A cable machine material automatic grabbing method and system
Patent Information
- Application Number
- CN202611117179.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-27
- Publication Date
- 2026-10-16
AI Technical Summary
[0006]本发明针对现有技术中的缺点，提供了一种缆机物料自动抓取方法、系统，解决了现有缆机物料抓取识别的精度较低的问题
[0039] By dynamically calculating the current signal to estimate the position, the problem of the delay error of the satellite signal in the ionosphere and troposphere changing rapidly over time when passing through the atmosphere (especially when solar activity is intense) is avoided. Specifically, the current signal to estimate the position is dynamically calculated through a time-signal position prediction model, which avoids the problem that the error correction amount at a single moment cannot provide a reliable basis for the traction trolley to make position corrections, thereby improving the accuracy of grasping and recognition.
Smart Images
Figure CN122915973A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of cable crane transportation technology, and specifically to an automatic material grabbing method and system for cable cranes. Background Technology
[0002] Cable cranes (cable hoists) are cranes that use load-bearing cables tensioned between the main and auxiliary towers as the track for the load-bearing trolley. They have a large load capacity and high transportation efficiency, and are widely used in construction operations such as the pouring of high concrete dams in water conservancy projects, the installation of electromechanical equipment, the installation and dismantling of large formwork, and the hoisting of equipment and materials. The efficiency and safety control of the cable crane transportation process are key to cable crane operation management, while the identification and positioning of the types of materials hoisted by the cable crane are the foundation for achieving automated hoisting and intelligent construction management.
[0003] Currently, the identification and handling of materials in cable cranes mainly rely on the following technical means:
[0004] First, material category identification based on weighing sensors. In existing technologies, some cable cranes use weighing sensors installed at the hook balance beam and hook position to collect weight signals during the lifting process and infer the category of the lifted object. However, this method can only roughly classify materials by weight, cannot identify materials with similar shapes but different weights, and is difficult to handle mixed lifting scenarios.
[0005] Second, positioning monitoring based on GNSS and travel encoders. Existing monitoring technologies for cable crane hoisting mainly include Global Navigation Satellite System (GNSS), travel encoders, and accelerometers. GNSS captures the movement trajectory of the hoisted container by locating its position. However, because cable crane spans can reach hundreds to thousands of meters, the flexible steel cable exhibits significant nonlinear elastic deformation and thermal elongation under its own weight and load. The "theoretical position" calculated by the encoder often has an error of 20-50 cm or even greater than the actual position of the traction trolley. The system cannot autonomously and accurately deliver the hook above the material, requiring repeated manual "jogging" adjustments. Summary of the Invention
[0006] This invention addresses the shortcomings of existing technologies by providing an automatic material grabbing method and system for cable cranes, thus solving the problem of low accuracy in material grabbing and identification in existing cable cranes.
[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:
[0008] An automatic material handling method for cable cranes includes the following steps:
[0009] Obtain the coordinates of the material area and receive the grabbing task request;
[0010] The validity of the grabbing area is verified based on the grabbing task request and the material area coordinates. If the verification passes, the grabbing task request is executed, and the target area is located based on differential correction.
[0011] Scan the target area location to obtain the corresponding 3D point cloud data.
[0012] The three-dimensional point cloud data is filtered, clustered, and feature-recognized to identify the target material. If the target material is identified, the center position of the target material is obtained, and the lifting device is adjusted to move above the center position of the target material to trigger the grasping action.
[0013] If no target material is identified, a "No Target Material" report will be generated.
[0014] Optionally, locating the target area includes the following steps:
[0015] A reference base station with known precise coordinates is set, and the current signal-estimated position is dynamically calculated based on the satellite signal of the reference base station. Based on the current signal-estimated position and the known precise coordinates of the reference base station, a comprehensive error correction amount is calculated.
[0016] The traction trolley is differentially corrected based on the comprehensive error correction amount, and the spreader is moved to the target area position based on the corrected current position of the trolley and the target area position.
[0017] Optionally, the overall error correction is calculated, including the following steps:
[0018] Construct an initial model for estimating location based on time and signal, and obtain the signal estimation location corresponding to each moment based on the timestamp to obtain historical data;
[0019] The historical data is randomly divided into a training set, a test set, and a validation set. The initial model for time-signal location estimation is trained based on the training set to obtain a time-signal location estimation training model.
[0020] Based on the test set, the loss function of the time-signal location estimation training model is corrected to obtain the time-signal location estimation optimization model.
[0021] The time-signal location estimation optimization model is validated based on the validation set. After the validation is passed, the time-signal location estimation prediction model is obtained.
[0022] Obtain the current time and output the current signal-estimated position based on the current time using a time-signal position prediction model;
[0023] Based on the current signal-estimated location and the known precise coordinates of the reference base station, the comprehensive error correction amount is calculated.
[0024] Optionally, the 3D point cloud data is filtered, including the following steps:
[0025] Statistical filtering or radius filtering is applied to the three-dimensional point cloud data to remove isolated noise points far from the subject, resulting in coarsely filtered point cloud data.
[0026] The coarse-filtered point cloud data is downsampled using a voxel grid filter, and then smoothed using a bilateral filtering algorithm to obtain fine-filtered point cloud data.
[0027] Optionally, the filtered 3D point cloud data is subjected to clustering processing, including the following steps:
[0028] The finely filtered point cloud data is clustered using Euclidean clustering to obtain clustered point cloud data.
[0029] Optionally, feature recognition is performed on the clustered 3D point cloud data, including the following steps:
[0030] Obtain the material library and perform feature recognition based on the clustered point cloud data and the material library to obtain the target material.
[0031] Optionally, the validity verification of the capture area includes: verifying whether the capture area exceeds the reachable working range of the cable crane, whether there are prohibited areas in the capture area, and whether the capture area conflicts with the operating areas of other equipment.
[0032] Optionally, after triggering the gripping action, the system may also collect a gripping position signal, an applied pressure signal, and a motor current signal to determine whether the gripping was successful.
[0033] An automatic material grabbing system for cable cranes, wherein the automatic material grabbing system for cable cranes executes the automatic material grabbing method for cable cranes as described in any of the above, comprising an RYK-GPS / BeiDou positioning module (positioning module), a position encoder, and a lidar module, wherein the positioning module is installed on a traction trolley, the position encoder is installed on the running mechanism of the cable crane, and the lidar module is installed on the hook beam of the cable crane;
[0034] The positioning module receives the differential signal sent by the RTK base station through the bridge, and performs error correction based on the differential signal;
[0035] The position encoder acquires the position changes in the traction direction, taxiing direction, and lifting direction.
[0036] The radar-laser module is used to acquire three-dimensional point cloud data of the target material.
[0037] A computer storage medium storing computer program instructions; when the computer program instructions are executed by a processor, the automatic material grabbing method for cable cranes as described above is implemented.
[0038] Compared with the prior art, the technical solution provided by this invention has the following advantages:
[0039] By dynamically calculating the current signal to estimate the position, the problem of the delay error of the satellite signal in the ionosphere and troposphere changing rapidly over time when passing through the atmosphere (especially when solar activity is intense) is avoided. Specifically, the current signal to estimate the position is dynamically calculated through a time-signal position prediction model, which avoids the problem that the error correction amount at a single moment cannot provide a reliable basis for the traction trolley to make position corrections, thereby improving the accuracy of grasping and recognition. Attached Figure Description
[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Figure 1 This is a flowchart of an automatic material grabbing method for cable cranes proposed in Embodiment 1. Detailed Implementation
[0042] The present invention will be further described in detail below with reference to the embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.
[0043] Example 1
[0044] like Figure 1 An automatic material grabbing method for cable cranes includes the following steps: obtaining the coordinates of the material area and receiving a grabbing task request; performing a legality check on the grabbing area based on the grabbing task request and the coordinates of the material area; if the check passes, executing the grabbing task request and locating the target area.
[0045] The process of locating the target area includes the following steps: setting a reference base station with known precise coordinates, dynamically calculating the current signal position based on the satellite signal of the reference base station, calculating the comprehensive error correction amount based on the current signal position and the known precise coordinates of the reference base station, performing differential correction on the traction trolley based on the comprehensive error correction amount, and moving the spreader to the target area position based on the corrected current position of the trolley and the target area position.
[0046] However, the correction values broadcast by the base station have a limited shelf life. As satellite signals pass through the atmosphere, the delay errors in the ionosphere and troposphere change rapidly over time (especially during periods of intense solar activity). Therefore, the error correction value at a single moment cannot provide a reliable basis for the traction trolley's position correction. Based on this, this embodiment improves the reliability of the traction trolley's position correction through the following methods, the specific steps of which are as follows:
[0047] An initial time-signal location estimation model is constructed, and the estimated signal location for each time point is obtained based on the timestamp, yielding historical data. This historical data is randomly divided into training, testing, and validation sets. The initial time-signal location estimation model is trained using the training set to obtain a trained time-signal location estimation model. The training model is then modified using a loss function based on the testing set to obtain an optimized time-signal location estimation model. The optimized model is validated using the validation set, and a predicted time-signal location estimation model is obtained upon successful validation. The current time is obtained, and the estimated signal location is output using the predicted time-signal location estimation model. Finally, the comprehensive error correction is calculated based on the current estimated signal location and the known precise coordinates of the reference base station.
[0048] This allows for dynamic calculation of the current signal-based position prediction model, avoiding the problem that the error correction amount at a single moment cannot provide a reliable basis for the traction trolley to correct its position.
[0049] In one specific embodiment of automatic positioning, the operator can input the coordinates of the area to be grabbed via a host computer (which can be directly obtained in the early stages). The coordinates of the area to be grabbed are defined using three-dimensional spatial coordinates. After receiving the task specification, the main control PLC performs a valid verification of the input area. The valid verification includes: whether it exceeds the reachable working range of the cable crane, whether there is a prohibited area, and whether it conflicts with the operating area of other equipment. If the verification passes, the driver presses the button to grab the material, and the cable crane's automatic operation program is started.
[0050] The RTK-GPS / BeiDou positioning module obtains the current position coordinates of the traction trolley and performs differential correction in conjunction with the base station in the driver's cab to reduce positioning errors caused by slippage of the traction rope and nonlinear changes in the wire rope due to temperature differences, thereby improving the positioning accuracy of the traction trolley.
[0051] Based on the deviation between the current position and the target area position, the main control PLC controls the following: the traction mechanism to run along the X direction, the trolley mechanism to run along the Y direction, and the hoisting mechanism to adjust its height along the Z direction, until the lifting device runs to the preset identification position above the target area. The positioning accuracy is controlled within ±5cm in the horizontal direction and ±3cm in the vertical direction.
[0052] Next, material identification and coordinate calculation are performed, with the following steps: Scan the target area to obtain the 3D point cloud data corresponding to the target area; perform filtering, clustering, and feature recognition processing on the 3D point cloud data to identify the target material; if the target material is identified, obtain the center position of the target material and adjust the lifting device to move above the center position of the target material to trigger the grabbing action; if the target material is not identified, generate a no-target-material report.
[0053] Specifically, when the cable car reaches the target area, the main control PLC sends a scanning command to the lidar. The lidar scans the target area and collects three-dimensional point cloud data. The control system identifies the target material by filtering, clustering and feature recognition of the point cloud data. First, filtering is used to ensure that the data is clean. Then, clustering is used to organize meaningless points into meaningful "object hypotheses". Finally, feature recognition is used to label these objects.
[0054] Filtering is necessary because the raw radar data contains a lot of noise, such as isolated noise points or unwanted objects in the distance. These interferences need to be removed, and only valid points are retained. Clustering is necessary because after filtering, the remaining point cloud is still just a bunch of disordered points. Therefore, clustering is needed to group points belonging to the same object into a "cluster" based on their spatial relationships. Feature recognition is necessary because after the clusters are formed, the system needs to identify what object each "cluster" corresponds to, such as a car, a pedestrian, or a tree. This is achieved through feature recognition.
[0055] In this embodiment, the identification method includes, but is not limited to: point cloud contour matching, height difference analysis, volume feature analysis, and edge feature recognition. After the target material is identified, the system calculates the coordinates of the material's center point and converts them into the following coordinates in the cable car coordinate system: X-direction offset (traction direction), Y-direction offset (trolley direction), and Z-direction height offset (lifting direction).
[0056] Specifically, the material being identified (a dedicated hopper, capable of storing tools and other materials for transport) is first analyzed using LiDAR to collect and analyze point cloud data, obtaining feature information. During subsequent use, identification is based on actual features. If similar features are detected, it is considered a target material, and the coordinates of the current cableway to the target material are output. The cableway PLC converts this information into direction in the cableway coordinate system. The first step involves raw point data acquisition and generation. The second step involves preprocessing, filtering, and ground segmentation to obtain a cleaned, simplified "foreground point cloud" with ground points removed. The third step involves object detection and segmentation to obtain a set of point cloud clusters, each cluster representing an independent, candidate object. The fourth step compares the previously trained data with the currently detected data to determine if it is a material.
[0057] The calculation results are then sent to the main control PLC via a wireless communication module. The main control PLC automatically corrects the cable crane position based on the offset, moving the lifting device directly above the material. If the lidar fails to detect material information, the system generates an abnormal status signal and displays a "Material Not Detected" message on the host computer interface, allowing operators to intervene manually. Compared to traditional manual visual positioning methods, this invention effectively improves material positioning accuracy and the success rate of material grabbing.
[0058] Finally, during the grabbing process, the gripper mechanism is driven by a jog actuator, which can perform two-dimensional planar fine-tuning in the X direction (traction direction) and Y direction (trolley direction). When the gripper moves directly above the material, the main control PLC controls the lifting mechanism to descend slowly. During the descent, the bottom-touching sensor signal on the gripper is monitored in real time. When the bottom-touching signal is triggered, the descent stops and the grabbing action is triggered. The gripper control PLC controls the special gripper to close and clamp.
[0059] Simultaneously, the system collects the gripping position signal, clamping pressure signal, and motor current signal to determine whether the gripping is successful. If the gripping fails, it automatically performs a second positioning gripping. If the maximum number of attempts is exceeded and the gripping is still unsuccessful, the system outputs a fault alarm message.
[0060] Example 2
[0061] An automatic material handling system for cable cranes includes a positioning module, a position encoder, and a lidar module. The positioning module is mounted on a traction trolley, the position encoder is mounted on the cable crane's running mechanism, and the lidar module is mounted on the cable crane's hook beam. The positioning module receives differential signals transmitted by an RTK base station via a network bridge and performs error correction based on the differential signals. The position encoder acquires the positional changes in the traction direction, the lifting direction, and the hoisting direction. The lidar module is used to acquire three-dimensional point cloud data of the target material.
[0062] Specifically, the automatic gripping system implemented here mainly includes: cable crane main unit, lifting beam, RTK-GPS / BeiDou positioning module, position encoder, laser radar identification module, main control PLC, lifting control PLC, wireless communication module, special gripping mechanism, special material (special hopper, which can store the tools and materials to be transported), host computer control system, power supply and battery module.
[0063] The connection relationships of each module are as follows: The RTK-GPS / BeiDou positioning mobile station module is installed on the traction trolley to obtain the absolute spatial coordinate information of the traction trolley in real time. An RTK base station is set up near the driver's cab. Through differential correction (the RTK base station transmits differential signals to the RTK-GPS / BeiDou positioning module installed on the traction trolley through a network bridge to improve the positioning accuracy of the traction trolley (RTK-GPS / BeiDou positioning module is configured to output differential signals from the base station, and the mobile station receives differential signals to improve positioning accuracy). The base station is fixed, and the corresponding mobile positioning module on the traction trolley is used to eliminate errors caused by slippage, temperature changes, etc. of the traction trolley), so that the mobile station data is accurate. The position encoder is installed on the cable crane running mechanism to obtain the position changes in the traction direction, the trolley direction, and the lifting direction.
[0064] The lidar is installed below the hook beam, with its scanning direction facing the material area, to acquire the three-dimensional point cloud data of the target material. The main control PLC is connected to the cable crane traveling mechanism, lifting mechanism, lidar, wireless communication module and lifting device control PLC to execute motion control and grasping logic. The wireless communication module is used for data interaction between the main control PLC and the lifting device control PLC.
[0065] It should be noted that since the automatic cable crane material grabbing system in this embodiment executes the automatic cable crane material grabbing method as shown in Embodiment 1, it will not be repeated in this embodiment.
[0066] A computer storage medium storing computer program instructions; when the computer program instructions are executed by a processor, the automatic material grabbing method for cable cranes as shown in Embodiment 1 is implemented.
[0067] More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0068] In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical cables, RF, etc., or any suitable combination thereof.
[0069] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules, units, or units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units, modules, or components may be combined or integrated into another device, or some features may be ignored or not executed.
[0070] The units may or may not be physically separate. The components shown as units can be one or more physical units, meaning they can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0071] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0072] In particular, according to embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0074] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0075] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any form or substance. It should be noted that those skilled in the art can make various improvements and additions without departing from the method of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention. Any modifications, alterations, and equivalent changes made by those skilled in the art based on the above-disclosed technical content without departing from the spirit and scope of the present invention are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, and evolutions made to the above embodiments based on the essential technology of the present invention still fall within the scope of the technical solution of the present invention.
Claims
1. A method for automatically grasping materials for a cable crane, characterized in that, Includes the following steps: Obtain the coordinates of the material area and receive the grabbing task request; The validity of the grabbing area is verified based on the grabbing task request and the material area coordinates. If the verification passes, the grabbing task request is executed, and the target area is located based on differential correction. Scan the target area location to obtain the corresponding 3D point cloud data. The three-dimensional point cloud data is filtered, clustered, and feature-recognized to identify the target material. If the target material is identified, the center position of the target material is obtained, and the lifting device is adjusted to move above the center position of the target material to trigger the grasping action. If no target material is identified, a "No Target Material" report will be generated.
2. The automatic material gripping method for cable cranes according to claim 1, characterized in that, The location of the target area includes the following steps: A reference base station with known precise coordinates is set, and the current signal-estimated position is dynamically calculated based on the satellite signal of the reference base station. Based on the current signal-estimated position and the known precise coordinates of the reference base station, a comprehensive error correction amount is calculated. The traction trolley is differentially corrected based on the comprehensive error correction amount, and the spreader is moved to the target area position based on the corrected current position of the trolley and the target area position.
3. The automatic material gripping method for cable cranes according to claim 2, characterized in that, The calculation of the overall error correction includes the following steps: Construct an initial model for estimating location based on time and signal, and obtain the signal estimation location corresponding to each moment based on the timestamp to obtain historical data; The historical data is randomly divided into a training set, a test set, and a validation set. The initial model for time-signal location estimation is trained based on the training set to obtain a time-signal location estimation training model. Based on the test set, the loss function of the time-signal location estimation training model is corrected to obtain the time-signal location estimation optimization model. The time-signal location estimation optimization model is validated based on the validation set. After the validation is passed, the time-signal location estimation prediction model is obtained. Obtain the current time and output the current signal-estimated position based on the current time using a time-signal position prediction model; Based on the current signal-estimated location and the known precise coordinates of the reference base station, the comprehensive error correction amount is calculated.
4. The automatic material gripping method for cable cranes according to claim 1, characterized in that, The filtering process for the 3D point cloud data includes the following steps: Statistical filtering or radius filtering is applied to the three-dimensional point cloud data to remove isolated noise points far from the subject, resulting in coarsely filtered point cloud data. The coarse-filtered point cloud data is downsampled using a voxel grid filter, and then smoothed using a bilateral filtering algorithm to obtain fine-filtered point cloud data.
5. The automatic material gripping method for cable cranes according to claim 4, characterized in that, The filtered 3D point cloud data is subjected to clustering processing, including the following steps: The finely filtered point cloud data is clustered using Euclidean clustering to obtain clustered point cloud data.
6. The automatic material gripping method for cable cranes according to claim 5, characterized in that, Feature recognition is performed on the clustered 3D point cloud data, including the following steps: Obtain the material library and perform feature recognition based on the clustered point cloud data and the material library to obtain the target material.
7. The automatic material gripping method for cable cranes according to claim 1, characterized in that, The validity verification of the capture area includes: verifying whether the capture area exceeds the reachable working range of the cable crane, whether there are prohibited areas in the capture area, and whether the capture area conflicts with the operating areas of other equipment.
8. The automatic material gripping method for cable cranes according to claim 1, characterized in that, After the gripping action is triggered, the system also collects the gripping position signal, the applied pressure signal, and the motor current signal to determine whether the gripping was successful.
9. An automatic material gripping system for cable cranes, characterized in that, The cable crane material automatic grasping system executes the cable crane material automatic grasping method as described in any one of claims 1-8, including an RYK-GPS / Beidou positioning module (positioning module), a position encoder, and a lidar module. The positioning module is installed on the traction trolley, the position encoder is installed on the cable crane's running mechanism, and the lidar module is installed on the cable crane's hook beam. The positioning module receives the differential signal sent by the RTK base station through the bridge, and performs error correction based on the differential signal; The position encoder acquires the position changes in the traction direction, taxiing direction, and lifting direction. The radar-laser module is used to acquire three-dimensional point cloud data of the target material.
10. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, they implement the automatic material grabbing method for cable cranes as described in any one of claims 1-8.