A method and device for positioning a load lifting attachment and a working machine

Abstract

The application discloses a kind of sling positioning detection method, device and engineering machinery, the method includes: when sling drops to preset height, utilize the point cloud data of scanning area collected by the 3D radar equipment, and effective point cloud data of car plate is extracted from the point cloud data collected;From the effective point cloud data of car plate, the point cloud data of car plate width mutation is identified, the point cloud data of car plate width mutation refers to: in the effective point cloud data of car plate, the point cloud data at the position where the difference between the width of car plate and other car plate width exceeds preset value;Based on the point cloud data of car plate width mutation, the point cloud data of car plate work site is extracted from the effective point cloud data of car plate;According to the point cloud data of car plate work site, the pose information of sling relative to car plate is calculated and determined.The application is filtered by the point cloud data collected by the 3D radar equipment installed on both sides of sling, and the like calculation process, so as to obtain car plate pose information to realize accurate lifting operation.

Description

Technical Field

[0001] This application relates to the field of engineering machinery, specifically to a method, device, and engineering machinery for positioning and detecting lifting devices. Background Technology

[0002] In recent years, with the continuous development of port transportation business, yard cranes and other equipment have become indispensable engineering machinery devices in the process of cargo loading and unloading. When using railways for cargo transportation, due to the wide variety of goods being transported, the types of train cars selected for transportation also vary.

[0003] With the increasing variety of train car platforms, the deviation of the train operation position is relatively large, or when facing a hollowed-out car platform, it is difficult to accurately identify the car platform and achieve precise positioning of the lifting equipment, which makes the lifting operation prone to errors and difficult to achieve. Summary of the Invention

[0004] In view of this, the embodiments of this application are committed to providing a method, device and engineering machinery for positioning and detecting a lifting device. The method uses 3D radar equipment set on both sides of the lifting device to collect point cloud data of the scanning area. By filtering out point cloud data with abrupt changes in the width of the vehicle board in the point cloud data, the point cloud data of the working position of the vehicle board is obtained. This method of identifying the working position of the vehicle board by the abrupt change in the width of the vehicle board can also accurately identify the working position of the vehicle board even for hollowed-out vehicle boards. Based on this, the position and posture information of the vehicle board and the lifting device are further obtained, realizing the accurate positioning of the lifting device and improving the work efficiency of lifting operations.

[0005] According to a first aspect of the embodiments of this application, a method for positioning and detecting a lifting device is provided, wherein 3D radar devices are respectively installed at both ends of the lifting device, and the method specifically includes:

[0006] When the lifting device is lowered to the preset height, the 3D radar device is used to collect point cloud data of the scanning area, and the effective point cloud data of the vehicle board is extracted from the collected point cloud data.

[0007] From the effective point cloud data of the vehicle panel, identify the point cloud data with abrupt changes in vehicle panel width. The point cloud data with abrupt changes in vehicle panel width refers to the point cloud data at the position where the difference between the width of the vehicle panel and the width of other vehicle panels exceeds a preset value in the effective point cloud data of the vehicle panel.

[0008] Based on the point cloud data of the abrupt change in the width of the vehicle board, the working point cloud data of the vehicle board is extracted from the effective point cloud data of the vehicle board.

[0009] Based on the cloud data of the working position on the vehicle platform, the position and orientation information of the spreader relative to the vehicle platform is calculated and determined.

[0010] In one embodiment, the lifting device positioning detection method further includes: downsampling and filtering the point cloud data of the scanning area collected by the 3D radar device.

[0011] In one embodiment, extracting effective point cloud data of the vehicle body from the collected point cloud data includes:

[0012] The ground point cloud data is filtered out from the point cloud data to obtain the effective point cloud data of the vehicle panel.

[0013] In one embodiment, calculating and determining the distance information between the spreader and the truck bed based on the cloud data of the truck bed working point includes:

[0014] Based on the point cloud data of the vehicle platform working position, obtain the vehicle platform attitude information; and perform reverse coordinate rotation on the point cloud data of the vehicle platform working position to obtain the point cloud data of the spreader.

[0015] Based on the point cloud data of the working points on the vehicle platform and the point cloud data of the spreader, the distance information between the spreader and the vehicle platform is calculated and determined.

[0016] In one embodiment, obtaining the vehicle platform attitude information based on the vehicle platform work point cloud data includes:

[0017] Depending on the type of 3D radar equipment used to collect point cloud data, the point cloud data of the vehicle platform operation points is divided into two groups.

[0018] The two sets of point cloud data are sorted separately to obtain two sets of ordered point cloud data;

[0019] The vehicle body attitude information is calculated based on two sets of ordered point cloud data.

[0020] In one embodiment, the step of calculating and obtaining vehicle attitude information based on two sets of ordered point cloud data includes:

[0021] Based on the coordinate difference between two sets of ordered point cloud data, the yaw angle, pitch angle and roll angle of the vehicle body are calculated to obtain the vehicle body attitude information.

[0022] or,

[0023] After performing planar fitting on the vehicle body based on two sets of ordered point cloud data, the yaw angle, pitch angle and roll angle of the vehicle body attitude are calculated to obtain the vehicle body attitude information.

[0024] In one embodiment, the step of performing a reverse coordinate rotation on the point cloud data of the vehicle platform to obtain the point cloud data of the spreader includes:

[0025] Based on the point coordinates in the point cloud data of the vehicle board working position, the point coordinates are rotated around different rotation axes in reverse coordinates to obtain the reverse point cloud data of the vehicle board.

[0026] The point cloud data of the spreader is obtained based on the reverse point cloud data of the vehicle platform.

[0027] In one embodiment, the spreader positioning and detection method further includes: determining the type of vehicle platform based on the effective point cloud data of the vehicle platform.

[0028] In one embodiment, determining the train type based on the effective point cloud data of the vehicle platform includes:

[0029] Separate the point cloud data above the vehicle panel, and identify the attribute information of the target object corresponding to the separated point cloud data. The attribute information includes at least one of the following: number, size, and shape.

[0030] If the separated point cloud data is determined to be container point cloud data and carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container high-side train.

[0031] If the separated point cloud data is determined to be the point cloud data of a container based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container flatbed train.

[0032] If the separated point cloud data is determined to be carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a high-side train;

[0033] If the point cloud data is not separated, then the train type is a flatbed train.

[0034] According to a second aspect of this application, a lifting device for positioning detection is provided, the lifting device for positioning detection comprising:

[0035] The scanning unit is a 3D radar device configured on both sides of the lifting device;

[0036] A control unit is used to implement the lifting device positioning and detection method described in any of the above embodiments.

[0037] According to a third aspect of this application, an engineering machinery is provided, the engineering machinery including the above-mentioned lifting tool positioning and detection device.

[0038] The lifting device, lifting equipment, and lifting machinery provided in this application are equipped with 3D radar devices on both sides of the lifting device. The point cloud data obtained by scanning with these devices is preprocessed to extract effective point cloud data of the vehicle platform. Then, abrupt changes in the width of the vehicle platform are removed from the effective data to obtain the working point point cloud data of the vehicle platform. Finally, the pose information of the vehicle platform and the lifting device are obtained based on the working point point cloud data. Based on the above method of identifying the working position of the vehicle platform through abrupt changes in width, this application can accurately identify the working position of a hollowed-out vehicle platform and further obtain the pose information of the vehicle platform and the lifting device, achieving accurate positioning of the lifting device and improving the efficiency of lifting operations. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating a lifting device positioning and detection method provided in an embodiment of this application.

[0041] Figure 2 This is a flowchart illustrating a lifting device positioning and detection method provided in another embodiment of this application.

[0042] Figure 3 This is a schematic diagram of a lifting device for positioning and detecting a lifting device according to another embodiment of this application.

[0043] Figure 4 This is a schematic diagram of the structure of an exemplary engineering machine provided for another embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] Exemplary methods

[0046] This application proposes a method for positioning and detecting lifting devices. This method is applied to the positioning and detection technology of lifting devices in the field of engineering machinery. In this application embodiment, 3D radar devices are installed on both sides of the lifting device. Figure 1This is a flowchart illustrating a lifting device positioning and detection method provided in an embodiment of this application, as shown below. Figure 1 As shown, in this embodiment, the proposed lifting device positioning detection method includes:

[0047] S101. When the lifting device descends to the preset height, the 3D radar device is used to collect point cloud data of the scanning area, and the effective point cloud data of the vehicle board is extracted from the collected point cloud data.

[0048] In hoisting operations, a yard crane, as a type of mechanical equipment used for lifting and moving heavy objects, generally consists of a bridge structure and a spreading system. The spreading system, as part of the yard crane, plays a crucial role in lifting and moving heavy objects. The spreading system can be electrically or hydraulically driven to lift and laterally move loads to meet the lifting needs of different situations. In this embodiment, to better view the cargo below the spreading system, 3D radar equipment is installed on both sides of the spreading system.

[0049] Among them, 3D radar equipment typically uses lasers to sense and measure objects. It sends out signals and measures the distance, position, and speed of the target object by receiving reflected signals. Due to the longer wavelength, larger receiving beam angle, and higher scanning speed of radar equipment, it can usually obtain data over a wider range and performs better in large-area environments.

[0050] In this embodiment, when the lifting device descends to a preset height, 3D radar devices installed on both sides of the lifting device scan the scanning area to obtain a point cloud dataset of the scanned area. Point cloud data refers to a collection of discrete points acquired by a 3D sensing device, typically composed of a large number of points. Each point contains 3D coordinate information (usually (x, y, z)) and possible other attributes such as reflection intensity, color, and normal direction. These points can form a point cloud model representing an object, showcasing its shape, surface details, and other information. This is an important data format for 3D modeling, environmental perception, and scene reconstruction. Furthermore, the aforementioned preset height can be determined based on actual work requirements, with minimal error.

[0051] Optionally, in this embodiment, to enrich the point cloud data, the 3D radar devices installed on both sides of the hoist will collect data on the scanning area in real time. Furthermore, to determine the valid data in the point cloud data, preprocessing is required. In this embodiment, the point cloud data of the scanning area collected by the 3D radar device is downsampled and filtered. In point cloud data processing, downsampling typically reduces the complexity of the point cloud data while preserving key geometric features by reducing the number of points and adjusting the point density. Filtering, as a signal processing technique, is used to remove or change certain frequency components or noise in a signal to extract the required signal information or improve signal quality.

[0052] After preprocessing the point cloud data of the scanning area collected by the 3D radar device, the effective point cloud data of the vehicle panel is extracted from the preprocessed point cloud data. Optionally, in this embodiment, when extracting the effective point cloud data of the vehicle panel, the point cloud data related to the ground in the preprocessed point cloud data is mainly filtered out, so that the filtered point cloud data only contains the effective point cloud data of the vehicle panel plane.

[0053] S102. Identify point cloud data with abrupt changes in width of the vehicle panel from the effective point cloud data of the vehicle panel.

[0054] The point cloud data of abrupt changes in the width of the car body refers to the point cloud data at the position where the difference between the width of the car body and the width of other car bodies exceeds a preset value in the effective point cloud data of the car body. For example, this application takes the bottom car body of a train car as an example. In the effective point cloud data of the car body collected by the 3D radar equipment, the value of the width of the car body can be calculated based on a continuous segment of point cloud data. However, the width at the connection between the two car bodies in the collected point cloud data is different from the width value when the car body was collected. Taking two car bodies as an example, in the effective point cloud data of the car body, there will be a segment of data with different widths in the two segments of car body data with the same width. This segment of data is the abrupt change in the width of the car body as described in this application.

[0055] Specifically, the process of identifying abrupt changes in the width of the train car body within the effective point cloud data can be achieved by calculating the difference in width between different car body segments. When the difference between the width value of the car body in the point cloud data and the width values ​​of other car body segments exceeds a preset value, the point cloud data at the location of that width value is considered the abrupt change in width. By identifying these abrupt changes in width, even when dealing with openwork sections in train car bodies, the differences in the gaps between the car bodies and their connections can be identified based on the abrupt width changes.

[0056] S103. Based on the point cloud data of the sudden change in the width of the vehicle board, extract the working point cloud data of the vehicle board from the effective point cloud data of the vehicle board.

[0057] Based on S102, and using the identified point cloud data of abrupt changes in the width of the car body, the abrupt changes in the width of the car body can be filtered out from the effective point cloud data of the car body collected in step S101. The remaining data contains only the point cloud data of the car body. After filtering out the abrupt changes, the working point cloud data of the car body is obtained, which contains only the point cloud data of the car body. At this time, the point cloud data contained in the working point data of the car body is used to characterize the effective working point of the car body. In other words, when the lifting device performs the lifting operation, it can obtain the specific working point from the working point cloud data of the car body to prevent the problem of inaccurate lifting position during the operation of the lifting device. Especially when facing the hollowed-out parts of the train car body, if the point cloud data of the gaps (abrupt changes) between the car bodies are not removed, it is easy to mistake the gaps for the hollowed-out parts of the car body, resulting in errors in the lifting operation.

[0058] S104. Calculate and determine the position and orientation information of the spreader relative to the vehicle platform based on the cloud data of the working position on the vehicle platform.

[0059] After acquiring the point cloud data of the work position on the vehicle platform, the lifting range of the spreader was determined, providing a data foundation for further accurate calculation of the pose information of the spreader and the vehicle platform. Based on the point cloud data of the work position on the vehicle platform, the pose information of the spreader relative to the vehicle platform was determined through operations such as calculation, extraction, transformation, and rotation of the point cloud data. The specific steps can be found in [reference needed]. Figure 2 A flowchart.

[0060] like Figure 2 As shown, in this embodiment, calculating and determining the position and orientation information of the spreader relative to the vehicle platform based on the cloud data of the working position on the vehicle platform includes:

[0061] S1041. Obtain vehicle board posture information based on the cloud data of the vehicle board working point.

[0062] In this embodiment, depending on the 3D radar device used to collect point cloud data, the point cloud data of the vehicle platform is divided into two groups, and the two groups of point cloud data are sorted to obtain two groups of ordered point cloud data. Then, based on the two groups of ordered point cloud data, the vehicle platform attitude information is calculated and obtained.

[0063] Generally, train wagons are quite long, and the wagons parked in the train yard may not all be neatly arranged. Therefore, obtaining wagon attitude information is particularly important during the lifting operation. Optionally, commonly used methods for representing attitude include quaternion equations and Euler angles. In this application, Euler angles are used to obtain wagon attitude information. Euler angles are a common attitude representation method, which can describe the orientation and attitude of an object in space through yaw angle, pitch angle, and roll angle. Among them, the yaw angle describes the angle of rotation of the object relative to a reference axis (usually a vertically upward axis) about the vertical direction, the pitch angle describes the angle of rotation of the object about the lateral axis (usually a lateral axis), and the roll angle is used to describe the angle of rotation of the object about the longitudinal axis (usually a longitudinal axis).

[0064] Specifically, when calculating and obtaining vehicle attitude information based on two sets of ordered point cloud data as described above, the yaw angle, pitch angle, and roll angle that characterize the vehicle attitude can be obtained based on the difference in outer boundary coordinates between the two sets of ordered point cloud data. Optionally, when the point cloud data characterizing the vehicle attitude is obvious in the ordered point cloud data, the yaw angle, pitch angle, and roll angle of the vehicle attitude can be calculated after performing plane fitting on the vehicle attitude based on the two sets of ordered point cloud data. Plane fitting refers to finding the best planar model in the point cloud data to describe the planar structure in the point cloud. The fitted plane can describe the flat area in the point cloud and is used to analyze the geometric structure of the data, extract features, or perform subsequent surface reconstruction.

[0065] In addition, after obtaining the attitude information of the vehicle platform, the attitude information of the spreader can be deduced from the yaw angle, pitch angle and roll angle of the vehicle platform, that is, the angle values ​​of the yaw angle, pitch angle and roll angle of the spreader.

[0066] S1042. Rotate the point cloud data of the vehicle platform working point in reverse coordinates to obtain the point cloud data of the lifting device.

[0067] Based on the point coordinates in the point cloud data of the vehicle platform working position, the point coordinates are rotated in the opposite direction around different coordinate rotation axes using the coordinate axis of the point coordinates as the rotation axis to obtain the reverse point cloud data of the vehicle platform; based on the reverse point cloud data of the vehicle platform, the point cloud data of the spreader is obtained.

[0068] In the acquisition of the reverse point cloud data of the vehicle board in this embodiment, the point coordinates are rotated in reverse around different coordinate axes. The point coordinates can be any point in the vehicle board working point cloud data. Taking point [Xi,Yi,Zi] as an example, the specific process of rotating the point coordinates in reverse around the z, x, and y axes in sequence is as follows:

[0069] If a point [Xi, Yi, Zi] in the point cloud data is rotated in the opposite direction around the Z-axis, then [X... iz ,Y iz Ziz The calculation process for the corresponding value is as follows:

[0070]

[0071] Point cloud data midpoint [X iz ,Y iz Z iz Rotate counterclockwise around the X-axis, then [X] ix ,Y ix Z ix The calculation process for the corresponding value is as follows:

[0072]

[0073] Point cloud data midpoint [X ix ,Y ix Z ix Rotate in the opposite direction around the Y-axis, then [X′] i ,Y′ i ,Z′ i The calculation process for the corresponding value is as follows:

[0074]

[0075] After the above-mentioned reverse coordinate rotation, the reverse point cloud data of the vehicle platform is obtained, and the point cloud data of the spreader is further deduced.

[0076] S1043. Based on the point cloud data of the working points on the vehicle platform and the point cloud data of the spreader, calculate and determine the position and pose information of the spreader relative to the vehicle platform.

[0077] Based on the point cloud data of the aforementioned workstation on the vehicle platform and the point cloud data of the spreader obtained through reverse coordinate rotation, the difference between the two sets of point cloud data is calculated to determine the pose information of the spreader relative to the vehicle platform. Combined with the calculation results in step S1041, and knowing the attitude information of the vehicle platform and the spreader, the pose information of the spreader relative to the vehicle platform is further obtained. Optionally, during the lifting operation, this application transmits the aforementioned pose information of the spreader and the vehicle platform to the control terminal so that the yard crane can control the gripping and releasing position and attitude of the spreader during the lifting operation.

[0078] In addition, the exemplary method proposed in this application can also determine the train type based on the effective point cloud data of the vehicle platform collected by the 3D device on the spreader. The train type refers to the train that needs to be lifted, which can be a flatbed train that only contains a bottom flatbed or a high-side train with a plate structure around it. High-side trains and flatbed trains are further divided into two types: those containing containers and those not containing containers.

[0079] Specifically, when determining the type of train, it is necessary to perform clustering and separation operations on the point cloud data above the train platform, separate the point cloud data above the train platform, and identify the attribute information of the target object corresponding to the separated point cloud data. The attribute information includes at least one of the following: number, size, and shape.

[0080] If the separated point cloud data is determined to be container point cloud data and carriage point cloud data based on the attribute information of the target objects corresponding to the separated point cloud data, then the train type is a container high-side train. The determination of container and carriage point cloud data is mainly achieved by performing clustering operations on the point cloud data and then judging the attributes of the separated target objects. If the calculated size information matches the commonly used dimensions of containers, then the point cloud data of that target object is considered to be container point cloud data. After determining the container point cloud data, if the size and length information of the target objects that can be separated around the container matches the size and length of the carriage's surrounding surface, then the point cloud data of that target object is considered to be carriage point cloud data. Based on the above analysis, when the train type is a container high-side train, the attribute information of the target objects can also increase the number and size of containers to be lifted for the lifting personnel, facilitating the selection of appropriate lifting processes.

[0081] If the separated point cloud data is determined to be container point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container flatbed train; if the separated point cloud data is determined to be carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a high-side train; if no point cloud data is separated, then the train type is a flatbed train.

[0082] The train type is determined based on the collected effective point cloud data of the train platform. If an effective point cloud dataset is detected above the point cloud data representing the train platform, the train type is a high-side train. Conversely, if no effective point cloud data is detected above the point cloud data of the train platform, the train type is a flatbed train. The detection of the effective point cloud dataset can be achieved by performing operations such as separation and clustering on the point cloud data above the effective point cloud data of the train platform. If the target object can be separated, it indicates that the train belongs to the high-side train type.

[0083] In this embodiment, determining the train type allows operators to determine the lifting operation process in advance based on the train type, ensuring the smooth execution of the lifting operation.

[0084] To make the above-mentioned lifting gear positioning detection method more specific, an exemplary case is used to describe the process of the lifting gear positioning detection method proposed in this application, for example:

[0085] Step 1: Install one 3D radar device on each side of the crane spreader. After the spreader is lowered to the preset height, use the 3D radar devices to collect point cloud data and perform downsampling and filtering of the data.

[0086] Step 2: Extract the effective point cloud data A1{(x1,y1,z1), (x2,y2,z2)…(x1,y1,z1), (x2,y2,z2)…(x2,y1,z2) from the vehicle panel collected by the 3D radar devices at both ends. n ,y n ,z n A1{(a1,b1,c1), (a2,b2,c2)…(a n ,b n ,c n Based on the abrupt changes in the width of the vehicle platform, the cloud data of the working positions on the vehicle platform, B1{(x1,y1,z1), (x2,y2,z2)…(x n ,y n ,z n B2{(a1,b1,c1), (a2,b2,c2)…(a n ,b n ,c n )};

[0087] Step 3: Sort the point cloud data of the vehicle platform operation points to obtain two sets of ordered point cloud datasets C1{(x1,y1,z1), (x2,y2,z2)…(x n ,y n ,z n C2{(a1,b1,c1), (a2,b2,c2)…(a n ,b n ,c n )};

[0088] Step 4: Based on the coordinate difference between the outer boundaries of point sets C1 and C2, calculate the yaw angle, pitch angle, and roll angle of the target vehicle body; or, if the features on C1 or C2 are significant, perform plane fitting to calculate the azimuth angle, pitch angle, and roll angle of the target vehicle body; then deduce the azimuth angle, pitch angle, and roll angle of the spreader.

[0089] Step 5: Based on the target's pose, determine the coordinates [X] of any point in point sets C1 and C2. i ,Y i Z i The rotation is performed sequentially along the z, x, and y axes in reverse order. The specific calculation process is as follows:

[0090] Point of convergence [X] i ,Y i Z iRotate the [X] axis counterclockwise around the Z-axis (Yaw), then [X] iz ,Y iz Z iz The corresponding value:

[0091]

[0092] Point of convergence [X] iz ,Y iz Z iz Rotate pitch in the opposite direction around the X-axis, then [X] ix ,Y ix Z ix The corresponding value:

[0093]

[0094] Point of convergence [X] ix ,Y ix Z ix Rotate Roll counterclockwise around the Y-axis, then [X′] i ,Y′ i ,Z′ i The corresponding value:

[0095]

[0096] Step 6: Finally, obtain point cloud datasets D1 and D2. Calculate the mean of the outer boundary values ​​of D1 and D2 to obtain the corresponding point cloud data of the spreader. Further, based on the point cloud data of the working point on the vehicle and the point cloud data of the spreader, calculate and determine the pose information of the spreader relative to the vehicle.

[0097] Exemplary device

[0098] This application also provides a lifting device for positioning and detecting lifting equipment, the simplified structural diagram of which is shown below. Figure 3 As shown, the lifting device for positioning and detection consists of a scanning unit and a control unit. The functions of each component are as follows:

[0099] The scanning unit, in this embodiment, mainly refers to the 3D radar equipment configured on both sides of the lifting device. The 3D radar equipment is a sensor used to measure and acquire three-dimensional environmental information. Unlike traditional two-dimensional radar, 3D radar can provide more detailed and comprehensive spatial information, making environmental perception and obstacle detection more accurate and reliable. The 3D radar equipment described in this application collects data from the scanning area to generate point cloud data of the scanning area, providing a data foundation for subsequent calculations.

[0100] The control unit is used to collect point cloud data of the scanning area using the 3D radar device when the spreader descends to a preset height, extract effective point cloud data of the vehicle platform from the collected point cloud data, further identify point cloud data with abrupt changes in vehicle platform width from the effective point cloud data of the vehicle platform, and extract the vehicle platform working point cloud data from the effective point cloud data of the vehicle platform based on the point cloud data with abrupt changes in vehicle platform width, and finally calculate and determine the position and pose information of the spreader relative to the vehicle platform using the vehicle platform working point cloud data.

[0101] Specifically, to improve the accuracy of the data collected by the lifting equipment, a preset descent height for the lifting equipment is set in advance according to the lifting requirements. In this embodiment, during the descent of the lifting equipment, the control unit controls the lifting equipment to stop descending after reaching the preset height, and further controls the 3D radar equipment to turn on in order to collect point cloud data of the scanning area of ​​the 3D radar equipment. In addition, the range accuracy of the scanning area can also be selected by the control unit according to the specific lifting scenario.

[0102] After acquiring the point cloud data of the scanning area, preprocessing is required to improve the effectiveness of the point cloud data. In this embodiment, the point cloud data of the scanning area acquired by the 3D radar device is downsampled and filtered. After preprocessing the point cloud data of the scanning area acquired by the 3D radar device, the effective point cloud data of the vehicle panel is extracted from the preprocessed point cloud data. Optionally, in extracting the effective point cloud data of the vehicle panel, this embodiment mainly filters out the point cloud data related to the ground from the preprocessed point cloud data, so that the filtered point cloud data only contains the effective point cloud data of the vehicle panel plane.

[0103] After acquiring the effective point cloud data of the vehicle panel, the control unit identifies the abrupt change in vehicle panel width based on the effective point cloud data. The abrupt change in vehicle panel width can be identified by calculating the difference in width between different segments of the vehicle panel. When the difference between the vehicle panel width value in the vehicle panel width point cloud data and the vehicle panel width value in other vehicle panel width point cloud data exceeds a preset value, then the point cloud data at the location of that segment of vehicle panel width value is the abrupt change in vehicle panel width.

[0104] Based on the identified abrupt changes in vehicle width, the abrupt changes in vehicle width are filtered out from the effective point cloud data of the vehicle, leaving only the point cloud data containing the vehicle. After filtering out the abrupt changes, the vehicle working point cloud data containing only the vehicle point cloud data is obtained. Furthermore, the pose information of the spreader relative to the vehicle is calculated and determined based on the vehicle working point cloud data.

[0105] Optionally, the control unit calculates Euler angles based on the vehicle panel working point cloud data to deduce the vehicle panel attitude information. By using different 3D radar devices to collect the data, the vehicle panel working point cloud data is sorted to obtain two sets of ordered point cloud data. Based on the difference in the outer boundary coordinates of the two sets of ordered point cloud data, the yaw angle, pitch angle, and roll angle that can characterize the vehicle panel attitude are obtained. Optionally, when the point cloud data that characterizes the vehicle panel is obvious in the ordered point cloud data, the vehicle panel can also be fitted to a plane based on the two sets of ordered point cloud data to calculate the yaw angle, pitch angle, and roll angle of the vehicle panel attitude, and finally obtain the vehicle panel attitude information.

[0106] To obtain the corresponding point cloud dataset of the spreader, based on the point cloud data of the working position on the vehicle platform, the coordinate axes of the point coordinates in the point cloud data of the working position on the vehicle platform can be used as rotation axes to rotate the point coordinates around different coordinate rotation axes in the opposite direction to obtain the reverse point cloud data of the vehicle platform. Based on the reverse point cloud data of the vehicle platform, the point cloud data of the spreader can be obtained. The process of obtaining the point cloud data can refer to the specific calculation process of the spreader positioning and detection method provided in the above embodiments of this application, which will not be elaborated here.

[0107] Based on the point cloud data of the aforementioned work position on the vehicle platform and the point cloud data of the spreader obtained by rotating in the reverse coordinates, the difference between the two sets of point cloud data can be calculated to determine the distance information of the spreader relative to the vehicle platform. Optionally, during the lifting operation, the position and posture information of the spreader and the vehicle platform will be transmitted to the control unit so that the yard crane can control the gripping position and posture of the spreader during the lifting operation.

[0108] In addition, in this embodiment, the control unit can also determine the train type based on the effective point cloud data of the vehicle platform collected by the 3D equipment on the spreader. The control unit discriminates the acquired effective point cloud data of the vehicle platform. When it detects that there is an effective point cloud dataset above the point cloud data representing the vehicle platform, it separates the point cloud data above the vehicle platform and identifies the attribute information of the target object corresponding to the separated point cloud data. The attribute information includes at least one of the following: number, size, and shape. If the separated point cloud data is determined to be container point cloud data and carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container high-side train. If the separated point cloud data is determined to be container point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container flatbed train. If the separated point cloud data is determined to be carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a high-side train. If no point cloud data is separated, then the train type is a flatbed train.

[0109] The lifting gear positioning and detection device provided in this embodiment belongs to the same concept as the lifting gear positioning and detection method provided in the above embodiments of this application. It can execute the lifting gear positioning and detection method provided in any of the above embodiments of this application and has the corresponding functional units and beneficial effects for executing the lifting gear positioning and detection method. Technical details not described in detail in this embodiment can be found in the specific processing content of the rotation angle verification method provided in the above embodiments of this application, and will not be repeated here.

[0110] Furthermore, each unit and / or module in the embodiments of this application can be configured with corresponding electronic components. The optional implementation methods of the embodiments of this application have been described in detail above with reference to the accompanying drawings. However, the embodiments of this application are not limited to the specific details of the above-described embodiments. Within the scope of the technical concept of the embodiments of this application, various simple modifications can be made to the technical solutions of the embodiments of this invention, and these simple modifications all fall within the protection scope of the embodiments of this application.

[0111] The readable storage media used by the communication devices or computing devices in the exemplary lifting device positioning and detection apparatus proposed in this application can be any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. Readable storage media can be, for example, including but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0112] Exemplary construction machinery

[0113] This application also provides an engineering machinery embodiment. Figure 4 This is a schematic diagram of the operation of an engineering machinery as one possible implementation of this application; the engineering machinery includes the lifting device mentioned in the above exemplary device. The composition, structure and function of the lifting device are described in the description of the exemplary device in this application, and will not be repeated here.

[0114] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0115] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting the positioning of a lifting device, characterized in that, The lifting device is equipped with 3D radar equipment at both ends, and the method includes: When the lifting device is lowered to the preset height, the 3D radar device is used to collect point cloud data of the scanning area, and the effective point cloud data of the vehicle board is extracted from the collected point cloud data. From the effective point cloud data of the vehicle panel, identify the point cloud data with abrupt changes in vehicle panel width. The point cloud data with abrupt changes in vehicle panel width refers to the point cloud data at the position where the difference between the width of the vehicle panel and the width of other vehicle panels exceeds a preset value in the effective point cloud data of the vehicle panel. Based on the point cloud data of the abrupt change in the width of the vehicle board, the working point cloud data of the vehicle board is extracted from the effective point cloud data of the vehicle board. Based on the cloud data of the working points on the vehicle platform, the position and orientation information of the spreader relative to the vehicle platform is calculated and determined; The step of calculating and determining the position and orientation information of the spreader relative to the vehicle platform based on the cloud data of the working position on the vehicle platform includes: Based on the point cloud data of the vehicle platform working position, obtain the vehicle platform attitude information; and perform reverse coordinate rotation on the point cloud data of the vehicle platform working position to obtain the point cloud data of the spreader. Based on the point cloud data of the working points on the vehicle platform and the point cloud data of the spreader, the position and pose information of the spreader relative to the vehicle platform is calculated and determined; The step of obtaining vehicle platform attitude information based on the vehicle platform work point cloud data includes: Depending on the type of 3D radar equipment used to collect point cloud data, the point cloud data of the vehicle platform operation points is divided into two groups. The two sets of point cloud data are sorted separately to obtain two sets of ordered point cloud data; The vehicle body attitude information is calculated based on two sets of ordered point cloud data.

2. The lifting device positioning detection method according to claim 1, characterized in that, The lifting device positioning and detection method further includes: downsampling and filtering the point cloud data of the scanning area collected by the 3D radar equipment.

3. The lifting device positioning detection method according to claim 1, characterized in that, The extraction of effective point cloud data for the vehicle body from the collected point cloud data includes: The ground point cloud data is filtered out from the point cloud data to obtain the effective point cloud data of the vehicle panel.

4. The lifting device positioning detection method according to claim 1, characterized in that, The calculation of vehicle board attitude information based on two sets of ordered point cloud data includes: Based on the coordinate difference between two sets of ordered point cloud data, the yaw angle, pitch angle and roll angle of the vehicle body are calculated to obtain the vehicle body attitude information. or, After performing planar fitting on the vehicle body based on two sets of ordered point cloud data, the yaw angle, pitch angle and roll angle of the vehicle body attitude are calculated to obtain the vehicle body attitude information.

5. The lifting device positioning detection method according to claim 1, characterized in that, The step of performing a reverse coordinate rotation on the point cloud data of the vehicle platform to obtain the point cloud data of the lifting device includes: Based on the point coordinates in the point cloud data of the vehicle board working position, the point coordinates are rotated around different rotation axes in reverse coordinates to obtain the reverse point cloud data of the vehicle board. The point cloud data of the spreader is obtained based on the reverse point cloud data of the vehicle platform.

6. The lifting device positioning detection method according to claim 1, characterized in that, The method for positioning and detecting the spreader also includes: determining the train type based on the effective point cloud data of the vehicle platform.

7. The lifting device positioning detection method according to claim 6, characterized in that, The step of determining the train type based on the effective point cloud data of the vehicle body includes: Separate the point cloud data above the vehicle panel, and identify the attribute information of the target object corresponding to the separated point cloud data. The attribute information includes at least one of the following: number, size, and shape. If the separated point cloud data is determined to be container point cloud data and carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container high-side train. If the separated point cloud data is determined to be the point cloud data of a container based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a container flatbed train. If the separated point cloud data is determined to be carriage point cloud data based on the attribute information of the target object corresponding to the separated point cloud data, then the train type is a high-side train; If point cloud data is not separated, then the train type is a flatbed train.

8. A lifting device for positioning and detecting lifting equipment, characterized in that, The lifting device for positioning and detection includes: The scanning unit is a 3D radar device configured on both sides of the lifting device; A control unit, wherein the control unit is used to implement the lifting device positioning and detection method according to any one of claims 1 to 7.

9. An engineering machinery, characterized in that, It includes the lifting gear positioning and detection device as described in claim 8.

Images