Cantilever crane anti-collision detection method, device and equipment and storage medium

Abstract

The invention discloses an anti-collision detection method and device for a cantilever crane, equipment and a storage medium. The method comprises the following steps: acquiring first point cloud data of the boom; performing curve fitting on the first point cloud data to obtain a boom curve of the boom; determining a target curvature of the boom based on the boom curve; based on the target curvature, target modeling parameter values are determined, and the target modeling parameter values are parameter values of modeling parameters used for spatial modeling; performing spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom curvature boom spatial model; anti-collision detection is carried out based on the boom space model, an anti-collision detection result of the boom is generated, and the anti-collision detection result is used for indicating the obstacle having the collision risk with the boom. Therefore, the detection precision of the anti-collision detection of the cantilever crane can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of operating machinery, and in particular, relates to an anti-collision detection method, device, equipment and storage medium for an arm. Background Art

[0002] With the rapid development of operating machinery technology, intelligent and high-safety operations have become the goals of operating machinery design. Among them, operating machinery with long and flexible booms, such as cranes, ladder fire trucks, and pump trucks, are widely used engineering machinery. During operation, there is a risk of collision between the boom and obstacles in the operating scene, thus affecting the operating safety of the operating machinery.

[0003] In the related art, in order to avoid the collision between the boom and obstacles during operation, image recognition technology is usually used, that is, the distance between the boom and the surrounding obstacles is detected based on the image of the boom during movement, so as to determine the obstacles with the risk of collision with the boom. However, image recognition technology may have low detection accuracy in certain scenarios, such as low visibility or long distance of detection position.

[0004] It can be seen that the anti-collision detection of the boom in the related art has the problem of low detection accuracy. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment and storage medium for anti-collision detection of an arm, which can improve the detection accuracy of anti-collision detection of the arm.

[0006] In a first aspect, an embodiment of the present application provides an anti-collision detection method for an arm, comprising:

[0007] Acquire first point cloud data of the boom;

[0008] Performing curve fitting on the first point cloud data to obtain a boom curve of the boom;

[0009] Based on the boom curve, determining a target curvature of the boom;

[0010] Based on the target curvature, determining a target modeling parameter value, wherein the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling;

[0011] Performing spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom spatial model of the boom;

[0012] Anti-collision detection is performed based on the boom space model to generate an anti-collision detection result of the boom, wherein the anti-collision detection result is used to indicate obstacles that have a risk of collision with the boom.

[0013] In some embodiments, determining the target curvature of the boom based on the boom curve includes:

[0014] Dividing the boom curve to obtain a plurality of sub-boom curves;

[0015] Based on each of the sub-boom curves, determining the curvature of the sub-boom curve, wherein the target curvature includes the curvatures of the multiple sub-boom curves;

[0016] Determining the parameter value of the target modeling parameter based on the target curvature includes:

[0017] Based on the curvature of each of the sub-boom curves, determining a sub-modeling parameter value corresponding to the sub-boom curve;

[0018] The boom spatial model includes spatial models of multiple sub-booms, the multiple sub-booms correspond to the multiple sub-boom curves, and the spatial model of each sub-boom is constructed based on the sub-modeling parameters corresponding to the sub-boom.

[0019] In some embodiments, dividing the boom curve to obtain the plurality of sub-boom curves includes:

[0020] Determine a first quantity according to the length of the boom and a preset maximum error, wherein the preset maximum error is a maximum error allowed by the boom anti-collision calculation;

[0021] The boom curve is divided into equal intervals to obtain the first number of sub-boom curves.

[0022] In some embodiments, before determining the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve, the method further includes:

[0023] determining a plurality of sampling points in the boom curve;

[0024] determining a curvature of at least one sampling point among the plurality of sampling points;

[0025] Determining the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve includes:

[0026] Based on the curvature of each of the sub-boom curves and the curvature of the at least one sampling point, a sub-modeling parameter value corresponding to the sub-boom curve is determined.

[0027] In some embodiments, determining a plurality of sampling points in the boom curve comprises:

[0028] Determine a second quantity based on the length of the boom and a preset minimum error, wherein the preset minimum error is a minimum error allowed by the boom anti-collision calculation;

[0029] A second number of sampling points are selected at equal intervals in the boom curve.

[0030] In some embodiments, determining the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve and the curvature of the plurality of sampling points includes:

[0031] Determine a maximum curvature and a minimum curvature among the curvatures of each of the plurality of sampling points;

[0032] Based on the curvature of each of the sub-boom curves and the maximum curvature and the minimum curvature, a sub-modeling parameter value corresponding to the sub-boom curve is determined.

[0033] In some embodiments, the number of the sub-arm curves is a first number, and the number of the sampling points is a second number,

[0034] The determining of the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve and the maximum curvature and the minimum curvature includes:

[0035] Based on the curvature of each of the sub-boom curves and the maximum curvature, the minimum curvature, the preset minimum error, and the preset maximum error, a sub-modeling parameter value corresponding to the sub-boom curve is calculated.

[0036] In some embodiments, before performing anti-collision detection based on the boom space model and generating an anti-collision detection result of the boom, the method further includes:

[0037] Acquire second point cloud data of obstacles around the boom;

[0038] generating an obstacle space model of the obstacle based on the second point cloud data;

[0039] The performing anti-collision detection based on the boom space model to generate the anti-collision detection result of the boom includes:

[0040] Anti-collision detection is performed on the boom space model and the obstacle space model to generate an anti-collision detection result of the boom.

[0041] In some implementations, the parameter value of the modeling parameter includes a model grid resolution, and the model grid resolution is negatively correlated with the target curvature.

[0042] In a second aspect, an embodiment of the present application further provides an anti-collision detection device for an arm, comprising:

[0043] A point cloud data acquisition module, used to acquire first point cloud data of the boom;

[0044] A curve fitting module, used for performing curve fitting on the first point cloud data to obtain a boom curve of the boom;

[0045] A curvature determination module, used for determining a target curvature of the boom based on the boom curve;

[0046] A modeling parameter value determination module, used to determine a target modeling parameter value based on the target curvature, wherein the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling;

[0047] A modeling module, configured to perform spatial modeling on the first point cloud data based on the target modeling parameter value to obtain the boom curvature boom spatial model;

[0048] An anti-collision detection module is used to perform anti-collision detection based on the boom space model to generate an anti-collision detection result of the boom, wherein the anti-collision detection result is used to indicate obstacles that have a risk of collision with the boom.

[0049] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising: a processor and a memory storing computer program instructions;

[0050] When the processor executes the computer program instructions, the anti-collision detection method for the boom as described in the first aspect is implemented.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the anti-collision detection method for the boom as described in the first aspect is implemented.

[0052] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the anti-collision detection method for the arm as described in the first aspect.

[0053] In a sixth aspect, an embodiment of the present application provides an operating machine, including a boom, a point cloud acquisition device, and the electronic device as described in the third aspect.

[0054] The anti-collision detection method, device, equipment and computer storage medium of the boom of the embodiment of the present application obtain first point cloud data of the boom; perform curve fitting on the first point cloud data to obtain a boom curve of the boom; determine a target curvature of the boom based on the boom curve; determine a target modeling parameter value based on the target curvature, the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling; perform spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom curvature boom spatial model; perform anti-collision detection based on the boom spatial model to generate an anti-collision detection result of the boom, which is used to indicate obstacles that have a risk of collision with the boom. In this way, since the boom spatial model is constructed based on the point cloud data of the boom, compared with image recognition technology, the acquisition of point cloud data is less affected by scene changes, thereby improving the detection accuracy of obstacles that pose a risk of collision with the boom, that is, improving the detection accuracy of boom anti-collision detection; in addition, since the modeling parameter values ​​used in model construction are determined based on the target curvature of the boom, the influence of boom deformation during operation on the accuracy of the constructed boom spatial model is taken into account, thereby further improving the accuracy of the constructed boom spatial model, and further improving the detection accuracy of obstacles that pose a risk of collision with the boom. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 It is a flow chart of an embodiment of the anti-collision detection method for the boom provided by the present application;

[0057] Figure 2 It is a flowchart of an application example of the anti-collision detection method of the boom provided in the present application;

[0058] Figure 3 It is a structural schematic diagram of an embodiment of an anti-collision detection device for an arm provided in the present application;

[0059] Figure 4 It is a schematic diagram of the structure of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0060] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0062] Figure 1 A flowchart of an embodiment of the anti-collision detection method for the boom provided in the present application is shown. The anti-collision detection method for the boom of the present application can be applied to an electronic device, which can be a central control device in an operating machine including the boom, or a smart phone, a portable computer or a server (such as a cloud server) outside the operating machine. Figure 1 As shown, the anti-collision detection method of the boom provided in the embodiment of the present application includes the following steps:

[0063] Step S101, obtaining first point cloud data of the boom;

[0064] Step S102, performing curve fitting on the first point cloud data to obtain a boom curve of the boom;

[0065] Step S103, determining a target curvature of the boom based on the boom curve;

[0066] Step S104: determining a target modeling parameter value based on the target curvature, where the target modeling parameter value is a parameter value of a modeling parameter used for space modeling;

[0067] Step S105, performing spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom curvature boom spatial model;

[0068] Step S106: perform anti-collision detection based on the boom space model to generate an anti-collision detection result of the boom, where the anti-collision detection result is used to indicate obstacles that have a risk of collision with the boom.

[0069] In an embodiment of the present application, first point cloud data of the boom is acquired; curve fitting is performed on the first point cloud data to obtain a boom curve of the boom; based on the boom curve, a target curvature of the boom is determined; based on the target curvature, a target modeling parameter value is determined, and the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling; spatial modeling is performed on the first point cloud data based on the target modeling parameter value to obtain a boom curvature boom spatial model; anti-collision detection is performed based on the boom spatial model to generate an anti-collision detection result of the boom, and the anti-collision detection result is used to indicate obstacles that pose a risk of collision with the boom. In this way, since the boom spatial model is constructed based on the point cloud data of the boom, compared with image recognition technology, the acquisition of point cloud data is less affected by scene changes, thereby improving the detection accuracy of obstacles that pose a risk of collision with the boom, that is, improving the detection accuracy of boom anti-collision detection; in addition, since the modeling parameter values ​​used in model construction are determined based on the target curvature of the boom, the influence of boom deformation during operation on the accuracy of the constructed boom spatial model is taken into account, thereby further improving the accuracy of the constructed boom spatial model, and further improving the detection accuracy of obstacles that pose a risk of collision with the boom.

[0070] In the above step 101, the first point cloud data of the boom is obtained by performing point cloud sensing in real time through a point cloud acquisition device during the movement of the boom, and detecting and obtaining point cloud data, which includes the first point cloud data of the boom. The point cloud acquisition device transmits the detected point cloud data to an electronic device.

[0071] The above-mentioned boom is a structure in an operating machine, and the operating machine may be a crane, a ladder fire truck or a pump truck, etc.

[0072] The above-mentioned point cloud acquisition device can be any device that can sense point clouds to detect and obtain point cloud data, which can include at least one of radars such as ultrasonic radar, laser radar and millimeter wave radar. Among them, the point cloud data (including the first point cloud data) detected by the point cloud acquisition device can also be called a three-dimensional point cloud or point cloud, which is a discrete data set composed of a series of three-dimensional coordinate points. Each coordinate point includes coordinate values ​​in the three directions of X, Y, and Z, which are used to describe the surface shape, spatial position, size and other geometric features of the object. It should be noted that the point cloud acquisition device can be set on the boom or at other positions of the operating machinery except the boom, which is not limited here.

[0073] The first point cloud data may be point cloud data including preset key parts in the boom, and the preset key parts may include the positions of hinge points, end points, and center of gravity.

[0074] The electronic device obtains the first point cloud data by extracting the point cloud data of the preset key part from the point cloud data detected by the point cloud acquisition device through feature extraction; or, it can be obtained based on the positioning of the key part of the boom, that is, a radar sensor is pre-arranged at the preset key part to collect the point cloud data of the preset key part, and the electronic device directly obtains the point cloud data of the preset key part from the point cloud data.

[0075] In the above step 102, when the electronic device obtains the first point cloud data, the electronic device may perform curve fitting on the first point cloud data to obtain a boom curve of the boom.

[0076] The above-mentioned curve fitting is performed on the first point cloud data to obtain the boom curve of the boom, and the electronic device may fit the first point cloud data using a preset curve fitting equation to obtain the boom curve.

[0077] In the above step 103, when the arm curve is obtained by fitting, the electronic device can determine the target curvature of the table based on the arm curve.

[0078] The target curvature of the boom is determined based on the boom curve by acquiring an image of the boom curve and identifying the curvature of the boom curve as the target curvature through image recognition technology. The curvature of the boom curve can be understood as an approximate curvature or a real curvature.

[0079] The above-mentioned target curvature may be the overall curvature of the boom curve. For example, the approximate curvatures of multiple nodes of the boom curve may be identified by image recognition technology, and the average of the approximate curvatures may be used as the target curvature; or, the overall curvature of the boom curve may be identified by image recognition technology, and the overall curvature may be used as the target curvature.

[0080] In the above step 104, when the target curvature of the boom is determined, the electronic device may determine the target modeling parameter value of the modeling parameter based on the target curvature.

[0081] The above-mentioned modeling parameters may be any parameters in the target space modeling strategy (method) based on point cloud data, and when the parameter value of the modeling parameter changes, the three-dimensional space model constructed by the target space modeling strategy will also change.

[0082] The target space modeling strategy may include at least one of a boundary representation scheme (Boundary Representation Scheme, BRS), a scanning representation, a constructive solid geometry method, and a space unit representation. For example, when the space modeling strategy is a scanning representation, the modeling parameter value may be a parameter value of at least one parameter of a trajectory parameter, a section parameter, and a scanning type.

[0083] Furthermore, the above-mentioned target space modeling strategy is an octree subdivision space gridding modeling strategy, and the above-mentioned modeling parameters include the resolution of the octree subdivision space gridding modeling strategy (ie, the minimum voxel size), and so on.

[0084] The target modeling parameter value is determined based on the target curvature. The electronic device may have a preset correspondence between the curvature and the modeling parameter value, and the electronic device determines the modeling parameter value having a correspondence with the target curvature as the target modeling parameter value.

[0085] For example, in the case where the target space modeling strategy is a scanning representation method, the electronic device may have a preset correspondence between the cross-section parameter value and the curvature, and the electronic device may determine the cross-section parameter value that corresponds to the target curvature as the target cross-section parameter value (i.e., the target modeling parameter value); or, in the case where the target space modeling strategy is an octree subdivision space gridding modeling strategy, the electronic device may have a preset correspondence between the resolution and the curvature, and the electronic device may determine the resolution that corresponds to the target curvature as the target resolution (i.e., the target modeling parameter value). In the above step 105, when the target modeling parameter value is determined, the electronic device may update the parameter value of the modeling parameter of the target space modeling strategy to the above target modeling parameter value, and use the updated target modeling strategy to model the first point cloud data to obtain the boom space model of the boom.

[0086] For example, when the above-mentioned target modeling strategy is a scanning representation, the electronic device updates the parameter values ​​of the section parameters of the scanning representation to the target section parameter values, and uses the updated scanning representation to construct the arm space model; and when the above-mentioned spatial modeling strategy is an octree subdivision spatial meshing modeling strategy, the electronic device updates the resolution of the octree subdivision spatial meshing modeling strategy to the target resolution, and uses the updated octree subdivision spatial meshing modeling strategy to construct the arm space model.

[0087] In the case where the target modeling parameter value is a parameter value determined by the overall curvature of the boom curve, the constructed boom space model may be a three-dimensional space model of the entire boom.

[0088] In the above step 106, when the above boom space model is generated, the electronic device performs anti-collision detection based on the boom space model and generates an anti-collision detection result to determine obstacles that have a risk of collision with the boom.

[0089] The above-mentioned anti-collision detection based on the boom space model to generate the anti-collision detection result may be that the electronic device also obtains the obstacle space model of the obstacles around the boom, performs anti-collision detection on the boom space model and the obstacle space model, and obtains the anti-collision detection result.

[0090] The obstacle space model of the obstacles around the boom may be obtained by scanning the obstacle space model of the obstacles around the boom using a scanning device (such as a scanner installed on the boom; or a drone independent of the boom, etc.).

[0091] The above-mentioned anti-collision detection of the boom space model and the obstacle space model may be to calculate the minimum spacing between the boom space model and each obstacle space model, and compare the minimum spacing between the boom space model and each obstacle space model with a preset spacing threshold to obtain a comparison result; when it is determined that the comparison result indicates that there is an obstacle space model whose minimum spacing with the boom space model is less than or equal to the preset spacing, an anti-collision detection result is generated.

[0092] It should be noted that after generating the anti-collision detection result, the electronic device may display the anti-collision detection result; it may also generate an alarm message based on the anti-collision detection result and output the alarm message; further, it may also perform an anti-collision avoidance operation based on the anti-collision detection result, for example, controlling the boom to stop moving or performing obstacle avoidance control.

[0093] In some embodiments, determining a target curvature of the boom based on the boom curve includes:

[0094] Divide the boom curve to obtain multiple sub-boom curves;

[0095] Based on each sub-boom curve, determining the curvature of the sub-boom curve, the target curvature includes the curvatures of the multiple sub-boom curves;

[0096] Based on the target curvature, the parameter values ​​of the target modeling parameters are determined, including:

[0097] Based on the curvature of each sub-boom curve, determine the sub-modeling parameter value corresponding to the sub-boom curve;

[0098] The boom spatial model includes spatial models of multiple sub-booms, the multiple sub-booms correspond to multiple sub-boom curves, and the spatial model of each sub-boom is constructed based on the sub-modeling parameters corresponding to the sub-boom.

[0099] In this embodiment, the boom curve can be divided into multiple sub-boom curves, and the sub-modeling parameter values ​​corresponding to the curvature of each sub-boom curve can be determined respectively, so that in the modeling process, the corresponding sub-boom spatial model can be constructed based on each sub-modeling parameter value, thereby realizing segmented modeling of the boom. In the modeling process, the influence of the boom bending deformation on the computational redundancy of modeling is taken into account. Compared with the overall modeling, the computational redundancy of modeling can be reduced and the modeling efficiency can be improved.

[0100] The above-mentioned division of the boom curve to obtain multiple sub-boom curves may be performed by dividing the boom curve into multiple sub-boom curves according to a preset curve division rule. For example, the sub-boom curve may be divided irregularly according to key parts; or, the boom curve may be divided into the preset number of sub-boom curves at equal intervals according to a preset number of divisions.

[0101] The curvature of the sub-boom curve is determined based on each sub-boom curve, and the curvature of each sub-boom curve can be obtained by identifying based on image recognition technology.

[0102] Alternatively, in some embodiments, the above-mentioned determination of the curvature of the sub-boom curve based on each sub-boom curve may include: obtaining point cloud data of both end points of the target sub-boom curve, and obtaining point cloud data of the center point of the target sub-boom curve, the target sub-boom curve being any sub-boom curve among the multiple sub-boom curves; and calculating the curvature of the target sub-boom curve based on the point cloud data of both end points of the target sub-boom curve and the point cloud data of the center point. In this way, the calculation accuracy of the curvature of the target sub-boom curve can be further improved.

[0103] The curvature of the target sub-boom curve is calculated based on the point cloud data of the two end points and the point cloud data of the center point of the target sub-boom curve, which can be achieved by the following formula (1):

[0104]

[0105] In the above formula (1), v1 = [x i-1 -x i ,y i-1 -y i ,z i-1 -z i ];

[0106] v2=[x i+1 -x i ,y i+1 -y i ,z i+1 -z i ];

[0107] (xi-1 ,y i-1 ,z i-1 ) and x i+1 ,y i+1 ,z i+1 Respectively represent the point cloud data of the two endpoints;

[0108] (x i ,y i ,z i ) represents the point cloud data of the center point.

[0109] In some implementations, the boom curve is divided to obtain a plurality of sub-boom curves, including:

[0110] Determine a first quantity according to the length of the boom and a preset maximum error, wherein the preset maximum error is a maximum allowable error for the boom anti-collision calculation;

[0111] The boom curve is divided into equal intervals to obtain a first number of sub-boom curves.

[0112] In this implementation, the first number may be determined according to the length of the boom and the preset maximum error, and the boom curve may be equally spaced to be divided into the first number of sub-boom curves, so that the divided sub-boom curves are more appropriate.

[0113] The first number is determined based on the length of the boom and the preset maximum error, which can be achieved by the following formula (2):

[0114]

[0115] In the above formula (2), Ceiling(·) represents the rounding operation;

[0116] arm_length indicates the length of the arm;

[0117] res_max indicates the maximum allowable error in the boom anti-collision calculation.

[0118] The above res_max may be set according to actual needs, or may be determined according to experimental data, and is not limited here.

[0119] In the above embodiment, based on the curvature of each sub-boom curve, the sub-modeling parameter value corresponding to the sub-boom curve is determined. The correspondence between the curvature and the modeling parameter value may be pre-set in the electronic device, and the electronic device directly determines the modeling parameter value that has a corresponding relationship with the curvature of the sub-boom curve as the sub-modeling parameter value corresponding to the sub-boom curve. At this time, the determination of the sub-modeling parameter value may only depend on the curvature of the corresponding sub-boom curve.

[0120] In some embodiments, before determining the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve, the method further includes:

[0121] Determine multiple sampling points in the boom curve;

[0122] determining a curvature of at least one sampling point among a plurality of sampling points;

[0123] Based on the curvature of each sub-boom curve, the sub-modeling parameter value corresponding to the sub-boom curve is determined, including:

[0124] Based on the curvature of each sub-boom curve and the curvature of at least one sampling point, a sub-modeling parameter value corresponding to the sub-boom curve is determined.

[0125] In this embodiment, the curvature of at least one sampling point among multiple sampling points in the boom curve can also be determined, and the sub-modeling parameter value corresponding to the sub-boom curve can be determined by combining the curvature of each sub-boom curve and the curvature of at least one sampling point, so that the determined sub-modeling parameter value is more accurate.

[0126] The above-mentioned determination of multiple sampling points in the boom curve may be to select multiple sampling points in the boom curve according to a preset sampling point selection rule. For example, the points where the key parts are located may be determined as sampling points; or, the endpoints of the above-mentioned multiple sub-boom curves may be determined as sampling points, and so on.

[0127] The above-mentioned determination of the curvature of at least one sampling point among the multiple sampling points may be to identify the curvature of each sampling point by using the above-mentioned image recognition technology, wherein the at least one sampling point may be all or part of the multiple sampling points.

[0128] Alternatively, in some embodiments, the above-mentioned determination of the curvature of each sampling point among the multiple sampling points may include: obtaining point cloud data of a target sampling point, and obtaining point cloud data of two adjacent sampling points on the boom curve adjacent to the target sampling point, where the target sampling point is a sampling point among the multiple sampling points; and determining the curvature of the target sampling point based on the point cloud data of the target sampling point and the point cloud data of the two adjacent sampling points. In this way, the accuracy of the determined curvature of the sampling point can be improved.

[0129] The above-mentioned determination of the curvature of the target sampling point based on the point cloud data of the target sampling point and the point cloud data of two adjacent sampling points can be implemented by the following formula (3):

[0130]

[0131] In the above formula (3), v3 = [x j-1 -x j ,y j-1 -y j,z j-1 -z j ];

[0132] V4=[x j+1 -x j ,y j+1 -y j ,z j+1 -z j ];

[0133] (x j-1 ,y j-1 ,z j-1 ) and (x j+1 ,y j+1 ,z j+1 ) represent the point cloud data of two adjacent sampling points respectively;

[0134] (x j ,y j ,z j ) represents the point cloud data of the target sampling point.

[0135] The above-mentioned determination of the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve and the curvature of at least one sampling point may be to determine the average curvature of the at least one sampling point, and determine the sub-modeling parameter value according to the correspondence between the pre-configured curvature of the sub-boom curve, the average curvature of the sampling points and the modeling parameter value.

[0136] In some embodiments, a plurality of sampling points are determined in the boom curve, including:

[0137] Determine a second quantity based on the length of the boom and a preset minimum error, where the preset minimum error is a minimum error allowed by the boom anti-collision calculation;

[0138] A second number of sampling points are selected at equal intervals on the boom curve.

[0139] In this implementation, the second number may be determined according to the length of the boom and the preset minimum error, and the second number of sampling points may be selected at equal intervals in the boom curve, so that the selected sampling points are more appropriate.

[0140] The second quantity is determined based on the length of the boom and the preset minimum error, which can be achieved by the following formula (4):

[0141]

[0142] In the above formula (4), res_min represents the maximum allowable error of the boom anti-collision calculation.

[0143] The above res_min is smaller than the above res_max, and res_min can be set according to actual needs, or can also be determined according to experimental data, which is not limited here.

[0144] In some embodiments, determining a sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve and the curvatures of a plurality of sampling points includes:

[0145] Determine a maximum curvature and a minimum curvature among the curvatures of each sampling point among the plurality of sampling points;

[0146] Based on the curvature, maximum curvature and minimum curvature of each sub-boom curve, the sub-modeling parameter value corresponding to the sub-boom curve is determined.

[0147] In this embodiment, the sub-modeling parameter value corresponding to the sub-boom curve can be determined based on the curvature, maximum curvature and minimum curvature of each sub-boom curve, so as to consider the influence of the extreme curvature at each position in the sub-boom curve on the constructed boom space model, so that the constructed boom space model is more accurate.

[0148] The sub-modeling parameter values ​​corresponding to the sub-boom curves are determined based on the curvature, maximum curvature and minimum curvature of each sub-boom curve. The sub-modeling parameter values ​​corresponding to the curvature, maximum curvature and minimum curvature of each sub-boom curve can be determined through a pre-configured corresponding relationship.

[0149] In some embodiments, the number of sub-arm curves is a first number, and the number of sampling points is a second number,

[0150] Based on the curvature, maximum curvature and minimum curvature of each sub-boom curve, the sub-modeling parameter values ​​corresponding to the sub-boom curve are determined, including:

[0151] Based on the curvature of each sub-boom curve and the maximum curvature, the minimum curvature, the preset minimum error, and the preset maximum error, the sub-modeling parameter value corresponding to the sub-boom curve is calculated.

[0152] In this embodiment, the sub-modeling parameter value corresponding to the sub-boom curve can be calculated based on the curvature of each sub-boom curve as well as the maximum curvature, minimum curvature, preset minimum error, and preset maximum error, so that the calculated sub-modeling parameter value is more accurate, further improving the accuracy of the constructed boom space model.

[0153] The sub-modeling parameter values ​​corresponding to the sub-boom curves are calculated based on the curvatures of the sub-boom curves and the maximum curvature, the minimum curvature, the preset minimum error, and the preset maximum error. This can be achieved by the following formula (5):

[0154]

[0155] In the above formula (5), res_sub i represents the sub-modeling parameter value corresponding to the i-th sub-boom curve;

[0156] cur_sub i represents the curvature of the curve of the i-th sub-arm;

[0157] cur_min represents the maximum curvature among the curvatures of at least one sampling point mentioned above;

[0158] cur_max represents the maximum curvature among the curvatures of the at least one sampling point.

[0159] In some implementations, before step 106, the method further includes:

[0160] Acquire second point cloud data of obstacles around the boom;

[0161] generating an obstacle space model of the obstacle based on the second point cloud data;

[0162] Anti-collision detection is performed based on the boom space model to generate anti-collision detection results for the boom, including:

[0163] Anti-collision detection is performed on the boom space model and the obstacle space model to generate an anti-collision detection result of the boom.

[0164] In this embodiment, three-dimensional space modeling can be performed through the second point cloud data of the obstacles around the boom to obtain an obstacle space model, thereby making the constructed obstacle space model more accurate and further improving the detection accuracy of obstacles that pose a risk of collision with the boom.

[0165] The above-mentioned electronic device obtains the second point cloud data, which can be that the electronic device first constructs a bounding box of the boom based on the first point cloud data, eliminates the first point cloud data in the point cloud data detected by the point cloud acquisition device, and retains the center point position of the bounding box of the boom; then, based on the center point position, the bounding box size range is expanded to construct a bounding box of the boom obstacle perception range, and the point cloud data of obstacles around the boom are retained; finally, the point cloud data of obstacles whose spatial distance from the boom exceeds a preset distance are removed to obtain the second point cloud data, thereby avoiding the point cloud data of the boom from interfering with the construction of the obstacle space model.

[0166] It should be noted that the spatial modeling strategy adopted to construct the obstacle space model and the spatial modeling strategy adopted to construct the boom space model can be the same modeling strategy. For example, both the boom space model and the obstacle space model are constructed by adopting the octree subdivision spatial gridding modeling strategy in the spatial unit representation method; or, the spatial modeling strategies adopted to construct the boom space model and the obstacle space model can also be different. For example, the octree subdivision spatial gridding modeling strategy is adopted to construct the boom space model, while the constructive solid geometry method is adopted to construct the obstacle space model, which is not limited here.

[0167] In addition, the obstacle space model may be an obstacle space model including one or more obstacles within a preset detection range around the boom, that is, the obstacle space model generated by the electronic device includes at least one.

[0168] In some embodiments, the parameter value of the modeling parameter may include a model grid resolution (e.g., the resolution of an octree subdivision spatial gridding modeling strategy, etc.), and the model grid resolution is negatively correlated with the target curvature, that is, the larger the target curvature, the smaller the model grid resolution, thereby further reducing the impact of boom deformation on the accuracy of the constructed boom spatial model.

[0169] In order to facilitate the understanding of the anti-collision detection method of the boom of the present application, an application example of the anti-collision detection method of the boom is provided here for illustration, such as Figure 2 As shown, the details are as follows:

[0170] Based on the laser radar sensor (i.e. point cloud acquisition device) installed on the boom, the boom and the surrounding environment are scanned in real time to obtain point cloud information (i.e. point cloud data), and then synchronous data processing and algorithm analysis are performed to achieve boom body modeling, obstacle detection and anti-collision calculation. Specifically, it includes two parts: long flexible boom body modeling, obstacle detection and anti-collision calculation.

[0171] 1. Spatial modeling of the long flexible boom

[0172] Due to its own weight and load, the long flexible boom mechanical equipment will produce a certain degree of deflection deformation during the actual operation. In order to consider the influence of the boom deformation on the error of anti-collision calculation during the operation, the flexible boom body is constructed through the boom deformation data, and the anti-collision calculation is performed with the obstacles and environmental information around the boom, which can greatly improve the calculation accuracy of the long flexible boom. The specific process is as follows:

[0173] Step 1.1: Based on the laser radar sensor installed at the starting end of the long flexible boom, the boom body is scanned in real time and point cloud data is obtained to obtain the real-time orientation of the boom's key positions (including the end and middle key positions). Optionally, the implementation method may include: (1) non-contact measurement: recognition and positioning of the boom's key position point cloud target based on feature extraction and analysis; (2) contact measurement: positioning based on the boom's key position identification point.

[0174] Step 1.2: Based on the real-time position of the boom key positions (including the end and middle key positions) obtained in step 1.1, the boom curve equation from the starting end to the end of the boom (i.e., the boom curve) is constructed by curve fitting to realize mathematical modeling of boom deformation.

[0175] Step 1.3: Based on the boom curve equation outputted in step 1.2, an octree subdivision spatial gridding modeling method is used to perform spatial modeling of the long flexible boom. In the octree subdivision spatial gridding modeling method, a uniform octree resolution (i.e., model grid resolution) can be used.

[0176] Taking into account that the degree of bending deformation at various positions of the boom as a whole deviates greatly during actual operation, a multi-scale octree spatial modeling method based on the change of boom deformation curvature is used here. For positions with smaller boom deformation curvature, that is, the boom deformation bending degree is lower, a larger octree resolution is used for boom spatial modeling. Conversely, for positions with larger boom deformation curvature, that is, the boom deformation bending degree is higher, a smaller octree resolution is used for boom spatial modeling.

[0177] Step 1.4, set the octree resolution range, res_min and res_max are the minimum and maximum values ​​of the octree resolution respectively. The curvature setting criteria of this range are: res_min is the minimum error allowed for the boom anti-collision calculation, and res_max is the maximum error allowed for the boom anti-collision calculation.

[0178] Step 1.5: Based on the boom curve equation output in step 1.2, select j sampling points at equal intervals. The number of sampling points is calculated as shown in formula (4).

[0179] Step 1.6: Calculate the curvature of the j sampling points in step 1.5 and obtain the curvature range cur_min and cur_max. The curvature calculation formula of each sampling point refers to formula (3).

[0180] Step 1.7, long flexible boom octree space modeling, using equal interval sampling segmented space modeling method, based on the boom curve equation output in step 1.2, the current boom is equally divided into N groups of sub-boom spaces (i.e., sub-boom curves). The calculation method of N is shown in formula (2).

[0181] Step 1.8: For the N groups of sub-boom spaces divided in step 1.7, calculate the curvature of each group of sub-booms. The calculation formula refers to formula (1) to calculate the curvature of the sub-boom space cur_sub i .

[0182] Step 1.9: Based on the calculated sub-arm spatial curvature cur_sub i , based on the octree resolution allocation principle in step 1.3, calculate the current subspace octree modeling resolution res_sub. The calculation formula refers to formula (5).

[0183] Step 1.10: Based on the above steps, complete the octree spatial modeling of the overall long flexible boom and generate the boom spatial model.

[0184] 2. Obstacle detection and anti-collision calculation

[0185] Based on the spatial modeling results of the long flexible boom output in the previous steps, anti-collision calculation is performed with the spatial obstacles around the boom, the anti-collision distance is output, and the boom is controlled to stop or bypass.

[0186] Step 2.1: Based on the arm deformation data and arm space modeling results in the previous steps, construct the arm body bounding box, remove the arm body point cloud data within the laser radar scanning range, and eliminate the interference of the arm body on obstacle detection.

[0187] Step 2.2: Based on the arm body bounding box constructed in step 2.1, retain the position of the center point of the bounding box, expand the size range of the bounding box, construct the arm obstacle perception range bounding box, retain the obstacle point cloud data around the arm, remove the obstacle data that is far away from the arm space, and improve the efficiency of collision avoidance calculation.

[0188] Step 2.3: Based on the obstacle point cloud data obtained in step 2.2, set the octree resolution and output the obstacle octree spatial modeling result (i.e., generate an obstacle spatial model).

[0189] Step 2.4: Based on the octree spatial modeling results of the overall long flexible boom and the obstacle octree spatial modeling results output in the above steps, perform anti-collision calculation, output the anti-collision distance and the obstacle position at the nearest point to the boom, and guide the boom control system to implement boom stopping and obstacle avoidance control.

[0190] The anti-collision detection method for the boom provided in the embodiment of the present application can be performed by the anti-collision detection device for the boom. In the embodiment of the present application, the anti-collision detection method for the boom performed by the anti-collision detection device for the boom is taken as an example to illustrate the anti-collision detection device for the boom provided in the embodiment of the present application.

[0191] Figure 3FIG. 1 is a schematic diagram showing the structure of the anti-collision detection device of the boom provided in an embodiment of the present application. Figure 3 As shown, the anti-collision detection device 300 of the arm of the present application includes:

[0192] The point cloud data acquisition module 301 is used to acquire first point cloud data of the boom;

[0193] A curve fitting module 302 is used to perform curve fitting on the first point cloud data to obtain a boom curve of the boom;

[0194] The curvature determination module 303 is used to determine the target curvature of the boom based on the boom curve;

[0195] A modeling parameter value determination module 304 is used to determine a target modeling parameter value based on the target curvature, where the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling;

[0196] A modeling module 305 is used to perform spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom curvature boom spatial model;

[0197] The anti-collision detection module 306 is used to perform anti-collision detection based on the boom space model and generate an anti-collision detection result of the boom. The anti-collision detection result is used to indicate obstacles that have a risk of collision with the boom.

[0198] In some implementations, the modeling parameter value determination module 304 is specifically configured to:

[0199] Divide the boom curve to obtain multiple sub-boom curves;

[0200] Based on each sub-boom curve, determining the curvature of the sub-boom curve, the target curvature includes the curvatures of the multiple sub-boom curves;

[0201] Based on the curvature of each sub-boom curve, determine the sub-modeling parameter value corresponding to the sub-boom curve;

[0202] The boom spatial model includes spatial models of multiple sub-booms, the multiple sub-booms correspond to multiple sub-boom curves, and the spatial model of each sub-boom is constructed based on the sub-modeling parameters corresponding to the sub-boom.

[0203] In some implementations, the modeling parameter value determination module 304 is specifically configured to:

[0204] Determine a first quantity according to the length of the boom and a preset maximum error, wherein the preset maximum error is a maximum allowable error for the boom anti-collision calculation;

[0205] The boom curve is divided into equal intervals to obtain a first number of sub-boom curves.

[0206] In some implementations, the modeling parameter value determination module 304 is further specifically configured to:

[0207] Determine multiple sampling points in the boom curve;

[0208] determining a curvature of at least one sampling point among a plurality of sampling points;

[0209] Based on the curvature of each sub-boom curve and the curvature of at least one sampling point, a sub-modeling parameter value corresponding to the sub-boom curve is determined.

[0210] In some implementations, the modeling parameter value determination module 304 is specifically configured to:

[0211] Determine a second quantity based on the length of the boom and a preset minimum error, where the preset minimum error is a minimum error allowed by the boom anti-collision calculation;

[0212] A second number of sampling points are selected at equal intervals on the boom curve.

[0213] In some implementations, the modeling parameter value determination module 304 is specifically configured to:

[0214] Determine a maximum curvature and a minimum curvature among the curvatures of each sampling point among the plurality of sampling points;

[0215] Based on the curvature, maximum curvature and minimum curvature of each sub-boom curve, the sub-modeling parameter value corresponding to the sub-boom curve is determined.

[0216] In some embodiments, the number of sub-arm curves is a first number, and the number of sampling points is a second number,

[0217] The modeling parameter value determination module 304 is specifically used for:

[0218] Based on the curvature of each sub-boom curve and the maximum curvature, the minimum curvature, the preset minimum error, and the preset maximum error, the sub-modeling parameter value corresponding to the sub-boom curve is calculated.

[0219] In some implementations, the point cloud data acquisition module 301 is further used to:

[0220] Acquire second point cloud data of obstacles around the boom;

[0221] The modeling module 305 is further used for:

[0222] generating an obstacle space model of the obstacle based on the second point cloud data;

[0223] The anti-collision detection module 306 is specifically used for:

[0224] Anti-collision detection is performed on the boom space model and the obstacle space model to generate an anti-collision detection result of the boom.

[0225] In some embodiments, the parameter value of the modeling parameter includes a model grid resolution, and the model grid resolution is negatively correlated with the target curvature.

[0226] The anti-collision detection device for the arm provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0227] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0228] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0229] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0230] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, memory 402 may be inside or outside of an integrated gateway disaster recovery device. In a specific embodiment, memory 402 is a non-volatile solid-state memory.

[0231] In some embodiments, the memory 402 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0232] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the arm anti-collision detection methods in the above embodiments.

[0233] In one example, the electronic device may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0234] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0235] Bus 410 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0236] The electronic device can execute the anti-collision detection method of the arm in the embodiment of the present application, thereby realizing the combination Figures 1 to 3 A method and device for anti-collision detection of a boom are described.

[0237] In addition, in combination with the anti-collision detection method for the boom in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the anti-collision detection method for the boom in the above embodiment is implemented.

[0238] In combination with the anti-collision detection method for the boom in the above-mentioned embodiment, an embodiment of the present application may provide a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device enables the anti-collision detection method for the boom in the above-mentioned embodiment.

[0239] In combination with the electronic device in the above embodiment, an embodiment of the present application may provide an operating machine, which includes a boom, a point cloud acquisition device and the electronic device in the above embodiment.

[0240] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0241] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0242] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0243] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0244] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for detecting an anti-collision of an arm, characterized in that: include: Acquire first point cloud data of the boom; Performing curve fitting on the first point cloud data to obtain a boom curve of the boom; Based on the boom curve, determining a target curvature of the boom; Based on the target curvature, determining a target modeling parameter value, wherein the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling; Performing spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom spatial model of the boom; Anti-collision detection is performed based on the boom space model to generate an anti-collision detection result of the boom, wherein the anti-collision detection result is used to indicate obstacles that have a risk of collision with the boom.

2. The method according to claim 1, characterized in that: The step of determining a target curvature of the boom based on the boom curve comprises: Dividing the boom curve to obtain a plurality of sub-boom curves; Based on each of the sub-boom curves, determining the curvature of the sub-boom curve, wherein the target curvature includes the curvatures of the multiple sub-boom curves; Determining the parameter value of the target modeling parameter based on the target curvature includes: Based on the curvature of each of the sub-boom curves, determining a sub-modeling parameter value corresponding to the sub-boom curve; The boom spatial model includes spatial models of multiple sub-booms, the multiple sub-booms correspond to the multiple sub-boom curves, and the spatial model of each sub-boom is constructed based on the sub-modeling parameters corresponding to the sub-boom.

3. The method according to claim 2, characterized in that The step of dividing the boom curve to obtain the plurality of sub-boom curves includes: Determine a first quantity according to the length of the boom and a preset maximum error, wherein the preset maximum error is a maximum error allowed by the boom anti-collision calculation; The boom curve is divided into equal intervals to obtain the first number of sub-boom curves.

4. The method according to claim 2, characterized in that: Before determining the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve, the method further includes: determining a plurality of sampling points in the boom curve; determining a curvature of at least one sampling point among the plurality of sampling points; Determining the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve includes: Based on the curvature of each of the sub-boom curves and the curvature of the at least one sampling point, a sub-modeling parameter value corresponding to the sub-boom curve is determined.

5. The method according to claim 4, characterized in that A plurality of sampling points are determined in the boom curve, including: Determine a second quantity based on the length of the boom and a preset minimum error, wherein the preset minimum error is a minimum error allowed by the boom anti-collision calculation; A second number of sampling points are selected at equal intervals in the boom curve.

6. The method according to claim 4, characterized in that The determining of the sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve and the curvature of the plurality of sampling points comprises: Determine a maximum curvature and a minimum curvature among the curvatures of each of the plurality of sampling points; Based on the curvature of each of the sub-boom curves and the maximum curvature and the minimum curvature, a sub-modeling parameter value corresponding to the sub-boom curve is determined.

7. The method according to claim 6, characterized in that The number of the sub-arm curves is a first number, and the number of the sampling points is a second number, Determining a sub-modeling parameter value corresponding to the sub-boom curve based on the curvature of each sub-boom curve and the maximum curvature and the minimum curvature includes: Based on the curvature of each of the sub-boom curves and the maximum curvature, the minimum curvature, the preset minimum error, and the preset maximum error, a sub-modeling parameter value corresponding to the sub-boom curve is calculated.

8. The method according to claim 1, characterized in that: Before performing anti-collision detection based on the boom space model and generating the anti-collision detection result of the boom, the method further includes: Acquire second point cloud data of obstacles around the boom; generating an obstacle space model of the obstacle based on the second point cloud data; The performing anti-collision detection based on the boom space model to generate the anti-collision detection result of the boom includes: Anti-collision detection is performed on the boom space model and the obstacle space model to generate an anti-collision detection result of the boom.

9. The method according to claim 1, characterized in that: The parameter value of the modeling parameter includes a model grid resolution, and the model grid resolution is negatively correlated with the target curvature.

10. An anti-collision detection device for an arm, characterized in that: include: A point cloud data acquisition module, used to acquire first point cloud data of the boom; A curve fitting module, used for performing curve fitting on the first point cloud data to obtain a boom curve of the boom; A curvature determination module, used for determining a target curvature of the boom based on the boom curve; A modeling parameter value determination module, used to determine a target modeling parameter value based on the target curvature, wherein the target modeling parameter value is a parameter value of a modeling parameter used for spatial modeling; A modeling module, configured to perform spatial modeling on the first point cloud data based on the target modeling parameter value to obtain the boom curvature boom spatial model; An anti-collision detection module is used to perform anti-collision detection based on the boom space model to generate an anti-collision detection result of the boom, wherein the anti-collision detection result is used to indicate obstacles that have a risk of collision with the boom.

11. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the anti-collision detection method for the boom as described in any one of claims 1-9 is implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the anti-collision detection method for the boom as described in any one of claims 1 to 9 is implemented.

13. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the anti-collision detection method for the boom as described in any one of claims 1-9.

14. A working machine, characterized in that: It comprises an arm, a point cloud acquisition device and the electronic device as claimed in claim 11.

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