Laser radar portal crane deflection angle and speed measuring method and system

Abstract

This invention relates to a method for measuring the yaw angle and speed of a gantry crane based on lidar, comprising the following steps: S1, a lidar capable of scanning 180 degrees vertically is installed on the rotating platform of the crane, enabling the lidar to acquire point cloud data in front; S2, the point cloud data obtained by the lidar scan is subjected to pass-through filtering and statistical filtering, and then Euclidean clustering is performed on the filtered data to obtain clustered point cloud clusters of the elephant trunk beam and the grab bucket; S3, the coordinates (X, Y, X) of the end point P of the elephant trunk beam are obtained based on the clustered point cloud cluster data. P Y P Z P The coordinates (X) of the grab's center of mass Q. Q Y Q Z Q S4. Obtain the horizontal swing angle θ of the suspended load using the coordinates of P and Q; S5. Monitor the coordinates of P and Q in real time as the luffing mechanism moves, and obtain the horizontal velocity v of P and Q at each moment. P v Q This invention offers high measurement accuracy and strong anti-interference capabilities, achieving a measurement precision of 10mm, and can perform accurate measurements even in harsh environments such as ports.

Description

Technical Field

[0001] This invention relates to the field of laser measurement, and more specifically, to a method and system for measuring the sway angle and speed of a gantry crane based on lidar. Background Technology

[0002] For a long time, the goods lifted by gantry cranes have been constantly swinging during operation. When unloading, it is necessary to ensure that the goods are stable, otherwise safety accidents may easily occur. Therefore, anti-sway operation is required after the goods arrive at each operation. Moreover, since most of the time workers have to visually observe the swing angle and then make corrections, it greatly affects work efficiency.

[0003] To address this issue, earlier published photogrammetric methods for measuring the luffing speed and horizontal displacement trajectory of gantry cranes proposed a photogrammetric approach. While this method is effective, its accuracy is not ideal and it lacks real-time capability. Existing methods for determining the luffing trajectory of gantry cranes include a theodolite-based approach, which offers some accuracy but requires precise theodolite placement. A machine vision-based method for measuring the sway angle of a gantry crane's load is also presented in existing research; however, this method requires the installation of markers on the crane mechanism, leading to excessive external interference and compromised accuracy. Additionally, an infrared-based method is available, but this requires the installation of baffles at the target location, which can negatively impact the crane's operation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for measuring the sway angle and speed of a gantry crane based on lidar, so as to realize the measurement of the sway angle of the cargo during the operation of the crane and transmit the data to the luffing mechanism control system in real time, thereby reducing the sway angle through the control system and ensuring the safety of the operation process.

[0005] The technical solution adopted by this invention to solve its technical problem is: to construct a method for measuring the sway angle and speed of a gantry crane based on lidar, including the following steps:

[0006] S1. The rotating platform of the crane is equipped with a lidar that can perform 180-degree scanning in the vertical direction, so that the lidar can acquire point cloud data in front.

[0007] S2. Perform pass-through filtering and statistical filtering on the point cloud data obtained by lidar scanning, and then perform Euclidean clustering on the filtered data to obtain clustered point cloud clusters of elephant trunk and grab bucket.

[0008] S3. Obtain the coordinates (X, X) of the endpoint P of the elephant's trunk bridge based on the clustered point cloud data. P YP Z P The coordinates (X) of the grab's center of mass Q. Q Y Q Z Q );

[0009] S4. Obtain the horizontal swing angle θ of the suspended weight using the coordinates of P and Q;

[0010] S5. As the luffing mechanism moves, monitor the coordinates of P and Q in real time and obtain the horizontal velocity v of P and Q at each moment. P v Q .

[0011] According to the above scheme, in step S2, the point cloud data is subjected to pass-through filtering, specifically: traversing each point of the three-dimensional point cloud data, outliers are removed according to the set threshold range to reduce the amount of calculation. The threshold range is: data within the range of (-5.0, 5.0) meters on the X-axis, and all data on the Y-axis and Z-axis.

[0012] According to the above scheme, in step S2, statistical filtering is performed on the point cloud data. Specifically, the average distance from each point to k neighboring points is calculated, where k is 50. Then, the average distance and standard deviation of the global samples are calculated. A standard range is set based on the mean and standard deviation. Points whose average distance is outside the standard range are removed to obtain point cloud data with noise points removed.

[0013] According to the above scheme, in step S2, Euclidean clustering is performed on the point cloud data. Specifically, a KD-Tree nearest neighbor search is used to search for a point M in the three-dimensional space to obtain K points that are closest to M. Points with a distance less than a threshold are added to the set. Then, a point N is selected from the set, and a KD-Tree nearest neighbor search is performed on the point. Points with a distance less than the threshold are added to the set. This process is repeated until the number of points in the set no longer increases, thus completing the clustering.

[0014] According to the above scheme, in step S3, the coordinates (X, X) of the end point P of the elephant trunk are calculated based on the point cloud clusters obtained by clustering. P Y P Z P The coordinates (X) of the grab's center of mass Q. Q Y Q Z Q The calculation method is as follows:

[0015]

[0016] Among them, (x i y i , z i ), i = 1, 2, ..., N are the three-dimensional coordinates of each point within the point cloud cluster of the elephant's trunk;

[0017]

[0018] Among them, (x j y j , z j ), j = 1, 2, ..., N are the three-dimensional coordinates of each point within the grab bucket point cloud cluster.

[0019] According to the above scheme, in step S4, the horizontal swing angle θ of the suspended weight is obtained based on the coordinates of P and Q. The calculation method is as follows:

[0020]

[0021] The direction of the swing angle can be divided into the positive Y-axis and the negative Y-axis.

[0022] According to the above scheme, in step S5, the velocities of P and Q are obtained based on the P and Q coordinates and the scanning interval T at each moment. The calculation method is as follows:

[0023]

[0024]

[0025] The present invention also provides a system for measuring the sway angle and speed of a gantry crane based on lidar, including a data acquisition module, a point cloud processing module, and a sway angle and speed calculation module;

[0026] The data acquisition module controls the lidar to scan a vertical 180-degree direction to acquire three-dimensional point cloud data via commands.

[0027] The point cloud processing module performs pass-through filtering, statistical filtering, Euclidean clustering, and calculates the centroid of point cloud clusters on the point cloud data.

[0028] The swing angle and velocity calculation module calculates the horizontal swing angle and amplitude motion velocity of the suspended weight based on the centroid of the obtained point cloud cluster.

[0029] The method and system for measuring the yaw angle and speed of a gantry crane based on lidar, as described in this invention, have the following beneficial effects:

[0030] 1. This invention uses lidar for measurement, which has high measurement accuracy and strong anti-interference ability. It can achieve a measurement accuracy of 10mm and can also perform accurate measurements in harsh environments such as ports.

[0031] 2. The lidar used in this invention is easy to install and simple to operate, and it is a non-contact measurement method that will not affect the operation of the crane;

[0032] 3. This invention solves the problem that cargo may sway to a certain extent during luffing transport by gantry cranes, which could lead to safety accidents. By measuring the sway angle during the luffing movement of the gantry crane, the data is transmitted to the luffing mechanism control system, which then reduces the sway angle to ensure the safety of the operation. Attached Figure Description

[0033] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0034] Figure 1 This is a flowchart of the method for measuring the sway angle and speed of a gantry crane based on lidar according to the present invention;

[0035] Figure 2 This is a schematic diagram of the installation structure of the lidar of the present invention;

[0036] Figure 3 This is a schematic diagram of the lidar model of the present invention;

[0037] Figure 4 This is a schematic diagram of the working range of the lidar of the present invention;

[0038] Figure 5 This is a schematic diagram of the positive Y-axis yaw of the present invention;

[0039] Figure 6 This is a schematic diagram of the negative Y-axis yaw of the present invention;

[0040] Figure 7 This is a schematic diagram illustrating the speed calculation of the present invention. Detailed Implementation

[0041] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0042] like Figure 1-5 As shown, the method for measuring the yaw angle and speed of a gantry crane based on lidar of the present invention includes the following steps:

[0043] S1. The crane's rotating platform is equipped with a lidar that can scan 180 degrees in the vertical direction, enabling the lidar to acquire point cloud data in front.

[0044] S2. Perform pass-through filtering and statistical filtering on the point cloud data obtained by lidar scanning, and then perform Euclidean clustering on the filtered data to obtain clustered point cloud clusters of elephant trunk and grab bucket.

[0045] The point cloud data undergoes a pass-through filtering process, including: traversing each point in the 3D point cloud data and removing outliers based on a set threshold range to reduce computational complexity. The threshold range is: data within the (-5.0, 5.0) meter range on the X-axis, and all data on the Y and Z axes. Statistical filtering of the point cloud data involves calculating the average distance from each point to its k neighbors (k = 50), then calculating the global sample average distance and standard deviation. A standard range is set based on the mean and standard deviation, and points with average distances outside the standard range are removed, resulting in point cloud data with noise removed. Euclidean clustering of the point cloud data involves performing a KD-Tree nearest neighbor search on a point M in 3D space to obtain the K nearest neighbors. Points with distances less than a threshold are added to a set. Then, a point N is selected from this set, and a KD-Tree nearest neighbor search is performed on it, adding points with distances less than a threshold to the set. This process is repeated until the number of points in the set no longer increases, completing the clustering.

[0046] S3. Obtain the coordinates (X, X) of the endpoint P of the elephant's trunk bridge based on the clustered point cloud data. P Y P Z P The coordinates (X) of the grab's center of mass Q. Q Y Q Z Q );

[0047] Calculate the coordinates (X, X) of the endpoint P of the elephant trunk bridge based on the point cloud clusters obtained from clustering. P Y P Z P The coordinates (X) of the grab's center of mass Q. Q Y Q Z Q The calculation method is as follows:

[0048]

[0049] Among them, (x i y i , z i ), i = 1, 2, ..., N are the three-dimensional coordinates of each point within the point cloud cluster of the elephant's trunk;

[0050]

[0051] Among them, (x j y j , z j ), j = 1, 2, ..., N are the three-dimensional coordinates of each point within the grab bucket point cloud cluster.

[0052] S4. Obtain the horizontal swing angle θ of the suspended weight using the coordinates of P and Q;

[0053] The horizontal swing angle θ of the suspended weight is obtained from the coordinates of P and Q. The calculation method is as follows:

[0054]

[0055] The direction of the swing angle can be divided into the positive Y-axis and the negative Y-axis.

[0056] S5. As the luffing mechanism moves, monitor the coordinates of P and Q in real time and obtain the horizontal velocity v of P and Q at each moment. P v Q ;

[0057] Based on the P and Q coordinates obtained at each moment and the scanning interval T, the velocities of P and Q are obtained. The calculation method is as follows:

[0058]

[0059]

[0060] This invention also provides a system for measuring the sway angle and speed of a gantry crane based on lidar, including a data acquisition module, a point cloud processing module, and a sway angle and speed calculation module. The data acquisition module controls the lidar to scan a vertical 180-degree direction to acquire three-dimensional point cloud data. The point cloud processing module performs pass-through filtering, statistical filtering, Euclidean clustering, and calculates the centroid of the point cloud clusters on the point cloud data. The sway angle and speed calculation module calculates the horizontal sway angle and amplitude-shifting speed of the suspended load based on the obtained centroid of the point cloud clusters.

[0061] In a preferred embodiment of the present invention, the lidar used is the RoboSense lidar, which measures 360 degrees horizontally and -15 to 15 degrees vertically. The lidar communicates with the industrial control computer via Ethernet and transmits 3D measurement data using the UDP protocol. The raw lidar data is parsed using the RoboSense ROS package, establishing the workspace for the proposed algorithm. ROS nodes are created within the workspace to acquire the lidar's 3D point cloud data. The obtained point cloud data contains a large amount of useless data and noise points; therefore, filtering is required to facilitate subsequent operations. This invention employs a two-step process to process point cloud data. The first step involves direct filtering to remove unwanted points. Specifically, each point in the 3D point cloud data is traversed, and outliers are pruned according to a set threshold range to reduce computation. The threshold range is defined as: X-axis points within the range of (-5.0, 5.0) meters, and all data points on the Y and Z axes. The second step, following direct filtering, involves statistical filtering to remove noise points. Specifically, the average distance from each point to its k-neighborhood points is calculated (k is typically set to 50). Then, the global sample average distance and standard deviation are calculated, assuming a Gaussian distribution. A standard range is set based on the mean and standard deviation, and points whose average distance falls outside this range are removed, resulting in point cloud data with noise points removed. After filtering the point cloud data, a relatively good point cloud data can be obtained. Then, the data needs to be clustered. The specific operation is as follows: Search for a point M in the three-dimensional space using KD-Tree nearest neighbor search to obtain K points that are closest to M. Add points whose distance is less than a threshold to the set. Then select a point N in the set and perform KD-Tree nearest neighbor search on this point. Add points whose distance is less than the threshold to the set as well. Repeat this step until the number of points in the set no longer increases, and the clustering is completed.

[0062] In a preferred embodiment of the present invention, the coordinates (X, X) of the endpoint P of the elephant's trunk are calculated based on the point cloud clusters obtained by clustering. P Y P Z P The coordinates (X) of the grab's center of mass Q. Q Y Q Z Q The calculation method is as follows:

[0063]

[0064] Among them, (x i y i , z i ), i = 1, 2, ..., N are the three-dimensional coordinates of each point in the elephant trunk point cloud cluster.

[0065]

[0066] Among them, (x j y j , z j ), j = 1, 2, ..., N are the three-dimensional coordinates of each point within the grab bucket point cloud cluster.

[0067] The horizontal swing angle θ of the suspended weight is obtained from the coordinates of P and Q. The calculation method is as follows:

[0068]

[0069] The direction of the swing angle can be divided into the positive Y-axis and the negative Y-axis. The positive Y-axis swing is as follows: Figure 5 As shown, the negative Y-axis yaw is as follows Figure 6 As shown.

[0070] A diagram illustrating velocity calculation is shown below. Figure 7 As shown, the velocities of P and Q are obtained based on the P and Q coordinates at each moment and the scanning interval T. The calculation method is as follows:

[0071]

[0072]

[0073] In a preferred embodiment of the present invention, a gantry crane yaw angle and speed measurement system based on lidar is also provided, comprising: a lidar installed on the crane and a host computer computing platform. The host computer computing platform includes the following program modules: a data acquisition module, a point cloud processing module, and a yaw angle and speed calculation module. The data acquisition module controls the lidar to scan a vertical 180-degree direction via commands to acquire three-dimensional point cloud data in the direction. The point cloud processing module processes the scanned data accordingly, specifically including: performing pass-through filtering, statistical filtering, Euclidean clustering, and calculating the centroid of the point cloud clusters. The yaw angle and speed calculation module calculates the horizontal yaw angle and variable amplitude speed of the suspended load based on the obtained centroid of the point cloud clusters.

[0074] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for measuring the yaw angle and speed of a gantry crane based on lidar, characterized in that, Includes the following steps: S1. A lidar capable of scanning 180 degrees vertically is installed on the rotating platform of the crane, enabling the lidar to acquire point cloud data in front. S2. Perform pass-through filtering and statistical filtering on the point cloud data obtained by lidar scanning, and then perform Euclidean clustering on the filtered data to obtain clustered point cloud clusters of elephant trunk and grab bucket. S3. Obtain the endpoints of the elephant trunk bridge based on the clustered point cloud data. P coordinates Grab center of gravity Q coordinates ; S4, Pass P , Q The coordinates are used to obtain the horizontal swing angle θ of the suspended weight; S5. Real-time monitoring as the luffing mechanism moves. P , Q Get the coordinates P , Q Horizontal velocity at every moment .

2. The method for measuring the yaw angle and speed of a gantry crane based on lidar according to claim 1, characterized in that, In step S2, the point cloud data is subjected to pass-through filtering, specifically: each point of the three-dimensional point cloud data is traversed, and outliers are removed according to a set threshold range to reduce the amount of computation. The threshold range is: data within the range of (-5.0, 5.0) meters on the X-axis, and all data on the Y-axis and Z-axis.

3. The method for measuring the yaw angle and speed of a gantry crane based on lidar according to claim 2, characterized in that, In step S2, statistical filtering is performed on the point cloud data. Specifically, the average distance from each point to k neighboring points is calculated, where k is 50. Then, the average distance and standard deviation of the global samples are calculated. A standard range is set based on the mean and standard deviation. Points whose average distance is outside the standard range are removed to obtain point cloud data with noise points removed.

4. The method for measuring the yaw angle and speed of a gantry crane based on lidar according to claim 3, characterized in that, In step S2, Euclidean clustering is performed on the point cloud data. Specifically, a KD-Tree nearest neighbor search is used to search for a point M in the three-dimensional space to obtain K points that are closest to M. Points whose distance is less than a threshold are added to the set. Then, a point N is selected from the set, and a KD-Tree nearest neighbor search is performed on this point. Points whose distance is less than the threshold are added to the set. This process is repeated until the number of points in the set no longer increases, thus completing the clustering.

5. The method for measuring the yaw angle and speed of a gantry crane based on lidar according to claim 1, characterized in that, In step S3, the endpoints of the elephant trunk bridge are calculated based on the point cloud clusters obtained from clustering. P coordinates Grab center of gravity Q coordinates The calculation method is as follows: in, , The three-dimensional coordinates of each point within the cloud cluster on the elephant's trunk bridge; in, , The coordinates of each point within the grab bucket point cloud cluster are given.

6. The method for measuring the yaw angle and speed of a gantry crane based on lidar according to claim 1, characterized in that, In step S4, according to P , Q The horizontal swing angle θ of the suspended weight is obtained from the coordinates, and the calculation method is as follows: The direction of the swing angle can be divided into the positive Y-axis and the negative Y-axis.

7. The method for measuring the yaw angle and speed of a gantry crane based on lidar according to claim 1, characterized in that, In step S5, based on the data obtained at each time step... P , Q Given the coordinates and the scan interval T, we obtain... P , Q The speed is calculated as follows: 。

Images