Method and apparatus for detecting and aligning a spreader

Abstract

The application provides a method and device for detecting and aligning a spreader. The method comprises detecting the position of a first alignment device and the position of a second alignment device; hovering when the first alignment device falls from an initial position to a first position, and determining whether the first alignment device and the second alignment device are aligned according to the positions of the first alignment device and the second alignment device at this time; and controlling the first alignment device and the second alignment device to accurately align when the first alignment device and the second alignment device are not aligned. In the process of unloading a container, the first alignment device is a spreader, and the second alignment device is a container. In the process of loading a container, the first alignment device is a combination of a spreader and a container, and the second alignment device is a standard container loading position. The application ensures that the first alignment device and the second alignment device are accurately aligned when the first alignment device falls around the second alignment device, and the container loading and unloading operation is completed.

Description

Technical Field

[0001] This application relates to the field of lifting equipment technology, and in particular to a method and apparatus for detecting and aligning lifting devices. Background Technology

[0002] With the continuous development and upgrading of technology, unmanned driving in ports is gradually becoming popular. Unmanned container trucks in ports need to work in conjunction with cranes during operations. The cranes can be quay cranes (also known as bridge cranes) or rail-mounted gantry cranes (also known as gantry cranes).

[0003] Container trucks, consisting of tractor units and trailers, require precise crane position detection during crane operations to ensure smooth loading and unloading of containers. Before operations begin, the crane's spreader is often outside the container truck's detection range due to the high initial position of the operation or ongoing other tasks. Current detection methods rely on cameras or lidar to detect markers on the crane (either inherent or added) before the spreader descends, determining the relative position of the container truck and the crane.

[0004] Existing technologies are affected by external factors (such as weather, equipment maintenance, aging of the work site, etc.), and the lifting equipment may deviate from the standard working position during the descent, which may result in the loading and unloading of containers being unable to be completed due to excessive deviation after the lifting equipment is lowered. Summary of the Invention

[0005] This application provides a method and apparatus for detecting and aligning lifting devices, which can solve the problem of inaccurate alignment of lifting devices during container loading and unloading operations and improve the success rate of operations.

[0006] In a first aspect, this application provides a method for detecting and aligning a lifting device, comprising:

[0007] The positions of the first alignment device and the second alignment device are detected. During the unloading process, the first alignment device is a spreader and the second alignment device is a container. During the loading process, the first alignment device is a combination of spreader and container and the second alignment device is a standard loading position for placing the container.

[0008] After the first alignment device descends from the initial position to the first position, it is determined whether the first alignment device and the second alignment device are aligned based on the positions of the first alignment device and the second alignment device. The height between the first position and the second alignment device is less than the height between the initial position and the first position.

[0009] When the first alignment device and the second alignment device are not aligned, the first alignment device and the second alignment device are aligned according to their respective positions.

[0010] In one feasible approach, detecting the position of the first alignment device and the position of the second alignment device includes:

[0011] The radar mounted on the top of the truck acquires point cloud data from the first alignment device and the second alignment device. The truck includes a tractor and a trailer. The radar is mounted on the top of the tractor and the second alignment device is located on the trailer.

[0012] The point cloud data is transformed from the radar coordinate system to the vehicle front coordinate system, which is the coordinate system of the truck.

[0013] Based on the installation pose of the radar, the hovering height of the first alignment device, and the installation position of the second alignment device, the regions of interest (ROI) of the first and second alignment devices are determined from the coordinate-transformed point cloud data.

[0014] Calculate the normal vector of the point cloud within the ROI, and determine the angle between the normal vector of each point in the point cloud within the ROI and the X-axis of the vehicle front coordinate system;

[0015] Remove points with included angles greater than or equal to a preset angle from the point cloud within the ROI to obtain the first point cloud set of the ROI;

[0016] The RANSAC algorithm is used to remove noise points from the first point cloud set of the ROI to obtain the second point cloud set of the ROI;

[0017] The position of the first alignment device is determined based on the second point cloud set of the ROI of the first alignment device, and the position of the second alignment device is determined based on the second point cloud set of the ROI of the second alignment device.

[0018] In one feasible approach, determining the position of the first alignment device based on a second point cloud set of the ROI of the first alignment device, and determining the position of the second alignment device based on a second point cloud set of the ROI of the second alignment device, includes:

[0019] Determine the centroid of the second point cloud set of the ROI of the first alignment device and the centroid of the second point cloud set of the ROI of the second alignment device;

[0020] The centroid of the second point cloud set of the ROI of the first alignment device is taken as the position of the first alignment device, and the centroid of the second point cloud set of the ROI of the second alignment device is taken as the position of the second alignment device.

[0021] In one feasible approach, controlling the alignment of the first alignment device and the second alignment device based on the positions of the first alignment device and the second alignment device includes:

[0022] Calculate the difference between the first alignment device and the second alignment device on the X-axis;

[0023] The position of the truck is adjusted according to the difference so that the first alignment device and the second alignment device are aligned.

[0024] In one feasible approach, removing noise points from the first point cloud set of the ROI using the RANSAC algorithm to obtain the second point cloud set of the ROI includes:

[0025] The RANSAC algorithm is used to fit the points in the first point cloud set of the ROI to obtain the fitting plane;

[0026] Points in the first point cloud set of the ROI that do not belong to the fitting plane are identified as noise points;

[0027] Remove the noise points from the first point cloud set of the ROI.

[0028] Secondly, this application provides a detection and alignment device for a lifting device, comprising:

[0029] The detection module is used to detect the position of the first alignment device and the position of the second alignment device. During the unloading process, the first alignment device is a spreader and the second alignment device is a container. During the loading process, the first alignment device is a combination of spreader and container and the second alignment device is a standard loading position for placing the container.

[0030] The determining module is used to determine whether the first alignment device and the second alignment device are aligned based on the positions of the first alignment device and the second alignment device after the first alignment device descends from the initial position to the first position, wherein the height between the first position and the second alignment device is less than the height between the initial position and the first position;

[0031] The control module is used to align the first alignment device and the second alignment device according to the positions of the first alignment device and the second alignment device when the first alignment device and the second alignment device are not aligned.

[0032] In one feasible approach, the detection module is specifically used for:

[0033] The radar mounted on the top of the truck acquires point cloud data from the first alignment device and the second alignment device. The truck includes a tractor and a trailer. The radar is mounted on the top of the tractor and the second alignment device is located on the trailer.

[0034] The point cloud data is transformed from the radar coordinate system to the vehicle front coordinate system, which is the coordinate system of the truck.

[0035] Based on the installation pose of the radar, the hovering height of the first alignment device, and the installation position of the second alignment device, the regions of interest (ROI) of the first and second alignment devices are determined from the coordinate-transformed point cloud data.

[0036] Calculate the normal vector of the point cloud within the ROI, and determine the angle between the normal vector of each point in the point cloud within the ROI and the X-axis of the vehicle front coordinate system;

[0037] Remove points with included angles greater than or equal to a preset angle from the point cloud within the ROI to obtain the first point cloud set of the ROI;

[0038] The RANSAC algorithm is used to remove noise points from the first point cloud set of the ROI to obtain the second point cloud set of the ROI;

[0039] The position of the first alignment device is determined based on the second point cloud set of the ROI of the first alignment device, and the position of the second alignment device is determined based on the second point cloud set of the ROI of the second alignment device.

[0040] In one feasible approach, the determining module is specifically used for:

[0041] Determine the centroid of the second point cloud set of the ROI of the first alignment device and the centroid of the second point cloud set of the ROI of the second alignment device;

[0042] The centroid of the second point cloud set of the ROI of the first alignment device is taken as the position of the first alignment device, and the centroid of the second point cloud set of the ROI of the second alignment device is taken as the position of the second alignment device.

[0043] In one feasible approach, the control module is specifically used for:

[0044] Calculate the difference between the first alignment device and the second alignment device on the X-axis;

[0045] The position of the truck is adjusted according to the difference so that the first alignment device and the second alignment device are aligned.

[0046] In one feasible approach, the detection module specifically comprises:

[0047] The RANSAC algorithm is used to fit the points in the first point cloud set of the ROI to obtain the fitting plane;

[0048] Points in the first point cloud set of the ROI that do not belong to the fitting plane are identified as noise points;

[0049] The noise points are removed from the first point cloud set of the ROI to obtain the second point cloud set of the ROI.

[0050] In one feasible approach, during the packing process, the detection module is further used for:

[0051] Measure the standard packing position according to the markings at the standard packing position;

[0052] Alternatively, determine the location of the standard packing position based on the installation drawings of the standard packing position.

[0053] Thirdly, this application provides an electronic device, the device comprising: a processor, and a memory communicatively connected to the processor;

[0054] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect above, or in various possible implementations of the first aspect above.

[0055] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect above, or in various possible implementations of the first aspect above.

[0056] The detection and alignment method and apparatus for the spreader provided in this application are based on the point cloud data of the first and second alignment devices detected by radar on the truck. The relative position of the two alignment devices is determined according to the point cloud data. Real-time detection of the spreader by radar avoids interference from external factors and achieves accurate alignment of the spreader. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] Figure 1 This is a schematic diagram of an unmanned truck system;

[0059] Figure 2 A flowchart illustrating a method for detecting and aligning a lifting device according to Embodiment 1 of this application;

[0060] Figure 3 This is a schematic diagram of a device position detection method provided in Embodiment 2 of this application;

[0061] Figure 4 This is a schematic diagram of a vehicle front coordinate system;

[0062] Figure 5 This is a schematic diagram showing the angle between the normal vector of a point within the ROI and the X-axis of the vehicle's front coordinate system.

[0063] Figure 6 This is a flowchart illustrating another method for detecting and aligning a lifting device provided in Embodiment 3 of this application;

[0064] Figure 7 This is a schematic diagram of a box unloading process;

[0065] Figure 8 A flowchart illustrating another method for detecting and aligning a lifting device provided in Embodiment 4 of this application;

[0066] Figure 9 This is a schematic diagram of a packing process;

[0067] Figure 10 This is a schematic diagram of the structure of a detection and alignment device for a lifting device provided in Embodiment 5 of this application;

[0068] Figure 11 This is a schematic diagram of the structure of an electronic device provided in Embodiment Six of this application.

[0069] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0070] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0071] First, let me explain the terms used in this application:

[0072] Unmanned container trucks in ports: These are unmanned container trucks used in ports. The trucks include tractors and trailers.

[0073] Point cloud: A massive collection of points that represents the spatial distribution and surface characteristics of a target in the same spatial reference frame. After obtaining the spatial coordinates of each sampling point on the surface of an object, what is obtained is a collection of points, called a "point cloud".

[0074] The front coordinate system is a right-handed coordinate system defined based on the position of the truck. Its origin is the center of the front end of the truck. The X-axis is parallel to the ground and extends forward along the front of the truck. The Y-axis is parallel to the ground and extends outward perpendicular to the X-axis. The Z-axis is perpendicular to the ground and extends away from the ground.

[0075] Region of Interest (ROI): In machine vision and image processing, the area in an image to be processed is delineated using shapes such as rectangles, circles, ellipses, or irregular polygons. Existing algorithms can be used to determine the ROI within an image. By identifying the ROI, image analysis and processing within that region can be targeted, reducing processing time, saving processing resources, and increasing processing accuracy.

[0076] RANSAC (Random Sample Consensus) is an algorithm that calculates mathematical model parameters for a dataset containing outliers to obtain valid sample data. RANSAC is frequently used in computer vision. Its basic assumption is that the samples contain both inliers (data that can be described by the model) and outliers (data that deviates significantly from the normal range and cannot fit into the mathematical model), meaning the dataset contains noise. These outliers may be caused by incorrect measurements, incorrect assumptions, or incorrect calculations.

[0077] Center of mass: refers to an imaginary point in a material system where the mass is believed to be concentrated.

[0078] With the advancement and development of autonomous driving, more and more ports are abandoning traditional manual driving and opting for intelligent and highly efficient unmanned container trucks during port crane operations. When large cranes are loading and unloading containers, the crane controls the raising and lowering of the spreader. When the unmanned container truck safely travels to the vicinity of the crane's operating area within the designated driving zone, the spreader accurately grabs or places containers to complete the loading and unloading operation.

[0079] It is undeniable that in current container loading and unloading operations, the spreader may deviate from the standard position during descent due to external factors (such as weather, equipment maintenance, aging of the work site, etc.). When the unmanned truck stops at the standard position, the spreader may be unable to complete the container loading and unloading operation due to the deviation.

[0080] This application provides a method for detecting and aligning spreader equipment. A multi-line lidar device is installed on the top of the truck, which can scan point cloud data of the spreader, container, and standard container in the crane's operating area. The positions of the spreader, container, and standard container are obtained by analyzing the point cloud data. During unloading operations, the positions of the spreader and container are detected multiple times during the spreader's descent, and the spreader and container are precisely aligned based on their positions at a specific hovering height. During loading operations, the position of the combined equipment formed by the spreader and container is continuously detected during the descent, and the combined equipment and the standard container position are precisely aligned based on their positions at a specific hovering height. Specifically, detecting and aligning the spreader when it reaches a specific hovering height solves the problem in the prior art where the spreader deviates from the standard operating position during descent, potentially preventing loading and unloading operations from being completed due to excessive deviation.

[0081] This application provides a method for detecting and aligning a lifting device. Figure 1 This is a schematic diagram of an unmanned truck system. Figure 1 As shown, the unmanned container truck 101 includes a tractor unit 102 and a trailer 106. The tractor unit 102 controls the movement of the trailer 106. A radar 103 is mounted on the top of the tractor unit 102. The trailer 106 has a standard container loading position for placing a container 105. During unloading, a spreader 104 unloads the container 105 from the standard container loading position on the trailer 106 and places it in a fixed position. During loading, the spreader 104 lifts the container 105 and places it in the standard container loading position on the trailer 106. The spreader 104 is controlled by a crane, which is not shown in the figure due to its large size. The radar 103 collects data and analyzes the collected data to determine the positions of the spreader 104, the container 105, and the standard container loading position. The radar 103 can scan the spreader 104, the container 105, and the trailer 106. Radar 103 can be a multi-line lidar or other types of radar, and this application does not limit it.

[0082] The technical solution of this application and how it solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0083] Figure 2This is a flowchart illustrating a method for detecting and aligning a spreader according to Embodiment 1 of this application. This method can be applied to a spreader detection and alignment device. In the following embodiments, this spreader detection and alignment device is simply referred to as an alignment device. The alignment device can be an on-board device for a truck, which can be either an unmanned or manned truck. The on-board device for an unmanned truck is used to control the operation of the unmanned truck. Optionally, the alignment device can also be a remote device, such as a dedicated computer, personal computer, mobile phone, or dedicated control alignment device. Figure 2 As shown, the method of this embodiment includes the following steps.

[0084] S201. Detect the position of the first alignment device and the position of the second alignment device.

[0085] The method of this embodiment can be applied not only to the unloading process but also to the loading process. When the method of this embodiment is applied to the unloading process, the first alignment device is a spreader and the second alignment device is a container. When the method of this embodiment is applied to the loading process, the first alignment device is a combination device of spreader and container, and the second alignment device is a standard loading position. The standard loading position is used to place the container and is set on the truck. When the truck includes a tractor and a trailer, the standard loading position is set on the trailer.

[0086] In this embodiment, the position detection method of the first alignment device and the second alignment device can adopt existing methods or the method described in Embodiment 2 of this application. For details, please refer to the description of Embodiment 2, which will not be repeated here.

[0087] S202. After the first alignment device descends from the initial position to the first position, determine whether the first alignment device and the second alignment device are aligned based on the positions of the first alignment device and the second alignment device.

[0088] Before the operation, the second alignment device has been initially aligned with the first alignment device by measuring the relative positions of the truck and the crane. The initial position refers to the position when the crane moves the first alignment device from the ship or other loading and unloading equipment to the work area, and the initial position is determined according to the on-site operation conditions.

[0089] Typically, the initial position is far from the radar. After the first alignment device descends from the initial position, due to the limited field of view of the radar, it may take some time before the radar detects the position of the first alignment device. The first position refers to the location the first alignment device must pass through during its descent from the initial position. The height between the first position and the second alignment device is less than the height between the initial position and the first position. The height of the first position can be determined according to the on-site operating conditions; please refer to Embodiment 3 for details, which will not be elaborated here.

[0090] When the first alignment device descends to the first position, it hovers at the first position. The radar detects the positions of the first and second alignment devices at this time, and the alignment device determines whether the first and second alignment devices are aligned.

[0091] S203. When the first alignment device and the second alignment device are not aligned, the first alignment device and the second alignment device are aligned according to their positions.

[0092] When the first alignment device is not aligned with the second alignment device in the first position, the first alignment device is positioned by a crane and the second alignment device is positioned by a truck. When controlling the positions of the first and second alignment devices, the position of the truck can be adjusted to align the first and second alignment devices, the position of the crane can be adjusted to align the first and second alignment devices, or the positions of the truck and the crane can be adjusted simultaneously to align the first and second alignment devices. This embodiment does not limit this.

[0093] When the first alignment device descends from a higher initial position to a lower first position, external factors (such as weather, equipment maintenance, aging work site, etc.) may cause it to deviate from the standard operating position during descent. This could result in the loading and unloading operations being impossible due to excessive deviation after the spreader has descended. Once the first alignment device is suspended at the first position and precisely aligned before descending, the loading and unloading operations can be successfully completed. Because the height of the first position is relatively low, the impact of external factors on the descent of the first alignment device to the vicinity of the second alignment device is minimal; in actual operation, this impact can be ignored.

[0094] In this application, to reduce interference from external factors and ensure precise alignment between the first alignment device and the second alignment device, the radar repeatedly detects the positions of the first and second alignment devices as the first alignment device descends from its initial position to its first position. Based on these positions at the first position, the first and second alignment devices are precisely aligned. If, at the first position, external factors cause a deviation in the positions of the first and second alignment devices after initial alignment at the initial position, the alignment device promptly adjusts the position of the container truck to ensure precise alignment between the first and second alignment devices when the first alignment device descends to the vicinity of the second alignment device, thus completing the loading and unloading operation.

[0095] In this application, the radar utilizes the reflection characteristics of objects to radio waves to detect the position of the target aiming device, including distance, altitude and azimuth. The radar will start detection when it acquires the point cloud data of the target aiming device, i.e. message-driven.

[0096] Based on Embodiment 1, Embodiment 2 of this application provides a position detection method for detecting the position of the first alignment device and the position of the second alignment device in Embodiment 1.

[0097] Figure 3 This is a schematic diagram of a device position detection method provided in Embodiment 2 of this application, as shown below. Figure 3 As shown, the method provided in this embodiment includes the following steps.

[0098] S301. Acquire point cloud data from a first alignment device and a second alignment device collected by a radar mounted on the top of a container truck. The container truck includes a tractor and a trailer. The radar is mounted on the top of the tractor, and the second alignment device is located on the trailer.

[0099] The radar can be mounted vertically or horizontally on the top of the tractor unit; this embodiment does not impose any limitation on this. The radar is used to collect data from the surfaces of the first and second alignment devices facing the top of the tractor unit, and sends the collected point cloud data to the alignment device.

[0100] The installation position and orientation of the radar determine the range of the point cloud that the radar can scan in the vertical direction. When the radar is installed horizontally, the mounting bracket can be raised to ensure that the radar can scan the lifting device.

[0101] In addition, the installation orientation of the radar affects the initial hovering height of the first alignment device, which is also related to safety factors. For example, when the tractor unit is 3 meters high, and the radar is vertically installed at a height of 3 meters, with the initial position at 8 meters, it can be ensured that the spreader will not collide with the truck whether it is loading or unloading containers, and the radar can scan the main body of the spreader.

[0102] During the loading and unloading process, the truck safely travels to the crane's operating area via its onboard satellite navigation system or other positioning devices and then stops. As the first alignment device descends to the target position, the radar collects point cloud data from both the first and second alignment devices. This point cloud data includes the point's location information, i.e., its three-dimensional coordinates.

[0103] S302. Convert the point cloud data from the radar coordinate system to the vehicle front coordinate system, which is the coordinate system of the truck.

[0104] The radar and the truck use different coordinate systems. After receiving the point cloud data from the radar, the alignment device transforms the data from the radar's coordinate system to the vehicle's front coordinate system. The radar's coordinate system is determined by its installation position; once installed, the radar's coordinate system is uniquely determined. Figure 4 This is a schematic diagram of a front coordinate system. The front coordinate system is a right-handed coordinate system defined according to the position of the truck. Its origin is the center of the front end of the truck. The X-axis is parallel to the ground and extends forward along the front of the truck. The Y-axis is parallel to the ground and extends outward perpendicular to the X-axis. The Z-axis is perpendicular to the ground and extends away from the ground.

[0105] S303. Based on the radar's installation orientation, the hovering height of the first alignment device, and the installation position of the second alignment device, determine the ROI of the first and second alignment devices from the point cloud data after coordinate transformation.

[0106] Because the radar has a wide acquisition range, the collected point cloud includes many points that are not located at the locations of the first and second alignment devices. Therefore, in this embodiment, the Regions of Interest (ROIs) of the first and second alignment devices are defined. This region can better reflect the spatial range of the target alignment devices. At this point, the alignment device has completed the first rough screening of the approximate locations of the first and second alignment devices.

[0107] Based on the radar's installation orientation, the hovering height of the first alignment device, and the installation position of the second alignment device, the coordinate range of the ROI of the first and second alignment devices can be obtained. The coordinate range of the ROI is the maximum and minimum value of the ROI on the X, Y, and Z axes.

[0108] Table 1 shows one possible coordinate range for the ROI of the spreader and container during unloading. The values ​​in the table are in meters (m). As shown in Table 1, the ROI of the spreader and container has the same coordinate range on the X and Y axes, but different coordinate range on the Z axis. This is due to the different heights of the spreader and container. The spreader needs to be precisely aligned at the first position. Considering factors such as radar field of view, safety, and the influence of obstacles, the Z-axis coordinate range for the spreader and container in Table 1 is determined. Within this range, accurate positioning of the spreader and container can be guaranteed, and safe unloading can be achieved. In Table 1, "trailer X" refers to the coordinate value of the leftmost position of the trailer in the X direction when the trailer and tractor are straightened. This can be obtained through on-site measurement or from the truck's drawings.

[0109] Table 1. ROI of Spreading Equipment and Containers

[0110]

[0111] S304. Calculate the normal vector of the point cloud within the ROI, and determine the angle between the normal vector of each point in the point cloud within the ROI and the X-axis of the vehicle front coordinate system.

[0112] The normal vector is one of the important attributes of each point in a point cloud. As can be seen from spatial transformations, the angle and curvature of the normal vector of each point in the point cloud do not change with the motion of the object, exhibiting rigid body invariance. Solving for the normal vector of a point cloud requires the support of its neighborhood points, and the size of the neighborhood is generally represented by the neighborhood radius or the number of neighboring points. In reality, the value needs to be selected based on factors such as point resolution, the level of detail of the object, and the intended use. An excessively large neighborhood will smooth out the details of the 3D structure, making the normal vector too coarse, while an excessively small neighborhood, containing too few points, is more susceptible to noise interference.

[0113] Commonly used methods for normal vector estimation include: Delaunay triangulation, robust statistical methods, and local surface fitting. This embodiment can use any of these existing methods to calculate the normal vectors of points within the point cloud; the specific algorithms will not be detailed here.

[0114] After calculating the normal vectors of each point, the next step is to calculate the angle formed by the intersection of the normal vectors of all points with the X-axis of the vehicle's front coordinate system.

[0115] S305. Remove points with included angles greater than or equal to a preset angle from the point cloud within the ROI to obtain the first point cloud set of the ROI.

[0116] Figure 5 This is a schematic diagram showing the angle between the normal vector of a point within the ROI and the X-axis of the vehicle's front coordinate system. Figure 5 The angle α is the angle between the normal vector and the X-axis. Assuming the preset angle is 10° or 5°, the angle α between the normal vector of each point in the point cloud of the ROI in S304 and the X-axis of the vehicle front coordinate system is compared with the preset angle. Points with an angle greater than or equal to the preset angle are removed to obtain the first point cloud set of the ROI.

[0117] The choice of the preset angle depends on the quality of the radar and / or the smoothness of the device surface. Better radar quality and / or a smoother device surface allow for a smaller preset angle, resulting in a more accurate first point cloud. However, the preset angle should not be too small, as it's crucial to ensure a sufficient number of points are included in the first point cloud for accurate location of the alignment device. In practice, since the alignment device surface is not perfectly smooth, the points in the final ROI first point cloud are located within the three-dimensional geometric structure.

[0118] In this step, points whose normal vectors make an angle greater than or equal to the X-axis within the ROI are removed. This removes points that are far from the surfaces of the first and second alignment devices, completing the first precise screening of points within the ROI and obtaining the first point cloud set of the ROI.

[0119] S306. Use the RANSAC algorithm to remove noise points from the first point cloud set of the ROI to obtain the second point cloud set of the ROI.

[0120] In one feasible approach, the existing RANSAC algorithm is used to fit the points in the first point cloud set of the ROI to obtain a fitting plane. Points in the first point cloud set of the ROI that do not belong to the fitting plane are identified as noise points. Noise points are removed from the first point cloud set of the ROI, thus completing the second precise screening of each point in the ROI and obtaining the second point cloud set of the ROI.

[0121] The existing RANSAC algorithm is used to fit discrete points within the first point cloud space of the ROI, resulting in a fitting plane. This fitting plane is parallel to the planes of the first and second alignment devices where the radar-acquired points are located. Figure 4 and Figure 5 In the coordinate system shown, the fitting plane is the plane containing the YOZ.

[0122] In daily operations, noise points may originate from incorrect measurements, incorrect assumptions, or incorrect calculations. The location of noise points on the plane is significantly far from the optimal linear regression model. Removing noise points can provide the precise location of the equipment.

[0123] S307. Determine the centroid of the second point cloud set of the ROI.

[0124] The center of mass, or centroid for short, refers to a hypothetical point on a material system where the mass is considered to be concentrated. The centroid of the second point cloud set of the ROI can be obtained using existing methods, which will not be explained in detail here.

[0125] S308. Take the centroid of the second point cloud set of the ROI of the first alignment device as the position of the first alignment device, and take the centroid of the second point cloud set of the ROI of the second alignment device as the position of the second alignment device.

[0126] The position of the centroid can be used to characterize the position of the target alignment device, wherein the centroid of the second point cloud set of the ROI of the first alignment device is used as the position of the first alignment device, and the centroid of the second point cloud set of the ROI of the second alignment device is used as the position of the second alignment device.

[0127] In this application, the position of the target alignment device can also be represented by a linear equation or a planar equation. For example, by projecting all points of the second point cloud set of the ROI onto the XOY plane, the optimal linear equation x = ky + b for all points is calculated, and the intersection of this linear equation with the X-axis is the position of the target alignment device. Similarly, the position is represented by a planar equation based on the fitting plane of the second point cloud set of the ROI being parallel to the YOZ plane, and the intersection of this fitting plane with the X-axis is the position of the target alignment device.

[0128] If position detection is performed during the container loading process, the first alignment device is a combination of the spreader and the container, and the second alignment device is the standard container loading position. The standard container loading position is used to place the container. The standard container loading position is measured automatically based on the markings at that position, or determined automatically based on known installation drawings. See Example 4 for details, which will not be repeated here. Therefore, during the loading process, the radar only needs to collect point cloud data from the first alignment device and does not need to collect point cloud data from the second alignment device. During the unloading process, the first alignment device is the spreader, and the second alignment device is the container. The radar needs to collect point cloud data from both target alignment devices to determine their positions.

[0129] The method of Embodiment 2 of this application can accurately measure the position of the first alignment device and the position of the second alignment device, thereby further improving the alignment efficiency of the first alignment device and the second alignment device.

[0130] Based on Embodiment 1 and Embodiment 2, Embodiment 3 of this application uses the unloading process as an example to illustrate the alignment method. During the unloading process, the first alignment device is a spreader, the second alignment device is a container, the spreader is controlled by a crane, and the container is placed on a truck. Figure 6 This is a flowchart illustrating another method for detecting and aligning a lifting device, as provided in Embodiment 3 of this application. Figure 6 As shown, the method includes:

[0131] S601, Inspect the position of the spreader and the container.

[0132] Figure 7 This is a schematic diagram of an unloading process, for reference. Figure 7 The point cloud data collected by the radar includes point cloud data of the first surface of the spreader facing the truck head and point cloud data of the second surface of the container facing the truck head.

[0133] The alignment device can accurately detect the positions of the spreader and the container using the method described in Embodiment 2. The specific implementation is detailed in Embodiment 2 and will not be repeated here. According to the detailed description in Embodiment 2, after the spreader descends from its initial position to the first position, it hovers at the first position. The radar detects the positions of the spreader and the container at this time. The position coordinates of the spreader and the container are the centroids of the second point cloud set of the ROI of the spreader and the container, denoted as centroid A and centroid B, respectively. Centroid A represents the position of the spreader, and centroid B represents the position of the container.

[0134] S602. After the spreader descends from the initial position to the first position, determine whether the spreader and the container are aligned based on the position of the spreader and the position of the container.

[0135] The positions of the spreader and the container are represented by three-dimensional coordinates in the front coordinate system of the vehicle, specifically the three-dimensional coordinates of the center of mass A and center of mass B in the front coordinate system. Based on the three-dimensional coordinates of center of mass A and center of mass B, the difference between them on the X-axis is obtained, and this difference is denoted as N. X This difference reflects the relative positions of the spreader and the container on the X-axis.

[0136] If N X If the absolute value of N is greater than the preset maximum tolerance deviation (e.g., 5cm, the maximum tolerance deviation is determined according to site requirements), then it is determined that the spreader and the container are not aligned. X If the absolute value is less than the preset maximum tolerance deviation, then the alignment of the spreader and the container is determined.

[0137] S603. When the spreader and container are not aligned, control the alignment of the spreader and container according to the position of the spreader and the position of the container.

[0138] If the Nx value described in S602 is greater than the preset maximum tolerance deviation, and it is determined that the spreader and container are not aligned, the alignment device controls the truck to adjust its position so that the spreader and container are aligned.

[0139] When the alignment device is an on-board device for a truck, the on-board device aligns according to the difference N. X Automatic control of the truck's position adjustment. When the alignment device is a remote device independent of the onboard equipment, it sends a position adjustment command to the truck's onboard equipment, the command including a difference value N. X The onboard equipment adjusts the position of the truck according to the adjustment command, so that the spreader and the container are aligned.

[0140] For example, when the spreader is 0.15 meters more than the container in the X direction, the alignment device controls the truck to move 0.15 meters in the positive X-axis direction. Conversely, when the spreader is 0.15 meters less than the container in the X direction, the alignment device controls the truck to move 0.15 meters in the negative X-axis direction.

[0141] Once the spreader is suspended in the first position and precisely aligned with the container, it descends to the target position where the container is located, and successfully grabs the container from the trailer, completing the unloading operation.

[0142] Embodiment 4 of this application uses the packing process as an example to illustrate the alignment method. In the packing process, the first alignment device is a combination device of the spreader and the container, referred to as the combination device, and the second alignment device is the standard packing position. Figure 8 This is a flowchart illustrating another method for detecting a lifting device, as provided in Embodiment 4 of this application. Figure 8 As shown, the method includes:

[0143] S801, the location of the testing combination equipment and the standard packaging location.

[0144] Unlike the unloading process, the loading process requires determining the standard loading position independently. This can be done by measuring the standard loading position based on the markings at that location. Specifically, the standard loading position is located at the end of the trailer closest to the radar. A mark is placed at this position, serving as the safe loading point for the container, ensuring that the left end of the container falls on the mark. When the trailer is straightened, the X-axis coordinate of this mark in the vehicle's front coordinate system is denoted as the Tx value, which is negative. For example, if you measure the distance to the mark on the X-axis using a rangefinder at the work site and find it to be 8 meters, then the Tx value is -8.

[0145] Optionally, based on the installation drawings where the standard packing position is known, the coordinates of the standard packing position on the X-axis in the front coordinate system of the vehicle can be read or calculated.

[0146] Figure 9 This is a schematic diagram of a packing process, for reference. Figure 9 The point cloud data collected by the radar includes point cloud data of the first surface of the combined equipment facing the truck head and point cloud data of the second surface of the trailer facing the truck head.

[0147] The alignment device can accurately detect the position of the combined equipment using the method described in Embodiment 2. The specific implementation is detailed in Embodiment 2 and will not be repeated here. According to the detailed description of Embodiment 2, after the spreader descends from the initial position to the first position, the combined equipment hovers at the first position. The radar detects the position of the combined equipment at this time. The position coordinates of the combined equipment are the centroid of the second point cloud set of the ROI of the combined equipment, denoted as centroid C. Centroid C represents the position of the combined equipment.

[0148] S802. After the combined equipment descends from the initial position to the first position, determine whether the combined equipment and the standard packing position are aligned based on the position of the combined equipment.

[0149] The position of the combined equipment is represented by three-dimensional coordinates in the front coordinate system of the vehicle, that is, the three-dimensional coordinates of the center of mass C in the front coordinate system of the vehicle. Based on the three-dimensional coordinates of the center of mass C, the difference between the X-coordinate value and the Tx value of the center of mass C is calculated, and this difference is denoted as N. X This difference reflects the relative positions of the combined equipment and the standard packing location on the X-axis. The specific determination method is described in Example 3, and will not be repeated here.

[0150] S803. When the combined equipment and the standard packing are not aligned, the combined equipment and the standard packing shall be aligned according to the positions of the combined equipment and the standard packing.

[0151] If the Nx value in S802 is greater than the preset maximum tolerance deviation, it is determined that the combined equipment and the standard container are not aligned. The alignment device then controls the truck to adjust its position, aligning the combined equipment and the standard container. The alignment device controls the truck to perform automatic or remote adjustment to align the combined equipment and the standard container, achieving precise alignment of the combined equipment in the first position and the standard container position. The truck adjustment method is described in Example 3 and will not be repeated here.

[0152] Once the combined equipment is hovering in the first position and precisely aligned with the standard container loading position, the combined equipment descends to the target position where the standard container is located, and the spreader smoothly places the container onto the trailer, completing the loading operation.

[0153] Figure 10 This is a schematic diagram of the structure of a detection and alignment device for a lifting device provided in Embodiment 5 of this application, as shown below. Figure 10 As shown, the device 100 includes the following modules.

[0154] The detection module 1001 is used to detect the position of the first alignment device and the position of the second alignment device. During the unloading process, the first alignment device is a spreader and the second alignment device is a container. During the loading process, the first alignment device is a combination of spreader and container and the second alignment device is a standard loading position for placing the container.

[0155] The determining module 1002 is used to determine whether the first alignment device and the second alignment device are aligned based on the positions of the first alignment device and the second alignment device after the first alignment device descends from the initial position to the first position, wherein the height between the first position and the second alignment device is less than the height between the initial position and the first position.

[0156] The detection module 1003 is used to control the alignment of the first alignment device and the second alignment device based on their respective positions when the first alignment device and the second alignment device are not aligned. In one feasible embodiment, the detection module 1003 is further used to:

[0157] The radar mounted on the top of the truck collects point cloud data from the first alignment device and the second alignment device. The truck includes a tractor and a trailer. The radar is mounted on the top of the tractor and the second alignment device is placed on the trailer.

[0158] The point cloud data is transformed from the radar coordinate system to the vehicle front coordinate system, which is the coordinate system of the container truck.

[0159] Based on the radar's installation pose, the hovering height of the first alignment device, and the installation position of the second alignment device, the regions of interest (ROI) of the first and second alignment devices are determined from the coordinate-transformed point cloud data.

[0160] Calculate the normal vector of the point cloud within the ROI, and determine the angle between the normal vector of each point in the point cloud within the ROI and the X-axis of the vehicle front coordinate system.

[0161] Remove points with included angles greater than or equal to a preset angle from the point cloud within the ROI to obtain the first point cloud set of the ROI;

[0162] The RANSAC algorithm is used to remove noise points from the first point cloud set of the ROI to obtain the second point cloud set of the ROI.

[0163] The position of the first alignment device is determined based on the second point cloud set of the ROI of the first alignment device, and the position of the second alignment device is determined based on the second point cloud set of the ROI of the second alignment device.

[0164] In one feasible approach, module 1002 is specifically used for:

[0165] Determine the centroid of the second point cloud set of the ROI of the first alignment device and the centroid of the second point cloud set of the ROI of the second alignment device;

[0166] The centroid of the second point cloud set of the ROI of the first alignment device is taken as the position of the first alignment device, and the centroid of the second point cloud set of the ROI of the second alignment device is taken as the position of the second alignment device.

[0167] In one feasible approach, the control module 1003 is specifically used for:

[0168] Calculate the difference between the first alignment device and the second alignment device on the X-axis;

[0169] Adjust the position of the truck according to the difference so that the first alignment device and the second alignment device are aligned.

[0170] In one feasible approach, the detection module 1001 specifically comprises:

[0171] The RANSAC algorithm is used to fit the points in the first point cloud set of the ROI to obtain the fitting plane;

[0172] Points in the first point cloud set of the ROI that do not belong to the fitting plane are identified as noise points;

[0173] Noisy points are removed from the first point cloud set of the ROI to obtain the second point cloud set of the ROI.

[0174] In one feasible approach, during the packing process, the detection module 1001 is specifically used for:

[0175] Measure the standard packing position according to the markings at the standard packing position;

[0176] Alternatively, determine the location of the standard packing position based on the installation drawings of the standard packing position.

[0177] The device 100 provided in this embodiment can be used to execute the method described in any one of the embodiments from embodiment one to embodiment four. The specific implementation and technical effects are similar, and will not be repeated here.

[0178] Figure 11 This is a schematic diagram of the structure of an electronic device provided in Embodiment Six of this application. Figure 11 As shown, the electronic device 110 may include a processor 1101 and a memory 1102 communicatively connected to the processor. The processor 1101 and the memory 1102 can be connected via a bus. The electronic device 110 provided in this embodiment can be used with any of the methods described in Embodiments 1 to 4, with similar implementation methods and technical effects, and will not be repeated here.

[0179] This application provides a computer-readable storage medium in Embodiment Seven. This computer-readable storage medium can be used with the methods described in any of Embodiments One to Four, with similar implementation methods and technical effects, and will not be repeated here.

[0180] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0181] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for detecting and aligning a lifting device, characterized in that, include: The method involves detecting the positions of a first alignment device and a second alignment device. During unloading, the first alignment device is a spreader, and the second alignment device is a container. During loading, the first alignment device is a combination of the spreader and the container, and the second alignment device is a standard loading position for placing the container. Detecting the positions of the first and second alignment devices includes: acquiring point cloud data of the first and second alignment devices collected by a radar mounted on the top of a truck (including a tractor and a trailer), with the radar mounted on the top of the tractor and the second alignment device located on the trailer; converting the point cloud data from the radar coordinate system to the truck's front coordinate system; and determining the regions of interest (ROIs) of the first and second alignment devices from the converted point cloud data based on the radar's mounting pose, the hovering height of the first alignment device, and the mounting position of the second alignment device. The coordinate range of the ROI refers to the maximum and minimum values ​​of the ROI on the X, Y, and Z axes. The ROIs of the spreader and the container have the same coordinate range on the X and Y axes, but different coordinate ranges on the Z axis. The normal vector of the point cloud within the ROI is calculated, and the angle between the normal vector of each point in the point cloud within the ROI and the X-axis of the vehicle front coordinate system is determined. Points with angles greater than or equal to a preset angle are removed from the point cloud within the ROI to obtain the first point cloud set of the ROI. The RANSAC algorithm is used to fit the points in the first point cloud set of the ROI to obtain a fitting plane. This fitting plane is the plane containing the YOZ coordinate system. Points in the first point cloud set of the ROI that do not belong to the fitting plane are identified as noise points. The noise points are removed from the first point cloud set of the ROI to obtain the second point cloud set of the ROI. The position of the first alignment device is determined based on the second point cloud set of the ROI of the first alignment device, and the position of the second alignment device is determined based on the second point cloud set of the ROI of the second alignment device. After the first alignment device descends from the initial position to the first position, it is determined whether the first alignment device and the second alignment device are aligned based on the positions of the first alignment device and the second alignment device. The height between the first position and the second alignment device is less than the height between the initial position and the first position. When the first alignment device and the second alignment device are not aligned, the first alignment device and the second alignment device are aligned according to their respective positions.

2. The method according to claim 1, characterized in that, Determining the position of the first alignment device based on the second point cloud set of the ROI of the first alignment device, and determining the position of the second alignment device based on the second point cloud set of the ROI of the second alignment device, includes: Determine the centroid of the second point cloud set of the ROI of the first alignment device and the centroid of the second point cloud set of the ROI of the second alignment device; The centroid of the second point cloud set of the ROI of the first alignment device is taken as the position of the first alignment device, and the centroid of the second point cloud set of the ROI of the second alignment device is taken as the position of the second alignment device.

3. The method according to claim 1 or 2, characterized in that, The step of controlling the alignment of the first alignment device and the second alignment device according to their positions includes: Calculate the difference between the first alignment device and the second alignment device on the X-axis; The position of the truck is adjusted according to the difference so that the first alignment device and the second alignment device are aligned.

4. The method according to claim 1, characterized in that, During the packing process, the position of the second alignment device is detected, including: Measure the standard packing position according to the markings at the standard packing position; Alternatively, the location of the standard packing position can be determined based on the installation drawings of the standard packing position.

5. A detection and alignment device for a lifting device, characterized in that, include: A detection module is used to detect the positions of a first alignment device and a second alignment device. During unloading, the first alignment device is a spreader, and the second alignment device is a container. During loading, the first alignment device is a combination of the spreader and the container, and the second alignment device is a standard loading position for placing the container. Specifically, the detection module includes: acquiring point cloud data of the first and second alignment devices collected by a radar mounted on the top of a truck, where the truck includes a tractor and a trailer; converting the point cloud data from the radar coordinate system to the truck's front coordinate system; and determining the points of interest of the first and second alignment devices from the converted point cloud data based on the radar's mounting posture, the hovering height of the first alignment device, and the mounting position of the second alignment device. Region of Interest (ROI); where the coordinate range of the ROI is the maximum and minimum values ​​of the ROI on the X, Y, and Z axes; the ROIs of the spreader and the container have the same coordinate range on the X and Y axes, but different coordinate ranges on the Z axis; calculate the normal vector of the point cloud within the ROI, and determine the angle between the normal vector of each point in the point cloud within the ROI and the X-axis of the vehicle front coordinate system; remove points from the point cloud within the ROI whose angle is greater than or equal to a preset angle to obtain the first point cloud set of the ROI; use the RANSAC algorithm to fit the points in the first point cloud set of the ROI to obtain a fitting plane; this fitting plane is the plane containing the YOZ; identify points in the first point cloud set of the ROI that do not belong to the fitting plane as noise points; remove the noise points from the first point cloud set of the ROI to obtain the second point cloud set of the ROI; determine the position of the first alignment device based on the second point cloud set of the ROI of the first alignment device, and determine the position of the second alignment device based on the second point cloud set of the ROI of the second alignment device. The determining module is used to determine whether the first alignment device and the second alignment device are aligned based on the positions of the first alignment device and the second alignment device after the first alignment device descends from the initial position to the first position, wherein the height between the first position and the second alignment device is less than the height between the initial position and the first position; The control module is used to align the first alignment device and the second alignment device according to the positions of the first alignment device and the second alignment device when the first alignment device and the second alignment device are not aligned.

6. An electronic device, characterized in that, The device includes: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-4.

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