Stacking machine box alignment control method and equipment based on laser radar detection and stacking machine

Abstract

The invention provides a stacking machine box alignment control method and equipment based on laser radar detection and a stacking machine, and relates to the technical field of engineering machinery. The stacking machine comprises a first radar arranged at the bottom of a portal frame, a second radar arranged at the tail of a vehicle, and a third radar and a fourth radar which are arranged at the left end and the right end of a lifting appliance respectively, and the method comprises the following steps: according to a first point cloud data set collected by the first radar and a second point cloud data set collected by the second radar, obtaining a first point cloud data set; determining the distance between front and back obstacles of the stacking machine; according to a third point cloud data set acquired by a third radar and a fourth point cloud data set acquired by a fourth radar, determining a scanning contour line of the front surface of the container; determining a deviation included angle between the stacking machine and the container according to the scanning contour line; and controlling the stacking machine to be aligned with the container according to the distance between the front obstacle and the rear obstacle of the stacking machine, the scanning contour line and the offset included angle. According to the method, the container aligning operation of the stacking machine and the container can be rapidly, accurately and safely completed.

Description

Technical Field

[0001] This application relates to the technical field of construction machinery, and particularly to a container alignment control method, device and stacker crane based on lidar detection. Background Art

[0002] In the operation process of automatic container loading and unloading, a stacker crane (forklift crane, empty container stacker crane) plays an important role. The stacker crane can lift containers from the ground onto trucks, trains or other transportation tools, or stack containers together to save space.

[0003] During the operation of the stacker crane, aligning the stacker crane body with the container (container alignment) is a very crucial step, which determines the efficiency of container picking and placing. In the related art, the operator needs to frequently adjust the stacker crane body to align the spreader of the stacker crane with the container. However, this manual operation depends on the operator's personal experience and often requires repeated operations to complete the container alignment operation. Moreover, due to the limited field of vision and the complexity of the operation during the container alignment process, safety accidents such as collisions may occur.

[0004] Therefore, a stacker crane container alignment control scheme that can quickly, accurately and safely complete the container alignment operation between the stacker crane and the container is needed. Summary of the Invention

[0005] The embodiments of this application provide a stacker crane container alignment control method, device and stacker crane based on lidar detection, which can quickly, accurately and safely complete the container alignment operation between the stacker crane and the container.

[0006] In a first aspect, the embodiments of this application provide a stacker crane container alignment control method. The stacker crane includes a first radar arranged at the bottom of the gantry, a second radar arranged at the rear of the vehicle body, and a third radar and a fourth radar respectively arranged at the left and right ends of the spreader. The method includes:

[0007] Determine the distances between the stacker crane and the front and rear obstacles according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar;

[0008] Determine the scanning contour line of the front of the container according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar;

[0009] Determine the offset angle between the stacker crane and the container according to the scanning contour line;

[0010] Control the stacker crane to align with the container according to the distances between the stacker crane and the front and rear obstacles, the scanning contour line and the offset angle.

[0011] In a possible implementation manner, determining the scanning contour line of the front of the container ahead according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar includes:

[0012] Performing point cloud fusion on the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar to obtain a fused point cloud data set;

[0013] Performing point cloud filtering on the fused point cloud data set according to a preset front distance threshold and the width of the container to obtain a target point cloud data set;

[0014] Using the random sample consensus fitting algorithm to extract multiple sets of fitting line segments from the target point cloud data set;

[0015] Performing line segment reconstruction on the multiple sets of fitting line segments to obtain a reconstructed line segment set;

[0016] Determining the scanning contour line of the front of the container ahead according to the line segment in the reconstructed line segment set that is closest to the stacker.

[0017] In a possible implementation manner, the using the random sample consensus fitting algorithm to extract multiple sets of fitting line segments from the target point cloud data set includes:

[0018] Step S1: Performing line segment fitting and extraction on the target point cloud data set according to the random sample consensus fitting algorithm and a preset first distance threshold between adjacent two points to obtain an initial line segment;

[0019] Step S2: Judging whether the number of point clouds corresponding to the initial line segment is greater than a preset minimum number of point clouds for a straight line segment. If it is greater, storing the initial line segment in the set of fitting line segments; if it is less, deleting the initial line segment;

[0020] Step S3: Determining an updated target point cloud data set according to the point cloud data in the target point cloud data set except for the initial line segment;

[0021] Step S4: Repeating the above steps S1 - S4 according to the updated target point cloud data set until the number of point clouds in the updated target point cloud data set is less than or equal to the minimum number of point clouds for a straight line segment;

[0022] Step S5: Determining multiple sets of fitting line segments according to the final set of fitting line segments, where the multiple sets of fitting line segments include the point cloud sets corresponding to the multiple line segments respectively.

[0023] In a possible implementation, the reconstructing the line segments of the set of multiple fitted line segments to obtain a set of reconstructed line segments includes:

[0024] For each line segment in the set of multiple fitted line segments,

[0025] traverse each point cloud of the line segment and calculate the Euclidean distance between any two adjacent point clouds;

[0026] determine whether there are target adjacent two point clouds in the line segment whose Euclidean distance is greater than a second distance threshold between two adjacent points preset;

[0027] If there are, split the target adjacent two point clouds for line segment reconstruction to obtain corresponding sub-line segments, and store the sub-line segments in the set of reconstructed line segments;

[0028] If not, store the line segment in the set of reconstructed line segments.

[0029] In a possible implementation, the determining the scanning contour line of the front of the container according to the line segment in the set of reconstructed line segments that is closest to the stacker includes:

[0030] For each line segment in the set of reconstructed line segments, determine the coordinates of the center point of the line segment according to the coordinates of the first point cloud and the last point cloud of the line segment; determine the distance between the line segment and the stacker according to the coordinates of the center point of the line segment and the coordinates of the center point of the stacker;

[0031] Determine the scanning contour line of the front of the container according to the line segment in the set of reconstructed line segments with the smallest distance from the stacker;

[0032] wherein, the center point of the stacker is the center point between the third radar and the fourth radar.

[0033] In a possible implementation, the determining the offset angle between the stacker and the container according to the scanning contour line includes:

[0034] Determine the contour line direction vector of the scanning contour line according to the coordinates of the first point cloud and the last point cloud in the scanning contour line;

[0035] Determine the offset angle between the stacker and the container according to the angle between the contour line direction vector and the horizontal line.

[0036] In a possible implementation, the controlling the alignment of the stacker and the container according to the distance between the front and rear obstacles of the stacker, the scanning contour line, and the offset angle includes any one of the following:

[0037] A: Output the first point cloud data set, the second point cloud data set, the distances between the reach stacker and the obstacles in front and behind, the scanning contour line, and the offset angle to a visualization screen, so that an operator can manually control the alignment of the reach stacker with the container based on this;

[0038] B: Output the distances between the reach stacker and the obstacles in front and behind, the scanning contour line, and the offset angle to the controller of the reach stacker, so that the controller can automatically control the alignment of the reach stacker with the container;

[0039] Wherein, when the reach stacker is aligned with the container, the offset angle is 0.

[0040] In a possible implementation manner, the determining the distances between the reach stacker and the obstacles in front and behind according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar includes:

[0041] Obtain the first point cloud data set collected by the first radar, and perform point cloud filtering on the first point cloud data set according to a preset front distance threshold and the body width of the reach stacker to obtain a front point cloud data set;

[0042] Obtain the second point cloud data set collected by the second radar, and perform point cloud filtering on the second point cloud data set according to a preset rear distance threshold and the body width to obtain a rear point cloud data set;

[0043] Determine the distance between the obstacle in front of the reach stacker according to the minimum front distance in the front point cloud data set, and determine the distance between the obstacle behind the reach stacker according to the minimum rear distance in the rear point cloud data set.

[0044] In a second aspect, an embodiment of the present application provides a computing device, including:

[0045] An acquisition module, configured to acquire a first point cloud data set collected by a first radar, a second point cloud data set collected by a second radar, a third point cloud data set collected by a third radar, and a fourth point cloud data set collected by a fourth radar;

[0046] A processing module, configured to determine the distances between the reach stacker and the obstacles in front and behind according to the first point cloud data set and the second point cloud data set; determine the scanning contour line of the front of the front container according to the third point cloud data set and the fourth point cloud data set; determine the offset angle between the reach stacker and the container according to the scanning contour line; and control the alignment of the reach stacker with the container according to the distances between the reach stacker and the obstacles in front and behind, the scanning contour line, and the offset angle.

[0047] In a third aspect, an embodiment of the present application provides another computing device, including:

[0048] a processor and a memory communicatively connected to the processor;

[0049] The memory is used to store computer-executable instructions;

[0050] The processor is configured to execute the computer-executable instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect as described above.

[0051] In a fourth aspect, an embodiment of the present application provides a reach stacker, including: a first radar disposed at the bottom of the gantry, a second radar disposed at the rear of the vehicle, a third radar and a fourth radar respectively disposed at the left and right ends of the spreader, and the computing device as described in the third aspect. The computing device is further communicatively connected to a display and control device, and the display and control device includes one or more visualization screens.

[0052] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect and / or various possible implementation manners of the first aspect as described above.

[0053] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it is used to implement the first aspect and / or various possible implementation manners of the first aspect as described above.

[0054] The embodiments of the present application provide a method, device and stacker for controlling container alignment based on lidar detection. The first radar set at the bottom of the gantry can collect the first point cloud data set of the obstacles in front of the stacker, the second radar set at the rear of the vehicle can collect the second point cloud data set of the obstacles behind the stacker, and the third radar and the fourth radar respectively set at the left and right ends of the spreader can collect the relevant point cloud data of the container in front of the stacker (the third point cloud data set and the fourth point cloud data set). Then, the computing device on the stacker can determine the distances of the obstacles in front of and behind the stacker according to the first point cloud data set and the second point cloud data set; determine the scanning contour line of the front of the container according to the third point cloud data set and the fourth point cloud data set, and determine the offset angle between the stacker and the container according to the scanning contour line; finally, according to the distances of the obstacles in front of and behind the stacker, the scanning contour line and the offset angle, the stacker can be controlled to align with the container. Through such a setting, the body of the stacker can be adjusted and controlled according to the scanning contour line of the front of the container and the offset angle between the stacker and the container, so that the offset angle between the stacker and the container is adjusted to 0, thereby quickly and accurately completing the container alignment operation of the stacker; during the adjustment process, the driving speed and the body position of the stacker can also be adjusted according to the distances of the obstacles in front of and behind the stacker to avoid collisions during the operation and ensure the safe progress of the container alignment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0056] Figure 1 It is a system architecture diagram of an embodiment of the present application;

[0057] Figure 2 It is a flowchart of a method for controlling container alignment of a stacker based on lidar detection according to an embodiment of the present application;

[0058] Figure 3 It is a schematic diagram of the position between a stacker and a container according to an embodiment of the present application;

[0059] Figure 4 It is a schematic diagram of first radar and second radar point cloud data acquisition according to an embodiment of the present application;

[0060] Figure 5 It is a schematic diagram of the line segment reconstruction process according to an embodiment of the present application;

[0061] Figure 6 It is a schematic diagram of the distance between a line segment and a stacker according to an embodiment of the present application;

[0062] Figure 7The figure shows the scanning result display of an embodiment of the present application;

[0063] Figure 8 The figure shows the structural schematic diagram of a computing device according to an embodiment of the present application;

[0064] Figure 9 The figure shows the structural schematic diagram of a computing device according to another embodiment of the present application.

[0065] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and more detailed descriptions will be given later. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Embodiments

[0066] Exemplary embodiments will be described in detail here, and examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0067] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0068] It should be noted that in the embodiments of the present application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0069] The container control method, device and container stacker based on lidar detection of the present application can be used in the field of construction machinery technology, and can also be used in any field other than the field of construction machinery technology, such as the field of container technology, etc. The application fields of the container control method, device and container stacker based on lidar detection of the present application are not limited.

[0070] The container alignment control method, device and stacker crane based on lidar detection of the present application can be applied to scenarios such as automatic loading and unloading of port containers. Any scenario involving the alignment of a stacker crane and a container can apply the container alignment control method, device and stacker crane based on lidar detection of the present application.

[0071] During the operation of automatic container loading and unloading, the stacker crane (forklift crane, empty container stacker crane) plays an important role. The stacker crane can lift the container from the ground onto a truck, train or other transportation tools, or stack the containers together to save space.

[0072] Currently, the research on empty container stacker cranes mainly focuses on the structural optimization, energy conservation and environmental protection, as well as electrification and automation of the stacker crane. The commonly used stacker cranes mainly include the following three types: manual stacker crane, fully electric stacker crane, and semi-electric stacker crane. Among them, the fully electric stacker crane is more and more widely used.

[0073] During the operation of the stacker crane, aligning the body of the stacker crane with the container (container alignment) is a very crucial step, which determines the efficiency of picking up and placing the container. In the related art, the operator needs to frequently adjust the body of the stacker crane to align the spreader of the stacker crane with the container.

[0074] However, this manual operation depends on the personal experience of the operator and often requires repeated operations to complete the container alignment operation. Moreover, due to the limited vision and the complexity of the operation during the container alignment process, there is a lack of perception of the surrounding environment, and safety accidents such as collisions may occur.

[0075] Based on the above technical problems, the inventive concept of the present application lies in: how to provide a container alignment control solution for the stacker crane that can quickly, accurately and safely complete the container alignment operation between the stacker crane and the container.

[0076] The embodiments of the present application provide a container alignment control method, device and stacker crane based on lidar detection. The first radar set at the bottom of the gantry of the stacker crane, the second radar set at the rear of the vehicle, the third radar and the fourth radar respectively set at the left and right ends of the spreader can be used to sense the operation environment of the stacker crane, and determine the distances between the front and rear obstacles of the stacker crane, the scanning contour line of the front of the container in front, and the offset angle between the stacker crane and the container according to the point cloud data collected by the above radars. According to this information, the container alignment operation of the stacker crane with the container can be guided, so as to quickly, accurately and safely complete the container alignment operation.

[0077] The following uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0078] Figure 1 is a system architecture diagram of an embodiment of the present application. As Figure 1 shown, the reach stacker may include a first radar disposed at the bottom of the mast, a second radar disposed at the rear of the vehicle, and a third radar and a fourth radar respectively disposed at the left and right ends of the spreader. The reach stacker further includes a computing device, and the computing device may be communicatively connected to the first radar, the second radar, the third radar, and the fourth radar respectively to obtain the point cloud data collected by each radar. The computing device may determine the distances of the obstacles in front of and behind the reach stacker according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar; determine the scanning contour line of the front of the container according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar; determine the offset angle between the reach stacker and the container according to the scanning contour line; and control the alignment of the reach stacker and the container according to the distances of the obstacles in front of and behind the reach stacker, the scanning contour line, and the offset angle.

[0079] Figure 2 is a flowchart of a method for controlling the container alignment of a reach stacker based on lidar detection according to an embodiment of the present application. In this embodiment, the execution subject is a computing device to illustrate the method for controlling the container alignment of the reach stacker based on lidar detection. As Figure 2 shown, the method for controlling the container alignment of the reach stacker based on lidar detection may include the following steps:

[0080] S201: Determine the distances of the obstacles in front of and behind the reach stacker according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar.

[0081] In this embodiment, the reach stacker may include a first radar disposed at the bottom of the mast, a second radar disposed at the rear of the vehicle, and a third radar and a fourth radar respectively disposed at the left and right ends of the spreader.

[0082] In this embodiment, the computing device may be an in-vehicle computing device installed on the reach stacker, and the specific type of the computing device may be flexibly set by those skilled in the art and is not limited herein.

[0083] In this embodiment, the computing device may be communicatively connected to the first radar, the second radar, the third radar, and the fourth radar respectively to obtain the point cloud data collected by each radar.

[0084] In this embodiment, the first radar can be set at any position at the bottom of the gantry, as long as the first radar can collect the point cloud data of the obstacles in front of the reach stacker. Similarly, the second radar can be set at any position above or below the rear of the reach stacker, as long as the second radar can collect the point cloud data of the obstacles behind the reach stacker.

[0085] In this embodiment, during the operation of the reach stacker, the distance between the obstacles in front of and behind the vehicle is an important parameter in the work. The operator or the controller grasps information such as the driving speed and position of the reach stacker according to the distance between the obstacles in the front and rear to avoid safety accidents such as collisions between the reach stacker and the obstacles during operation.

[0086] In this embodiment, the obstacles can be containers, vehicles, pedestrians, or other people or objects that affect the driving of the reach stacker.

[0087] S202: Determine the scanning contour line of the front of the container according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar.

[0088] In this embodiment, the third radar and the fourth radar can be respectively set at the left and right ends of the spreader of the reach stacker.

[0089] In this embodiment, the third radar (or the fourth radar) can collect the point cloud data of the left side of the front container, and the fourth radar (or the third radar) can collect the point cloud data of the right side of the front container. By fusing the third point cloud data set and the fourth point cloud data set, all the point cloud data of the front of the container can be obtained. By performing line segment fitting and processing on all the point cloud data of the front of the container, all the point cloud data of the front of the container can be obtained.

[0090] In this embodiment, when the reach stacker is aligned with the container, it is the spreader of the reach stacker that is aligned with the front of the container to lift (fork) the container for loading and unloading. Therefore, it is necessary to determine the scanning contour line of the front of the container.

[0091] S203: Determine the offset angle between the reach stacker and the container according to the scanning contour line.

[0092] In this embodiment, the offset angle between the reach stacker and the container can be determined according to the angle between the scanning contour line and the horizontal line.

[0093] In this embodiment, Figure 3 is a schematic diagram of the position between the reach stacker and the container according to an embodiment of the present application. In the figure, the red line segment ab represents the scanning contour line obtained by scanning the current vehicle angle, and the yellow line segment represents the actual scanning contour line of the container. As Figure 3 shown in (1) below, when the offset angle When it is >0, the reach stacker deflects to the right, and the left turn of the reach stacker needs to be controlled; as Figure 3 shown in (2) below, when the offset angle between the reach stacker and the container is <0, the reach stacker deflects to the left, and the right turn of the reach stacker needs to be controlled; as Figure 3 shown in (3) below, when the offset angle between the reach stacker and the container is =0, the reach stacker is aligned with the container (the spreader is parallel to the container).

[0094] S204: Control the reach stacker to be aligned with the container according to the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle.

[0095] In this embodiment, the computing device can also be communicatively connected to the display and control device of the reach stacker to output and display information such as the point cloud data scanned by the radar, the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle on the visualization screen of the display and control device, so that the operator can perform manual box alignment according to the information displayed on the visualization screen. The display and control device can include one or more visualization screens. When there are multiple visualization screens, the above information can be respectively displayed on the corresponding visualization screens.

[0096] In this embodiment, the computing device can also be communicatively connected to the controller of the reach stacker to transmit information such as the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle to the controller, and the controller can perform automatic box alignment control accordingly.

[0097] In this embodiment, the first point cloud data set of the obstacles in front of the reach stacker can be collected by the first radar disposed at the bottom of the gantry, the second point cloud data set of the obstacles behind the reach stacker can be collected by the second radar disposed at the rear of the vehicle, and the relevant point cloud data (the third point cloud data set and the fourth point cloud data set) of the container in front of the reach stacker can be collected by the third radar and the fourth radar respectively disposed at the left and right ends of the spreader. Then, the computing device on the reach stacker can determine the distances of the obstacles in front of and behind the reach stacker according to the first point cloud data set and the second point cloud data set; determine the scanned contour line of the front of the container in front according to the third point cloud data set and the fourth point cloud data set, and determine the offset angle between the reach stacker and the container according to the scanned contour line; finally, according to the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle, the reach stacker can be controlled to be aligned with the container. Through such a setting, the body of the reach stacker can be adjusted and controlled according to the scanned contour line of the front of the container in front and the offset angle between the reach stacker and the container, so that the offset angle between the reach stacker and the container is adjusted to 0, thereby quickly and accurately completing the box alignment operation of the reach stacker and the container; during the adjustment process, the traveling speed and the body position of the reach stacker can also be adjusted according to the distances of the obstacles in front of and behind the reach stacker to avoid collisions during the operation and ensure the safe progress of the box alignment operation.

[0098] In a possible implementation manner, step S201 above determines the distances of the obstacles in front of and behind the stacker according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar, and may include:

[0099] S11: Obtain the first point cloud data set collected by the first radar, and perform point cloud filtering on the first point cloud data set according to a preset front distance threshold and the body width of the stacker to obtain a front point cloud data set.

[0100] S12: Obtain the second point cloud data set collected by the second radar, and perform point cloud filtering on the second point cloud data set according to a preset rear distance threshold and the body width to obtain a rear point cloud data set.

[0101] S13: Determine the distance of the obstacle in front of the stacker according to the minimum front distance in the front point cloud data set, and determine the distance of the obstacle behind the stacker according to the minimum rear distance in the rear point cloud data set.

[0102] In this implementation manner, those skilled in the art can flexibly set the preset front distance threshold according to actual needs. For example, the front distance threshold can be 10 m or 12 m. Similarly, those skilled in the art can also flexibly set the preset rear distance threshold according to actual needs. For example, the rear distance threshold can be 10 m or 12 m. The front distance threshold and the rear distance threshold can be the same or different, and no limitation is made here.

[0103] In this implementation manner, when the first radar / second radar collects point cloud data, it scans in a fan shape. When performing point cloud filtering, only the point cloud data in the area of the front distance threshold / rear distance threshold and the body width is retained to obtain the front point cloud data set / rear point cloud data set, and only the point cloud data in the working area of the stacker is retained to exclude the interference outside the working area.

[0104] Exemplarily, Figure 4 is a schematic diagram of the first radar and the second radar point cloud data collection in an embodiment of the present application. As Figure 4 shown, the first radar and the second radar scan in a fan shape. The x direction is the front-rear direction of the stacker, the y direction is the left-right direction of the stacker, and the z direction is the up-down direction of the stacker.

[0105] The computing device may first obtain the first point cloud data set collected by the first radar (front middle radar).

[0106] Secondly, exclude the interference outside the working area: filter the point cloud outside x1 to x2 m in the x direction at the front side, and filter the point cloud outside y1 to y2 m in the y direction on the left and right sides to obtain the front point cloud data set. Among them, x1 can be 0, x2 can be 10, and y1 to y2 can be the body width of the stacker.

[0107] Finally, select the minimum front distance in the x direction from the front point cloud data set as the distance to the obstacle in front of the stacker.

[0108] Similarly, the computing device can first obtain the second point cloud data set collected by the second radar (rear radar).

[0109] Secondly, exclude the interference outside the working area: filter the point cloud outside x3 to x4 m in the x direction at the rear side, and filter the point cloud outside y3 to y4 m in the y direction on the left and right sides to obtain the rear point cloud data set. Among them, x3 can be 0, x4 can be 10, and y3 to y4 can be the body width of the stacker.

[0110] Finally, select the minimum rear distance in the x direction from the rear point cloud data set as the distance to the obstacle behind the stacker.

[0111] In this embodiment, after obtaining the point cloud data set collected by the radar, the point cloud data can be filtered by using the preset front distance threshold / rear distance threshold and the body width of the stacker, and only the point cloud data in the working area of the stacker is retained, excluding the interference outside the working area, reducing the calculation amount while maintaining the measurement accuracy. According to the minimum value in the x direction in the filtered point cloud data set, the distances to the obstacles in front of and behind the stacker can be quickly and accurately determined.

[0112] In a possible implementation manner, the above step S202 determines the scanning contour line of the front of the container according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar, and may include:

[0113] S21: Perform point cloud fusion on the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar to obtain a fused point cloud data set.

[0114] S22: Filter the fused point cloud data set according to the preset front distance threshold and the width of the container to obtain a target point cloud data set.

[0115] S23: Use the random sample consensus fitting algorithm to extract multiple sets of fitting line segments from the target point cloud data set.

[0116] S24: Perform line segment reconstruction on the multiple sets of fitting line segments to obtain a reconstructed line segment set.

[0117] S25: Determine the scanning contour line of the front of the container ahead according to the line segment in the reconstructed line segment set that is closest to the stacker.

[0118] In this embodiment, the third radar (or the fourth radar) can collect the point cloud data on the left side of the front of the container ahead, and the fourth radar (or the third radar) can collect the point cloud data on the right side of the front of the container ahead. By fusing the third point cloud data set and the fourth point cloud data set, all the point cloud data of the front of the container ahead can be obtained.

[0119] In this embodiment, those skilled in the art can refer to the relevant existing technologies for the specific process of point cloud fusion, which will not be elaborated here.

[0120] In this embodiment, the preset front distance threshold can be flexibly set by those skilled in the art according to actual needs. For example, the front distance threshold can be 10m or 12m, and there is no limitation here.

[0121] In this embodiment, when the radar collects point cloud data, it scans in a fan shape. When filtering the fused point cloud data set, only the point cloud data in the area of the front distance threshold and the width of the container is retained to obtain the target point cloud data set, and only the point cloud data in the working area of the container is retained to exclude the interference outside the working area.

[0122] Exemplarily, (1) The computing device can obtain the third point cloud data set collected by the third radar (front left radar) and the fourth point cloud data set collected by the fourth radar (front right radar).

[0123] (2) After the computing device aligns the timestamps of the above point cloud data, point cloud fusion can be performed to obtain the fused point cloud data set.

[0124] (3) The computing device uses the preset front distance threshold and the width of the container to filter the point cloud and exclude the interference outside the working area: filter the point cloud outside x5m to x6m in the x direction and filter the point cloud outside y5m to y6m in the y direction to obtain the target point cloud data set. Among them, x5 can be 0, x6 can be 10, and y5 to y6 can be the width of the container.

[0125] In this embodiment, after performing point cloud fusion to obtain a fused point cloud data set, the point cloud data can be filtered using a preset forward distance threshold and the body width of the stacker, and only the point cloud data in the container working area is retained to obtain a target point cloud data set, so as to exclude interference outside the working area, reduce the calculation amount while maintaining the measurement accuracy. Then, the random sample consensus (RANSAC) fitting algorithm can be used to perform virtual line segment fitting and extraction on the target point cloud data set to obtain a set of multiple fitted line segments, and the set of multiple fitted line segments can be reconstructed to improve the line segment quality and obtain a more accurate reconstructed line segment set. According to the line segment closest to the stacker in the reconstructed line segment set, the scanning contour line of the front of the container can be accurately determined.

[0126] In a possible embodiment, the above step S23 of using the random sample consensus (RANSAC) fitting algorithm to extract a set of multiple fitted line segments from the target point cloud data set may include:

[0127] Step S1: Perform line segment fitting and extraction on the target point cloud data set according to the random sample consensus (RANSAC) fitting algorithm and a preset first distance threshold between adjacent two points to obtain an initial line segment.

[0128] Step S2: Determine whether the number of point clouds corresponding to the initial line segment is greater than a preset minimum number of point clouds for a straight line segment. If it is greater, store the initial line segment in the set of fitted line segments; if it is less, delete the initial line segment.

[0129] Step S3: Determine an updated target point cloud data set according to the point cloud data in the target point cloud data set except for the initial line segment.

[0130] Step S4: Repeat the above steps S1 - S4 according to the updated target point cloud data set until the number of point clouds in the updated target point cloud data set is less than or equal to the minimum number of point clouds for a straight line segment.

[0131] Step S5: Determine a set of multiple fitted line segments according to the final set of fitted line segments. The set of multiple fitted line segments includes the point cloud sets corresponding to multiple line segments respectively.

[0132] In this embodiment, the preset first distance threshold between adjacent two points can be flexibly set by those skilled in the art according to the actual situation. For example, it can be 0.1 m or 0.2 m, and no limitation is made here.

[0133] In this embodiment, the preset minimum number of point clouds for a straight line segment can be flexibly set by those skilled in the art according to the actual situation. For example, it can be 30 or 40, and no limitation is made here.

[0134] In this embodiment, the initial line segment can be a point cloud set of a line segment. The multiple fitting line segment sets can include the point cloud sets corresponding to the respective multiple line segments, and can be sorted in descending order according to the number of point clouds of each line segment.

[0135] In this embodiment, the random sample consensus (RANSAC) fitting algorithm and a preset first distance threshold between adjacent two points can be used to perform line segment fitting and extraction on the target point cloud data set to obtain the initial line segment, and a preset minimum number of point clouds of a straight line segment can be used to determine whether the initial line segment is available. If it is available, the initial line segment is stored in the fitting line segment set. This process is repeated multiple times until the number of point clouds in the updated target point cloud data set is less than or equal to the minimum number of point clouds of a straight line segment. The multiple fitting line segment sets can be determined according to the final fitting line segment set.

[0136] In a possible embodiment, the above step S24 performs line segment reconstruction on the multiple fitting line segment sets to obtain a reconstructed line segment set, which may include:

[0137] S31: For each line segment in the multiple fitting line segment sets, traverse each point cloud of the line segment and calculate the Euclidean distance between any two adjacent point clouds.

[0138] S32: Determine whether there are target adjacent two point clouds in the line segment whose Euclidean distance is greater than a preset second distance threshold between adjacent two points.

[0139] S33: If so, split the target adjacent two point clouds for line segment reconstruction to obtain corresponding sub-line segments, and store the sub-line segments in the reconstructed line segment set.

[0140] S34: If not, store the line segment in the reconstructed line segment set.

[0141] In this embodiment, the preset second distance threshold between adjacent two points can be flexibly set by those skilled in the art according to the actual situation. For example, it can be 0.1 m or 0.2 m, and there is no limitation here.

[0142] In this embodiment, if the Euclidean distance between two adjacent point clouds is greater than the preset second distance threshold between adjacent two points, these two point clouds may belong to different objects, and these two point clouds need to be split so that they belong to different line segments.

[0143] Exemplarily, Figure 5 is a schematic diagram of the line segment reconstruction process of an embodiment of the present application. As Figure 5 shown, traverse each line segment in the multiple fitting line segment sets , if If the Euclidean distance between two adjacent point clouds is greater than the second distance threshold, split these two point clouds for line segment reconstruction to obtain corresponding sub-line segments. .

[0144] In this embodiment, after obtaining a set of multiple fitted line segments, the line segments in the set can also be split and reconstructed using a preset second distance threshold between adjacent points to improve the quality of the line segments, make them more conform to the real scene, and thus improve the accuracy and authenticity of the reconstructed line segment set.

[0145] In a possible embodiment, step S25 above for determining the scanning contour line of the front of the container based on the line segment in the reconstructed line segment set closest to the stacker crane may include:

[0146] S41: For each line segment in the reconstructed line segment set, determine the coordinates of the center point of the line segment according to the coordinates of the first point cloud and the last point cloud of the line segment.

[0147] S42: Determine the distance between the line segment and the stacker crane according to the coordinates of the center point of the line segment and the coordinates of the center point of the stacker crane.

[0148] S43: Determine the scanning contour line of the front of the container according to the line segment with the smallest distance from the stacker crane in the reconstructed line segment set.

[0149] Among them, the center point of the stacker crane is the center point between the third radar and the fourth radar.

[0150] In this embodiment, the point clouds in the line segment are arranged from left to right. Therefore, the first point cloud can be the leftmost point cloud of the line segment, and the last point cloud can be the rightmost point cloud of the line segment.

[0151] In this embodiment, the Euclidean distance between the center point of the line segment and the center point of the stacker crane is the distance between the line segment and the stacker crane.

[0152] In this embodiment, when the radar scans, it may scan the objects above, below, left, and right of the front container, so that the reconstructed line segment set includes multiple line segments. Since the stacker crane is most likely to be parallel to the front container, other objects usually have a certain angle with the stacker crane due to the height difference or azimuth difference. When parallel, the Euclidean distance between objects is the smallest. Therefore, the line segment with the smallest distance from the stacker crane is the scanning contour line of the front of the container.

[0153] Exemplarily, Figure 6 is a schematic diagram of the distance between the line segment and the stacker crane in an embodiment of the present application. As Figure 6 shown, the reconstructed line segment set includes , , After obtaining the distances between each of the three line segments and the stacker respectively, it is found that is the closest to the stacker, then it is determined that is the scanning contour line of the front of the container.

[0154] In this embodiment, according to the coordinates of the center point of the line segment and the coordinates of the center point of the stacker, the distance between the line segment and the stacker can be determined; after calculating the distances between each line segment in the reconstructed line segment set and the stacker respectively, the minimum distance between them and the stacker can be determined, and the line segment with the smallest distance from the stacker is the scanning contour line of the front of the container in front.

[0155] In a possible embodiment, step S203 of determining the offset angle between the stacker and the container according to the scanning contour line may include:

[0156] S51: Determine the contour line direction vector according to the coordinates of the first point cloud and the coordinates of the last point cloud in the scanning contour line.

[0157] S52: Determine the offset angle between the stacker and the container according to the angle between the contour line direction vector and the horizontal line.

[0158] In this embodiment, the offset angle between the stacker and the container can be:

[0159]

[0160] where, (0, 1, 0) represents the three-dimensional coordinates of the horizontal line, represents the norm of the contour line direction vector.

[0161] In this embodiment, after calculating the contour line direction vector according to the three-dimensional coordinates of the first point cloud and the three-dimensional coordinates of the last point cloud in the scanning contour line, according to the contour line direction vector and the horizontal line, the angle between the contour line direction vector and the horizontal line can be determined, that is, the offset angle between the stacker and the container.

[0162] In a possible embodiment, step S204 of controlling the alignment of the stacker and the container according to the distances between the front and rear obstacles of the stacker, the scanning contour line and the offset angle may include any one of the following:

[0163] A: Output the first point cloud data set, the second point cloud data set, the distances between the front and rear obstacles of the stacker, the scanning contour line and the offset angle to the visualization screen, so that the operator can manually control the alignment of the stacker and the container accordingly.

[0164] B: Output the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle to the controller of the reach stacker, so that the controller can automatically control the alignment of the reach stacker with the container.

[0165] Wherein, when the reach stacker is aligned with the container, the offset angle is 0.

[0166] In this embodiment, the computing device can also be communicatively connected to the display and control device of the reach stacker to output and display information such as the point cloud data scanned by the radar, the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle on the visualization screen of the display and control device, so that the operator can manually align the container according to the information displayed on the visualization screen. The display and control device can include one or more visualization screens. When there are multiple visualization screens, the above information can be respectively displayed on the corresponding visualization screens.

[0167] Exemplarily, Figure 7 is a display diagram of the scanning result of an embodiment of the present application. As Figure 7 shown, part A in the figure is the first visualization screen of the display and control device. The first visualization screen can display information such as the point cloud data scanned by the radar, the self-image of the reach stacker, the scanned contour line, and the offset angle. Part B in the figure is the second visualization screen of the display and control device, and the second visualization screen can display the distance of the obstacle in front of the reach stacker vehicle.

[0168] In this embodiment, the computing device can also be communicatively connected to the controller of the reach stacker to transmit information such as the distances of the obstacles in front of and behind the reach stacker, the scanned contour line, and the offset angle to the controller, and the controller can perform automatic container alignment control based on this.

[0169] In this embodiment, the computing device can output the point cloud data scanned by the radar and the information calculated therefrom to the visualization screen, so that the operator can manually control the alignment of the reach stacker with the container based on this; the computing device can also output the information obtained from the point cloud data scanned by the radar to the controller of the reach stacker, so that the controller can automatically control the alignment of the reach stacker with the container.

[0170] Next, a specific embodiment is used to elaborate on the container alignment control method for the reach stacker based on lidar detection of the present application.

[0171] In a specific embodiment, a certain port uses a reach stacker to achieve automatic loading and unloading of containers. The reach stacker includes a first radar arranged at the bottom of the gantry, a second radar arranged at the rear of the vehicle, and a third radar and a fourth radar respectively arranged at the left and right ends of the spreader. During the container loading and unloading process, first, the alignment of the reach stacker with the container needs to be carried out. The specific control process is as follows:

[0172] First, the computing device of the forklift truck acquires the first point cloud data set collected by the first radar, and filters the first point cloud data set according to a preset front distance threshold and the body width of the forklift truck to obtain a front point cloud data set.

[0173] Second, the computing device acquires the second point cloud data set collected by the second radar, and filters the second point cloud data set according to a preset rear distance threshold and the body width to obtain a rear point cloud data set.

[0174] Third, the computing device determines the distance of the obstacle in front of the forklift truck according to the minimum front distance in the front point cloud data set, and determines the distance of the obstacle behind the forklift truck according to the minimum rear distance in the rear point cloud data set.

[0175] Fourth, the computing device performs point cloud fusion on the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar to obtain a fused point cloud data set.

[0176] Fifth, the computing device filters the fused point cloud data set according to a preset front distance threshold and the width of the container to obtain a target point cloud data set.

[0177] Sixth, the computing device uses the random sample consensus fitting algorithm, a preset first distance threshold between adjacent points, and a preset minimum number of point clouds for a straight line segment to extract multiple sets of fitting line segments from the target point cloud data set.

[0178] Seventh, the computing device reconstructs the multiple sets of fitting line segments using a preset second distance threshold between adjacent points to obtain a set of reconstructed line segments.

[0179] Eighth, the computing device calculates the distance between each line segment in the set of reconstructed line segments and the forklift truck respectively, and determines the scanning contour line of the front of the container in front according to the line segment with the minimum distance between the set of reconstructed line segments and the forklift truck.

[0180] Ninth, the computing device determines the contour line direction vector of the scanning contour line according to the coordinates of the first point cloud and the last point cloud in the scanning contour line; determines the offset angle between the forklift truck and the container according to the angle between the contour line direction vector and the horizontal line.

[0181] Tenth, the computing device outputs the distances of the obstacles in front of and behind the forklift truck, the scanning contour line, and the offset angle to the controller of the forklift truck, so that the controller can automatically control the alignment of the forklift truck with the container.

[0182] Figure 8 The structural schematic diagram of the computing device according to an embodiment of the present application is as Figure 8As shown in the figure, the computing device includes: an acquisition module 81, configured to acquire a first point cloud data set collected by a first radar, a second point cloud data set collected by a second radar, a third point cloud data set collected by a third radar, and a fourth point cloud data set collected by a fourth radar; a processing module 82, configured to determine the distances of obstacles in front of and behind the stacker according to the first point cloud data set and the second point cloud data set; determine the scanning contour line of the front of the front container according to the third point cloud data set and the fourth point cloud data set; determine the offset angle between the stacker and the container according to the scanning contour line; and control the alignment of the stacker and the container according to the distances of obstacles in front of and behind the stacker, the scanning contour line, and the offset angle.

[0183] The computing device provided by the embodiment of the present application can execute the technical solutions shown in the above method embodiments, and its implementation principles and beneficial effects are similar, and will not be elaborated here.

[0184] Figure 9 It is a schematic structural diagram of a computing device according to an embodiment of the present application. As Figure 9 shown, the computing device includes: a processor 901, and a memory 902 communicatively connected to the processor 901; the memory 902 stores computer-executable instructions; the processor 901 executes the computer-executable instructions stored in the memory 902 to implement the steps of the stacker box control method based on lidar detection in the above method embodiments.

[0185] In the above computing device, the memory 902 and the processor 901 are directly or indirectly electrically connected to implement data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus connection. The memory 902 stores computer-executable instructions for implementing the data access control method, including at least one software function module that can be stored in the memory 902 in the form of software or firmware. The processor 901 executes the software programs and modules stored in the memory 902 to perform various functional applications and data processing.

[0186] The memory 902 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), etc. Among them, the memory 902 is used to store programs. After receiving an execution instruction, the processor 901 executes the program. Further, the software programs and modules in the memory 902 may further include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide a running environment for other software components.

[0187] The processor 901 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 901 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0188] An embodiment of the present application further provides a reach stacker, as Figure 1 shown, including: a first radar disposed at the bottom of the gantry, a second radar disposed at the rear of the vehicle, a third radar and a fourth radar respectively disposed at the left and right ends of the spreader, and a Figure 9 computing device as shown, and the computing device is also communicatively connected to a display and control device, and the display and control device includes one or more visualization screens.

[0189] In this embodiment, the operation environment of the reach stacker can be sensed by the first radar disposed at the bottom of the gantry of the reach stacker, the second radar disposed at the rear of the vehicle, the third radar and the fourth radar respectively disposed at the left and right ends of the spreader, and the distances to the obstacles in front and behind the reach stacker, the scanning contour line of the front of the container, and the offset angle between the reach stacker and the container can be determined respectively according to the point cloud data collected by the above radars. According to this information, the box alignment operation of the reach stacker with respect to the container can be guided, so as to quickly, accurately, and safely complete the box alignment operation.

[0190] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the steps of the method embodiments of the present application.

[0191] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the method embodiments of the present application.

[0192] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0193] Furthermore, it should be noted that although the steps in the flowchart are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0194] It should be understood that the above device embodiments are only illustrative, and the devices of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0195] In addition, without special instructions, in each embodiment of the present application, each functional unit / module can be integrated in one unit / module, or each unit / module can exist physically alone, or two or more units / modules can be integrated together. The above integrated unit / module can be implemented in the form of hardware or in the form of a software program module.

[0196] In the above embodiments, the descriptions of the various embodiments each have their own emphasis. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0197] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the appended claims.

[0198] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for controlling a container by a forklift based on laser radar detection, characterized in that: The forklift includes a first radar arranged at the bottom of the gantry, a second radar arranged at the rear of the vehicle, and a third radar and a fourth radar respectively arranged at the left and right ends of the spreader, and the method includes: Determine the distance between obstacles in front and behind the forklift according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar; Determine a scanning contour line of the front side of the container in front according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar; Determine an offset angle between the stacker and the container according to the scan contour line; The forklift is controlled to be aligned with the container according to the distance between obstacles in front and behind the forklift, the scanning contour line and the offset angle.

2. The method for controlling a box by a forklift based on laser radar detection according to claim 1 is characterized in that: The step of determining a scanning contour line of the front side of the container according to the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar comprises: Performing point cloud fusion on the third point cloud data set collected by the third radar and the fourth point cloud data set collected by the fourth radar to obtain a fused point cloud data set; Performing point cloud filtering on the fused point cloud data set according to a preset front distance threshold and the width of the container to obtain a target point cloud data set; Extracting a plurality of fitting line segment sets from the target point cloud data set using a random sampling consistent fitting algorithm; Reconstructing the plurality of fitted line segment sets to obtain a reconstructed line segment set; The scanning contour line of the front side of the container ahead is determined according to the line segment closest to the stacker in the reconstructed line segment set.

3. The method for controlling a box by a forklift based on laser radar detection according to claim 2 is characterized in that: The method of extracting a plurality of fitting line segment sets from the target point cloud data set using a random sampling consistent fitting algorithm includes: Step S1: performing line segment fitting and extraction on the target point cloud data set according to a random sampling consistent fitting algorithm and a preset first distance threshold between two adjacent points to obtain an initial line segment; Step S2: determining whether the number of point clouds corresponding to the initial line segment is greater than a preset minimum number of point clouds for a straight line segment; if so, storing the initial line segment in a fitted line segment set; if less, deleting the initial line segment; Step S3: determining an updated target point cloud data set according to the point cloud data in the target point cloud data set except the initial line segment; Step S4: repeating the above steps S1-S4 according to the updated target point cloud data set until the number of point clouds in the updated target point cloud data set is less than or equal to the minimum number of point clouds of the straight line segment; Step S5: determining a plurality of fitting line segment sets according to the final fitting line segment set, wherein the plurality of fitting line segment sets include point cloud sets corresponding to the plurality of line segments.

4. The method for controlling a box by a forklift based on laser radar detection according to claim 2 is characterized in that: The reconstructing the plurality of fitted line segment sets to obtain a reconstructed line segment set includes: For each line segment in the plurality of fitted line segment sets, Traversing each point cloud of the line segment, and calculating the Euclidean distance between any two adjacent point clouds; Determine whether there are two adjacent point clouds of the target in the line segment whose Euclidean distance is greater than a preset second distance threshold between two adjacent points; If so, split the two adjacent point clouds of the target to reconstruct the line segments, obtain corresponding sub-line segments, and store the sub-line segments in the reconstructed line segment set; If it does not exist, the line segment is stored in the reconstructed line segment set.

5. The method for controlling a box by a forklift based on laser radar detection according to claim 2 is characterized in that: The step of determining the scanning contour line of the front side of the container according to the line segment closest to the stacker in the set of reconstructed line segments includes: For each line segment in the reconstructed line segment set, determine the coordinates of the center point of the line segment according to the coordinates of the first point cloud and the coordinates of the last point cloud of the line segment; determine the distance between the line segment and the forklift according to the coordinates of the center point of the line segment and the coordinates of the center point of the forklift; Determine a scanning contour line of the front side of the container in front according to the line segment with the smallest distance from the stacker in the set of reconstructed line segments; The center point of the forklift is the center point between the third radar and the fourth radar.

6. The method for controlling a box on a forklift based on laser radar detection according to any one of claims 1 to 5, characterized in that: The step of determining the offset angle between the stacker and the container according to the scan contour line includes: Determine a contour line direction vector of the scanned contour line according to the coordinates of the first point cloud and the coordinates of the last point cloud in the scanned contour line; The offset angle between the stacker and the container is determined according to the angle between the contour line direction vector and the horizontal line.

7. The method for controlling a box on a forklift based on laser radar detection according to any one of claims 1 to 5, characterized in that: The controlling the forklift to align with the container according to the distance between the obstacles in front and behind the forklift, the scanning contour line and the offset angle includes any one of the following: A: outputting the first point cloud data set, the second point cloud data set, the distance between the obstacles in front and behind the forklift, the scanning contour line and the offset angle to a visualization screen, so that an operator can manually control the forklift to align with the container accordingly; B: outputting the distance between the obstacles in front and behind the forklift, the scanning contour line and the offset angle to the controller of the forklift, so that the controller automatically controls the forklift to align with the container; Wherein, when the forklift is aligned with the container, the offset angle is 0.

8. The method for controlling a container by a forklift based on laser radar detection according to any one of claims 1 to 5, characterized in that: The determining the distance between obstacles in front of and behind the forklift according to the first point cloud data set collected by the first radar and the second point cloud data set collected by the second radar comprises: Acquire a first point cloud data set collected by the first radar, and perform point cloud filtering on the first point cloud data set according to a preset front distance threshold and a body width of the forklift to obtain a front point cloud data set; Acquire a second point cloud data set collected by the second radar, and perform point cloud filtering on the second point cloud data set according to a preset rear distance threshold and the vehicle body width to obtain a rear point cloud data set; The distance to the obstacle in front of the forklift is determined according to the minimum front distance in the front point cloud data set, and the distance to the obstacle behind the forklift is determined according to the minimum rear distance in the rear point cloud data set.

9. A computing device, characterized in that include: A processor, and a memory communicatively connected to the processor; The memory is used to store computer-executable instructions; The processor is used to execute the computer-executable instructions stored in the memory, so that the processor executes the forklift box control method based on laser radar detection as described in any one of claims 1-8.

10. A forklift, characterized in that: include: A first radar arranged at the bottom of the gantry, a second radar arranged at the rear of the vehicle, a third radar and a fourth radar respectively arranged at the left and right ends of the sling, and a computing device as described in claim 9, wherein the computing device is also communicatively connected to a display and control device, and the display and control device includes one or more visualization screens.

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