpiece to be measured; a sampling unit for determining multiple measurement points from the measurement route based on a preset maximum sampling distance; an adaptive iteration unit configured to iteratively detect multiple measurement points based on the interval and deflection angle between adjacent measurement points. During the iterative detection process, new measurement points are inserted in the region where the interval is greater than the minimum sampling distance and the deflection angle is greater than an angle threshold, until the interval between any adjacent measurement points is no greater than the minimum sampling distance or the deflection angle is no greater than the angle threshold, thereby obtaining a discrete sequence of measurement points; and a path planning unit for determining the curve path based on the sequence of measurement points.

[0013] Thirdly, this application also provides a coordinate measuring machine, which includes a measuring machine with a probe and a motion control device. The motion control device is used to perform curve path adaptive planning method in any of the above embodiments to plan a curve path for the measuring machine. The motion control device controls the measuring machine based on the curve path so that the probe measures the workpiece to be measured along the set curve path.

[0014] Fourthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the measurement path planning steps in any of the above embodiments.

[0015] Fifthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of path planning in any of the above embodiments.

[0016] Sixthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of path planning in any of the above embodiments.

[0017] The aforementioned adaptive curve path planning method, system, coordinate measuring machine, and computer equipment first determine the curve measurement route and perform initial sampling based on the maximum sampling distance to obtain multiple measurement points. Then, iterative detection is performed based on the interval and deflection angle between adjacent measurement points. During the detection process, new measurement points are quickly identified and inserted, enabling the measurement point set to adaptively match the changes in the curve's geometric features. This increases measurement points in key geometric feature areas, ensuring measurement accuracy. Simultaneously, this scheme uses the deflection angle as the encryption criterion, effectively reducing computational complexity and improving measurement efficiency, making it better suited for large-scale data scenarios involving complex workpiece measurement. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a diagram illustrating the application environment of a coordinate measuring machine in one embodiment; Figure 2 This is a flowchart illustrating a measurement path adaptive planning method in one embodiment; Figure 3 This is a schematic diagram illustrating the determination of a measurement route on a digital model in one embodiment; Figure 4 This is a schematic diagram illustrating the determination of multiple measurement points on the measurement route based on the maximum sampling distance in one embodiment; Figure 5 This is a schematic diagram of the iterative detection process for measurement points in one embodiment; Figure 6 This is a flowchart illustrating the process of determining a sequence of measurement points along a measurement route in one embodiment. Figure 7 This is a block diagram of a measurement path adaptive planning system in one embodiment; Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various data, but these data are not limited by these terms. These terms are only used to distinguish the first data from the second data. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more.

[0022] The adaptive planning method for curve paths provided in this application can be applied to, for example... Figure 1 The application environment is shown. The coordinate measuring machine (CMM) includes a measuring machine 120 and a motion control device 110. The motion control device 110 is used for measurement path planning and coordinated control of the measuring machine 120, including motion position and speed planning for each motion position. The measuring machine 120 includes a multi-axis drive structure and a probe. The multi-axis drive structure, controlled by the motion control device 110, controls the probe's motion along the axes. The probe can measure the workpiece along the set measurement path. Optionally, the motion control device 110 includes a host computer 112 and a motion controller 114. When using the host computer 112, the motion controller 114 can control the corresponding measuring machine 120, and the measuring machine 120 can then control the probe for measurement. The host computer 112 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and IoT devices. IoT devices can include smart TVs, smart vehicle devices, projection devices, etc.

[0023] For example, the coordinate measuring machine includes a computer (i.e., a host computer 112), a motion controller 114, and a measuring machine 120. The host computer 112 has built-in measurement software and is responsible for configuring the measurement program and evaluating the measurement results. During the configuration of the measurement program, the host computer 112 can determine the measurement path of the workpiece and discretize it into a series of dense measurement points. The host computer 112 is connected to the motion controller 114 for information transmission. The motion controller 114 determines the measurement path based on the measurement points planned in the measurement program and coordinates the motor-driven measuring machine 120 to perform motion measurement according to the expected measurement path and feed back measurement signals to obtain measurement data. The measuring machine 120 includes a probe, a multi-axis drive structure, and a marble working platform. The multi-axis drive structure includes mutually perpendicular X-axis drive structures, Y-axis drive structures, and Z-axis drive structures, which control the relative movement of the probe and the marble platform in the X, Y, and Z directions, respectively. During the measurement process, the workpiece is placed on the marble working platform, and the probe is controlled to move according to the expected measurement path and contact the workpiece to perform measurement. The position of the contact point when the probe contacts the workpiece is recorded to obtain measurement data.

[0024] In one exemplary embodiment, see Figure 2 This paper provides a measurement path adaptive planning method, which can be applied to... Figure 1 Taking a coordinate measuring machine (CMM) as an example, the adaptive planning method for measurement paths (or curve path adaptive planning method, measurement path planning method) can be executed within the motion control device 110 of the CMM, including the following steps 220 to 280. Wherein: Step 220: Obtain the measurement path on the workpiece to be tested.

[0025] In some embodiments, see Figure 3 The measurement location on the digital model F (or CAD model F) of the workpiece to be measured is selected to define the measurement path S. The measurement path S can characterize the contour of the workpiece to be measured, such as an ideal geometric curve defined based on a CAD model or user interaction. The measurement software can describe the theoretical contour of the workpiece in the form of parametric equations, analytical equations, or NURBS (Non-Uniform Rational B-Splines) (i.e., ideal curve equations).

[0026] Specifically, during the editing of the measurement program in the measurement software, the feature extraction tool can be used to select a contour line on the digital model F loaded in the measurement software to define a measurement route S. For example, the curve feature tool can be used to select a target surface area on the digital model F to define a curved measurement route S (referred to as a curved measurement route). The curved measurement route is then described in the measurement software using an ideal curve equation.

[0027] Step 240: Determine multiple measurement points from the measurement route S based on the preset maximum sampling distance.

[0028] In some embodiments, the configuration interface of the measurement software provides a setting for the maximum sampling distance. Users can set an empirical threshold based on workpiece characteristics. For example, for blade-type workpieces, the maximum sampling distance can be set to 0.02 mm. Furthermore, the measurement software can pre-store the correspondence between workpiece types and maximum sampling distances, automatically using the corresponding maximum sampling distance or sampling step size as the default value after selecting a workpiece type.

[0029] Based on the determined maximum sampling distance, the software samples along the measurement route S with the corresponding sampling step size, and the generated sampling points are used as measurement points.

[0030] Optionally, the measurement point may include position information, i.e., three-dimensional coordinate data; this three-dimensional coordinate data is used to indicate the target spatial position to which the probe should move when measuring the workpiece. Optionally, the measurement point may also include vector information, i.e., the normal direction of the measurement point's position on the digital model F; this normal direction is used to determine the spatial attitude of the probe relative to the measured surface, for example, in actual measurement, maintaining a preset angle relationship between the probe's measurement axis and the surface's normal direction.

[0031] In some embodiments, the measurement software samples along the measurement route S to acquire multiple measurement points with a sampling step size no greater than the maximum sampling distance. Specifically, a fixed sampling step size is determined based on the maximum sampling distance, and sampling is performed with this fixed step size to generate a series of measurement points (also referred to as sampling points) distributed with equal chord lengths or equal arc lengths. An equal chord length distribution means that the straight-line distance between any two adjacent sampling points is equal, and this distance is the sampling step size; an equal arc length distribution means that the arc distance along the measurement route S between any two adjacent sampling points is equal, and this distance is also the sampling step size.

[0032] For example, see Figure 3 and Figure 4 The measurement software uses the maximum sampling distance as a fixed sampling step size to sample along the measurement route S, obtaining multiple sampling points with equal arc lengths distributed along the route S. In this case, the maximum sampling distance is first used to perform sparse sampling across the entire measurement route S, establishing an iterative benchmark with fewer sampling points for subsequent iterative detection. During iterative detection, measurement points are inserted only in necessary areas, reducing unnecessary dense sampling in flat areas, avoiding data redundancy, and effectively improving measurement efficiency. Optionally, the arc length of the measurement route S is calculated based on the ideal curve equation, and then the position of each sampling point is determined using the arc length and sampling step size to achieve an equal arc length distribution. If the length of the last segment is insufficient to meet the fixed sampling step size, sampling can be directly performed at the end position of the curve by default. For example, see... Figure 4 Multiple measurement points sampled along the curve measurement route are denoted as M0, M1, ..., M p M n This allows us to determine the spatial coordinates (i.e., three-dimensional coordinate data) of each measurement point.

[0033] In other embodiments, the measurement software may use a variable sampling step size to sample along the measurement path S. A variable sampling step size means that the straight-line distance or arc length between adjacent sampling points is not fixed during the sampling process, but changes with the sampling position. The maximum value of the variable sampling step size is also no greater than the maximum sampling distance.

[0034] Step 260: Along the measurement route S, iteratively detect multiple measurement points to obtain a discrete sequence of measurement points.

[0035] Iterative detection is performed based on the positional relationship between adjacent measurement points. During the detection process, new measurement points (also known as newly added measurement points) are quickly identified and inserted, enabling the final set of measurement points (i.e., the sequence of measurement points) to adaptively match changes in the geometric features of the curve. In this case, measurement accuracy is ensured by adding measurement points in key geometric feature regions (such as the leading and trailing edges of blades, where curvature changes significantly or is large).

[0036] Specifically, the deflection angle is calculated based on the position of the measurement point on the measurement route S. Iterative detection is performed based on the deflection angle between adjacent measurement points. During the detection process, new measurement points are inserted in the regions where measurement points with larger deflection angles are located, until the sampling requirements between adjacent measurement points are met. Here, the sampling requirements mean that the obtained sequence of measurement points can satisfy the condition that the deflection angle between any two adjacent measurement points is not greater than an angle threshold.

[0037] In this scenario, flat areas along the measurement route S, due to their gentle slope, minimize the need for excessive deflection angles between adjacent measurement points, thus requiring few or no additional measurement points. Conversely, critical geometric feature areas such as boundaries, abrupt curvature changes, or excessive curvature, where significant route variations can lead to excessive deflection angles between adjacent measurement points, necessitate the insertion of more new measurement points. Therefore, adaptive planning of measurement points is achieved, sparsely distributing them in flat areas to reduce unnecessary dense sampling, avoid data redundancy, and effectively improve measurement efficiency. Furthermore, measurement points are automatically densified in critical feature areas such as boundaries or abrupt curvature changes, ensuring accurate capture of geometric details and improving measurement accuracy.

[0038] In some embodiments, the deflection angle is defined as the angle between the tangent directions at the locations of two adjacent measurement points along the measurement route S (i.e., the tangent deflection angle). In this case, using the tangent deflection angle as a criterion for identifying key geometric feature regions and triggering encryption significantly reduces the computational complexity compared to traditional adaptive sampling, which requires a second derivative of the ideal curve equation to calculate curvature. For example, solving for the tangent deflection angle only requires a first derivative of the ideal curve equation.

[0039] Alternatively, the deflection angle is defined as the angle formed by the lines connecting the selected measurement point to the adjacent measurement points on either side. That is, three consecutive measurement points are obtained along the measurement route S, denoted as M. p 1. M p M p+1 M p Select the measurement point; connect M p 1 and M p Form the first vector L1, and connect M p With M p+1 A second vector L2 is formed; both the first vector L1 and the second vector L2 point from the front measurement point to the back measurement point along the direction of the measurement point arrangement, or they can both point from the back measurement point to the front measurement point; the deflection angle θ between the first vector L1 and the second vector L2 is calculated as the deflection angle. In this case, the deflection angle can be calculated using only three coordinate values ​​in one step, requiring only simple geometric operations and no complex surface fitting or differential operations, effectively reducing computational complexity and improving iteration efficiency.

[0040] In some embodiments, the configuration interface of the measurement software provides an angle threshold setting. Users can set an empirical threshold based on workpiece characteristics. For example, for blade-type workpieces, the angle threshold can be set to 3°, 5°, or 10°. Furthermore, the measurement software can pre-store the correspondence between workpiece types and angle thresholds, automatically using the corresponding angle threshold as the default value after selecting a workpiece type.

[0041] In this example, the deflection angle is used to characterize the degree of change in the direction of the measurement route S at different locations; the angle threshold serves as the judgment criterion. When the deflection angle is greater than the angle threshold, it indicates that the route direction has changed abruptly, corresponding to key geometric feature areas such as boundaries or curvature changes; when the deflection angle is less than the angle threshold, it indicates that the route direction is gentle, corresponding to flat areas. Therefore, it is possible to quickly determine whether new measurement points need to be inserted in the current area, ensuring high-precision measurement requirements.

[0042] In other embodiments, in addition to the deflection angle, the interval between adjacent measurement points is also incorporated as a detection requirement into the iterative detection. Specifically, iterative detection is performed based on the interval between adjacent measurement points and the deflection angle. During the detection process, new measurement points are inserted in the regions where measurement points with large intervals and large deflection angles are located; this continues until the sampling requirements between adjacent measurement points are met. In this embodiment, the sampling requirement means that any adjacent measurement points in the acquired measurement point sequence must satisfy at least one of the following: the interval is no greater than the minimum sampling distance, and the deflection angle is no greater than an angle threshold.

[0043] It should be noted that the minimum step distance of the measuring device is constrained by factors such as probe radius compensation and device performance. For example, if the step distance is too small, the probe radius compensation vectors of adjacent actual measuring points may interfere with each other, leading to compensation distortion. This example adds an interval as a detection requirement. When the interval between adjacent measuring points is not greater than the minimum sampling distance, the sampling density at that location is considered to meet the requirements, and there is no need to further densify (i.e., no need to insert measuring points). Thus, by limiting excessive densification through the minimum sampling distance, both high-precision measurement requirements and measurement efficiency are guaranteed.

[0044] In some embodiments, the interval between adjacent measurement points refers to the arc length between two measurement points on the measurement route S. Alternatively, the interval between adjacent measurement points refers to the straight-line distance between two measurement points. For example, during each detection, the arc length between two adjacent measurement points is compared with a set minimum sampling distance to determine whether the sampling density of the area meets the requirements.

[0045] In some embodiments, the configuration interface of the measurement software provides a setting for the minimum sampling distance. Users can set an empirical threshold based on workpiece characteristics and the minimum step distance. The minimum sampling distance must be no less than the minimum step distance. For example, for blade-type workpieces, this minimum sampling distance can be set to 0.001 mm. Furthermore, the measurement software can pre-store the correspondence between workpiece types and minimum sampling distances, automatically using the corresponding minimum sampling distance as the default value after selecting a workpiece type.

[0046] In some embodiments, see Figure 5 Iterative detection is performed based on the interval and deflection angle between adjacent measurement points. The iterative detection may include the following steps: Step 262: Determine whether the interval between two adjacent measurement points is greater than the preset minimum sampling distance.

[0047] The interval between adjacent measurement points is determined based on the currently acquired measurement points and compared with the minimum sampling distance. If the interval between two measurement points is not greater than the set minimum sampling distance, it means that the current measurement point position has met the encryption requirements (sampling requirements). If the interval between two measurement points is greater than the set minimum sampling distance, it is necessary to proceed to step 264 to further determine the deflection angle of the two measurement points.

[0048] Step 264: If the distance is greater than the minimum sampling distance, determine whether the deflection angle at the location of the two measurement points is greater than the preset angle threshold.

[0049] The deflection angle at the selected measurement point is determined based on step 262. If the deflection angle is not greater than the set angle threshold, it indicates that the route at the measurement point is relatively flat, and the sampling requirement is met. If the deflection angle is greater than the set angle threshold, it indicates that the route at the measurement point changes abruptly, and step 266 is required to encrypt the location of the current measurement point.

[0050] For example, in step 262, two measurement points with an interval greater than the minimum sampling distance are both selected measurement points; the deflection angles formed by the lines connecting the selected measurement points to the adjacent measurement points on both sides are obtained, and the route direction at the location of the two measurement points is determined. Alternatively, the tangents of the two selected measurement points at their locations on the measurement route S are obtained, the deflection angles of the two tangents are calculated, and the route direction between the two measurement points is determined.

[0051] Step 266: If the deflection angle is greater than the angle threshold, insert a new measurement point in the area where the two measurement points are located.

[0052] The area containing these two measurement points includes the region between each measurement point and the measurement points on either side of it. For example, the two measurement points determined in step 264 are represented as M. pM p+1 The area where the two measurement points are located can contain M p-1 With M p M p With M p+1 M p+1 With M p+2 The area between.

[0053] The measurement software can selectively insert new measurement points at target locations within the areas where the two measurement points are located, for encryption purposes. In some embodiments, the target location can be two measurement points M. p M p+1 The center location or other selected location.

[0054] In other embodiments, there are multiple target locations, which can be iteratively calculated based on sampling and distribution requirements.

[0055] Optionally, the distribution requirement may refer to the area where newly added measurement points are inserted, with adjacent measurement points distributed at equal intervals.

[0056] Alternatively, the distribution requirement can refer to the region where new measurement points are inserted, where the intervals between adjacent measurement points change smoothly and gradually. That is, from a sparse region with large intervals to a dense region with small intervals, the interval between measurement points gradually decreases; similarly, from a dense region with small intervals to a sparse region with large intervals, the interval between measurement points gradually increases. In this case, the numerical change in the interval between adjacent measurement points along the measurement route S in the acquired measurement point sequence is gradual, effectively suppressing abrupt changes and ensuring a smooth and continuous change in the interval between adjacent measurement points; it enables a smooth transition between dense and sparse regions. This reduces the vibration risk caused by abrupt changes in the interval between measurement points during route planning, improves the continuity of the change in the normal vector of adjacent measurement points, and makes the measurement data more accurate. It should be noted that the large and small intervals here are relative and not specifically specified numerical ranges.

[0057] For example, two adjacent measurement points M p M p+1 If the deflection angle at the location is greater than the angle threshold, then measurement points need to be inserted between the two measurement points to form a dense region with small intervals to meet the sampling requirements; if the area in front of the two measurement points is a sparse region with large intervals, a gradual transition area needs to be set between the sparse and dense regions, such as at measurement point M. p adjacent measurement point M on the front side p-1New measurement points are also inserted between these points, with the intervals between them decreasing monotonically and continuously to achieve a smooth transition from sparse to dense areas. If the area following two measurement points is a sparse region with large intervals between measurement points, a gradual transition zone needs to be set between the sparse and dense regions, such as at measurement point M. p+1 adjacent measurement point M on the rear side p+2 New measurement points are also inserted between dense and sparse areas, with the intervals between them increasing monotonically and continuously to achieve a smooth transition. Optionally, the intervals between measurement points within dense areas may also be smoothly gradual or evenly distributed. The interval between adjacent measurement points may be reduced to a value related to the minimum sampling distance, such as a maximum reduction to 50% of the minimum sampling distance.

[0058] Step 268: Traverse all measurement points until the interval between any adjacent measurement points is no greater than the minimum sampling distance, or the deflection angle is no greater than the angle threshold.

[0059] Along the measurement route S, adjacent measurement points are detected sequentially, i.e., steps 262 to 266 are repeated to traverse all measurement points. If the obtained measurement point sequence meets the sampling requirements, the iterative detection can be stopped, and subsequent step 280 can be executed. The final obtained measurement point sequence is designed to ensure that the interval between any two adjacent measurement points is no greater than the minimum sampling distance, or the deflection angle is no greater than the angle threshold.

[0060] This embodiment achieves adaptive densification of the measurement route S by combining sampling distance constraints and deflection angle constraints. Deflection angle is used to automatically densify in key geometric feature areas with high curvature and sparsely sample in flat areas. Simultaneously, a minimum sampling distance limits excessive densification, while a maximum sampling distance ensures full route coverage. The entire process eliminates the need for complex curvature calculations, significantly reducing algorithm complexity and computational resource consumption, and resulting in rapid iterative convergence.

[0061] Steps 220 to 260 above describe the process of determining the sequence of measurement points on the measurement route S, enabling adaptive planning of measurement points. For example, see... Figure 6 The sequence of measurement points generated by the curve measurement route contains k+1 measurement points. If multiple measurement points are reordered, they are represented as T0, T1, ..., T k This allows us to determine the spatial coordinates of each measurement point. A measurement point can be represented as: , among which, T i Let X represent the (i+1)th measurement point. i Y i Z i Let be the spatial coordinates of the (i+1)th measurement point. The subsequent step 280 is the process of planning the measurement path.

[0062] Step 280: Determine the measurement path based on the measurement point sequence.

[0063] The measurement path to be executed by the probe is generated based on the position information of multiple measurement points in the measurement point sequence. Specifically, the trajectory parameter equation C(u) representing the measurement path of the probe is obtained based on the measurement point sequence.

[0064] In some embodiments, a discrete sequence of measurement points is converted into a trajectory parametric equation using spline curve interpolation. In this case, the discrete sequence of measurement points is transformed into a smooth parametric curve representing the probe's measurement path (e.g., a curved path). Specifically, the trajectory parametric equation C(u) describes how the position of each point on the curve along the measurement path changes with the curve node parameter u (or curve parameter, parameter). Multiple measurement points M0, M1, ..., M n For discrete samples, the trajectory parametric equation C(u) is a continuous function satisfying: C(u) i )=M i ,i=0,1,…,n; where u i It is the curve node parameter corresponding to the (i+1)th measurement point.

[0065] Optionally, spline interpolation methods can include cubic spline interpolation, B-spline interpolation, or NURBS, with NURBS being used as an example below. Of course, besides spline interpolation, other methods can also be used to convert discrete measurement points into trajectory parametric equations that describe the measurement path, without excessive restrictions.

[0066] Optionally, a NURBS curve is used to represent the measurement path; the trajectory parameter equation C(u) is expressed as:

[0067] Where C(u): represents the curve position at the curve node parameter u; u: curve node parameter, usually in the range [0, 1]; P i : The coordinates (X) of the (i+1)th control point i Y i Z i n+1: represents the number of control points, denoted as P0, P1, ..., P n-1 P n W i : The weight of the (i+1)th control point, which is either defaulted to 1 or calculated according to mathematical rules when inverting the curve; N i,p (u): p-th B-spline basis function, which defines the value of the (i+1)th basis function at the curve parameter u.

[0068] Specifically, the process of obtaining the trajectory parametric equation C(u) may include: Control points are calculated based on the obtained sequence of measurement points. These control points (P in the NURBS curve equation) are... i A spline curve defined by control points is a spatial point used to define the shape of a curve. A spline curve defined by control points can pass precisely through known measurement points. Using measurement points to calculate control points is a known method and is not subject to many restrictions.

[0069] The curve node parameter u is calculated based on the sequence of measurement points. Specifically, the curve node parameter u is obtained based on curve information (i.e., measurement points and control points) and using the chord length parameter method; the curve node parameter u refers to the normalized parameter value assigned to each measurement point using the chord length parameter method. For example:

[0070] Among them, u i This refers to the curve node parameters corresponding to the (i+1)th measurement point. k This is the (k+1)th control point. The calculation of the curve node parameter u can be performed on the host computer 112.

[0071] The trajectory parametric equation C(u) is established based on the curve node parameters and control points. The process of establishing the trajectory parametric equation C(u) can be executed in the host computer 112.

[0072] Subsequently, velocity planning and real-time interpolation calculations are performed based on the trajectory parameter equation C(u). Specifically, according to the spatial distribution of each measurement point on the measurement path and the preset kinematic constraints (including maximum allowable speed, maximum allowable acceleration, etc.), the velocity curve of the measuring machine 120 moving along the measurement path is planned, so that the motion speed adaptively decreases in the dense area of ​​measurement points and adaptively increases in the sparse area of ​​measurement points, thereby achieving dynamic matching between speed and measurement point density. Subsequently, in the real-time control stage, the motion controller 114 discretizes the curve parameter u with a preset interpolation period and calculates the position coordinates C(u) of each interpolation point cycle by cycle. k This generates continuous motion commands, driving the measuring machine 120 to run smoothly along the planned path. In this case, through the smooth transition of speed and the continuous generation of motion trajectory, the probe can be driven to continuously, smoothly, and accurately perform complex spatial trajectory movements along the preset measurement path to complete the measurement of the workpiece under test, reducing vibration during sampling and improving measurement accuracy.

[0073] Based on the same inventive concept, this application also provides a measurement path adaptive planning system 130 (or curve path adaptive planning system) for implementing the measurement path adaptive planning method described above. The solution provided by the measurement path adaptive planning system 130 is similar to the solution described in the above method. Therefore, the specific limitations of one or more measurement path adaptive planning systems 130 provided below can be found in the limitations of the measurement path adaptive planning method above, and will not be repeated here.

[0074] In one exemplary embodiment, such as Figure 7 As shown, a measurement path adaptive planning system 130 is provided. The measurement path adaptive planning system 130 is disposed in the motion control device 110 and includes: The curve determination unit 131 is used to obtain the measurement path S on the workpiece to be measured. That is, the curve determination unit 131 is used to execute step 220 in the measurement path adaptive planning method.

[0075] The sampling unit 132 is used to determine multiple measurement points from the measurement route S based on a preset maximum sampling distance. That is, the sampling unit 132 is used to execute step 240 in the measurement path adaptive planning method.

[0076] The adaptive iteration unit 133 is used to iteratively detect multiple measurement points along the curve measurement route to obtain a discrete sequence of measurement points. That is, the adaptive iteration unit 133 is used to execute step 260 in the adaptive planning method for the measurement path. For example, the adaptive iteration unit 133 performs iterative detection based on the interval and deflection angle between adjacent measurement points. During the detection process, new measurement points are inserted in the regions where measurement points with large intervals and large deflection angles are located; this continues until the sampling requirements are met between any two adjacent measurement points, thus obtaining a discrete sequence of measurement points.

[0077] The path planning unit 134 is configured to determine the measurement path based on the sequence of measurement points. That is, the path planning unit 134 is used to perform step 280 in the adaptive planning method for the measurement path.

[0078] In some embodiments, the measurement path adaptive planning system 130 may further include a velocity planning unit and an interpolation unit (not shown). The velocity planning unit performs velocity planning based on the trajectory parameter equation C(u) and optimizes the velocity curve along the measurement path using a look-ahead algorithm. The interpolation unit obtains real-time interpolation points based on the velocity curve and the trajectory parameter equation. Specifically, within each interpolation cycle T, the interpolation unit obtains the interpolation curve parameters using a nodal prediction method based on the pre-planned velocity curve, and calculates the target position to be reached in the current interpolation cycle T, i.e., the interpolation point coordinates, in real time, in conjunction with the trajectory parameter equation C(u). Optionally, the interpolation unit may be located in the motion controller 114.

[0079] In some embodiments, the measurement path adaptive planning system 130 may further include a drive unit (not shown), which generates a drive signal based on the real-time interpolation point and sends it to the multi-axis drive structure. This enables the probe to continuously, smoothly, and accurately move along a preset measurement path to perform complex spatial trajectory movements to complete the scanning measurement of the workpiece, reducing vibration and improving measurement efficiency and quality. Optionally, the drive unit may be located in the motion controller 114.

[0080] Each module in the aforementioned adaptive planning system 130 for measurement paths can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the coordinate measuring machine (CMM) in hardware form or independent of it, or stored in the memory of the CMM in software form, so that the processor can call and execute the corresponding operations of each module.

[0081] In one exemplary embodiment, a computer device is provided, which may be a host computer 112, and its internal structure diagram may be as follows. Figure 8As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with an external host computer 112. Wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments. The display unit of the computer device forms a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0082] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0083] In one embodiment, a coordinate measuring machine (CMM) is also provided. The CMM includes a motion control device 110 and a measuring machine 120. The motion control device 110 includes a memory and a processor. The memory stores a computer program. The measuring machine 120 includes a probe. When the processor executes the computer program, it implements the measurement path planning steps in any of the above embodiments until a measurement path is obtained. The motion control device 110 controls the measuring machine 120 based on a curved path so that the probe measures the workpiece to be measured along a set curved path.

[0084] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0085] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0086] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one of relational databases and non-relational databases. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0090] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An adaptive planning method for curved paths, characterized in that, The adaptive curve path planning method is applied to a coordinate measuring machine (CMM), which includes a measuring machine with a probe and a motion control device. The motion control device performs curve path planning and coordinated control for the measuring machine based on the acquired measurement points. The probe measures the workpiece to be measured along the set curve path. The adaptive curve path planning method includes: Obtain the curve measurement path on the workpiece to be tested; Multiple measurement points are determined from the curve measurement route based on a preset maximum sampling distance; Along the curve measurement route, multiple measurement points are iteratively detected to obtain a discrete sequence of measurement points; The curve path is determined based on the sequence of measurement points; The iterative detection includes: Determine whether the interval between two adjacent measurement points is greater than the preset minimum sampling distance; If the distance is greater than the minimum sampling distance, then determine whether the deflection angle at the location of the two measurement points is greater than the preset angle threshold. If the deflection angle is greater than the angle threshold, then a new measurement point is inserted in the area where the two measurement points are located; Repeat the above steps until the interval between any adjacent measurement points is no greater than the minimum sampling distance, or the deflection angle is no greater than the angle threshold.

2. The method according to claim 1, characterized in that, The deflection angle is defined as the deflection angle between the tangent directions at the locations of two adjacent measurement points on the curve measurement route, or the deflection angle formed by the lines connecting the selected measurement point to the adjacent measurement points on both sides.

3. The method according to claim 1, characterized in that, The step of determining multiple measurement points from the curve measurement route based on a preset maximum sampling distance includes: determining a sampling step size based on the maximum sampling distance to sample and obtain multiple measurement points from the curve measurement route.

4. The method according to claim 3, characterized in that, The multiple measurement points are distributed with equal arc lengths along the curve measurement route.

5. The method according to claim 1, characterized in that, The area where the two measurement points are located includes the area between each of the two measurement points and the measurement points on the adjacent sides.

6. The method according to claim 5, characterized in that, In the region where the newly added measurement point is inserted, the interval between adjacent measurement points is smoothly and gradually changes.

7. The method according to claim 6, characterized in that, The minimum allowable interval between adjacent measurement points is the minimum sampling distance.

8. An adaptive planning system for curved paths, characterized in that, The adaptive curve path planning system is applied to a coordinate measuring machine (CMM), which includes a measuring machine with a probe and a motion control device. The motion control device performs curve path planning and coordinated control for the measuring machine based on acquired measurement points. The probe measures the workpiece to be measured along the set curve path. The adaptive curve path planning system includes: A curve determination unit is used to acquire the measurement path on the workpiece to be measured; A sampling unit is used to determine multiple measurement points from the measurement route based on a preset maximum sampling distance; An adaptive iterative unit is configured to iteratively detect multiple measurement points based on the interval and deflection angle between adjacent measurement points. During the iterative detection process, new measurement points are inserted in the region where the interval is greater than the minimum sampling distance and the deflection angle is greater than the angle threshold, until the interval between any two adjacent measurement points is no greater than the minimum sampling distance or the deflection angle is no greater than the angle threshold, thereby obtaining a discrete sequence of measurement points. A path planning unit is used to determine the curve path based on the sequence of measurement points.

9. A coordinate measuring machine, characterized in that, The coordinate measuring machine includes a measuring machine with a probe and a motion control device. The motion control device is used to perform curve path adaptive planning for the measuring machine by executing the curve path adaptive planning method as described in any one of claims 1 to 7. The motion control device controls the measuring machine based on the curve path so that the probe measures the workpiece to be measured along the set curve path.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

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