Intelligent decision method for scrap grabbing machine in scrap yard bulk material scene

Abstract

The present application relates to a kind of scrap yard bulk yard scene grab steel machine intelligent decision-making method, belong to grab steel decision-making technical field, comprising: S1: considering single bucket grab rate and single grab moving time, the selection of grabbing point, specifically includes: introducing grabbing window, mass distribution value, the three-dimensional feature distribution of bulk material is analyzed, and single bucket grab rate is calculated;Grabbing time is calculated when grabbing grab steel machine moves;Considering single bucket grab rate and single grab moving time, double-target optimization is carried out, and the point with the highest weighted score is selected as the grabbing point;S2: considering manual operation experience and safety operation rule requirement, the selection of placement point, including the following two selection modes: mode one: for artificial experience, consider placing from the rear of truck bed forward, and select the placement point;Mode two: for safety operation rule requirement, consider the defects of hardware equipment in scrap yard, judge whether there is material pile shielding sensing situation from front to back, and select the placement point.

Description

Technical Field

[0001] This invention belongs to the field of steel grabbing decision technology, and relates to an intelligent decision-making method for steel grabbing machines in bulk material grabbing scenarios in scrap steel yards. Background Technology

[0002] With the continuous advancement of industrialization and urbanization, the demand for steel is constantly expanding. As a crucial downstream base for steel processing, scrap yards are facing increasing operational burdens. Currently, the steel industry is becoming increasingly intelligent, and scrap yards are gradually moving towards intelligent operation, with unmanned environments becoming a new trend. Unmanned scrap yard processing can improve operational efficiency, reduce labor costs, and minimize safety risks associated with manual operation. In the field of scrap processing, unmanned steel grabbers are already widely used. The core of autonomous operation for unmanned steel grabbers lies in automatically selecting the grabbing and placement points for scrap steel.

[0003] The operational efficiency of scrap yards largely depends on the grabbing and placement operations of scrap grabbers. Bulk materials are usually irregularly piled up, characterized by large volume and complex shape. When grabbing, the key is to find a relatively raised point within the grabbing range of the material pile; when placing, the key is to find a safe location in the truck bed to ensure that the scrap does not leak or collapse, thus preventing safety hazards.

[0004] Currently, when manually operating steel grabbers for continuous high-intensity bulk material loading and unloading operations, the following problems may occur:

[0005] (1) Efficiency issues: Manual operation of steel grabbing machines relies too much on experience, and the operation process has a large degree of freedom, making it difficult to ensure that the selection of operation points can be efficient every time;

[0006] (2) Safety issues: Operators face significant safety risks. There may be safety risks such as scrap falling when operating the steel grabber to process scrap steel. Operator fatigue or inconsistent skills may affect the stability and safety of the operation.

[0007] (3) Cost issues: Over-reliance on manual operation leads to high labor costs. Long hours of physical labor not only increase the burden on personnel, but also affect the overall cost-effectiveness. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide an intelligent decision-making method for steel grabbers in bulk material handling scenarios at scrap yards.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A smart decision-making method for steel grabbers in bulk scrap yard scenarios includes:

[0011] S1: Grab Point Selection: Taking into account both the single-bucket grab rate and the single grab movement time, the grab point is selected. Specifically, this includes: introducing the grab window and mass distribution value, analyzing the three-dimensional characteristic distribution of the bulk material, and calculating the single-bucket grab rate; calculating the steel grabber movement time during grabbing; and performing dual-objective optimization by comprehensively considering both the single-bucket grab rate and the single grab movement time, selecting the point with the highest weighted score as the grab point.

[0012] S2: Placement Site Selection: The placement site is selected by comprehensively considering manual operation experience and safety operation requirements, including the following two selection modes:

[0013] Mode 1: Based on human experience, the placement point is selected from the rear of the truck bed to the front.

[0014] Mode 2: In response to the requirements of safe operation rules and considering the defects of hardware equipment in the scrap yard, determine from front to back whether there is any obstruction of the detection by the material pile, and select the placement point accordingly.

[0015] Furthermore, the steps for selecting the grab point are as follows:

[0016] A1: Obtain 3D data of bulk materials;

[0017] A2: Filter the grabbable material pile grid data according to the grabbable range;

[0018] A3: Set the grabbing window size according to the opening area of ​​the grab bucket of the steel grabber, and calculate the characteristic value of the mass distribution of bulk material within the grabbing window;

[0019] A4: By sliding the grab window according to a pattern using a sliding step size, calculate the characteristic value of the bulk material mass distribution in the entire bulk material area;

[0020] A5: Calculate the travel time from the grab point to the placement point;

[0021] A6: Perform dual-objective optimization based on the bulk material mass distribution characteristics of the entire bulk material area and the movement time from the grab point to the placement point, calculate the weighted sum, and output the grab point with the largest score.

[0022] Furthermore, the specific operations for steps A2-A4 are as follows:

[0023] Calculate the distance from the stockpile grid to the steel grabber. If the distance is outside the maximum or minimum operating range of the steel grabber, then remove the stockpile grid data for that grid, thus filtering the stockpile grid data.

[0024] Fill the selected stockpile grid data into the height vector;

[0025] A grabbing window is generated based on the size of the grab bucket of the steel grabber. The sliding step size is fixed. Starting from the lower left corner of the height matrix, the window slides from left to right and from bottom to top to find the grabbing window information with the largest value, which is the optimal grabbing window information.

[0026] Furthermore, the step of selecting the placement point is as follows:

[0027] B1: Acquire real-time data of the stockpile, truck location information, and steel grabber information, and initialize them;

[0028] B2: Calculate the points on the central axis of the truck bed based on a certain sliding step length;

[0029] B3: Update the available placement point information based on the maximum and minimum operating range of the steel grabber;

[0030] B4: Determine if the truck is full. If it is full, stop the calculation. Otherwise, select the placement point generation mode as needed, including mode one and mode two.

[0031] Mode 1 calculates the average height of the grid within the placement window from back to front, determines whether it exceeds the threshold, and if it does, slides forward to continue the calculation; otherwise, it outputs the point as the placement point.

[0032] Mode 2 involves checking from the front to the back of the carriage whether the material pile inside the placement window exceeds the carriage height threshold. If it does, it is considered to be blocking the data behind, and the previous point is selected as the placement point.

[0033] Furthermore, during the selection of grab points and placement points, after obtaining the grid data, the system first checks whether the data source is correct and whether there are any outliers. If the data source is incorrect or there are outliers, the process ends and errors are handled first.

[0034] The beneficial effects of this invention are as follows:

[0035] Independence. This technology requires no human intervention and can automatically generate suitable gripping and placement points based on the input data, enabling the unmanned steel gripper to operate independently and continuously.

[0036] High efficiency. This technology has been tested and verified to ensure the correctness and effectiveness of the grabbing and placement points, which can improve the efficiency of autonomous operation of unmanned steel grabbers and reduce labor costs.

[0037] Safety. This technology enables unmanned operation of scrap metal yards, reducing the safety hazards associated with manual operations.

[0038] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0040] Figure 1 Flowchart for selecting capture points;

[0041] Figure 2 Here is a flowchart for calculating the capture points;

[0042] Figure 3 Write a flowchart to the file;

[0043] Figure 4 Flowchart for grid data inspection;

[0044] Figure 5 To update the stockpile data flow chart;

[0045] Figure 6 Flowchart for filling height data;

[0046] Figure 7 Flowchart for optimal window filtering;

[0047] Figure 8 To calculate the data flow diagram for all capture windows;

[0048] Figure 9 Flowchart for calculating feature values ​​of a single capture window;

[0049] Figure 10 Flowchart for calculating feature values ​​of the captured sub-window;

[0050] Figure 11 Flowchart for calculating feature values ​​of the capture window;

[0051] Figure 12 Flowchart for selecting placement points;

[0052] Figure 13 Flowchart for placement point calculation;

[0053] Figure 14 Flowchart for grid data inspection;

[0054] Figure 15 Here is a flowchart for point calculation;

[0055] Figure 16The flowchart is used to determine whether the boundary has been exceeded.

[0056] Figure 17 Flowchart for determining if a truck is full;

[0057] Figure 18 This is the flowchart for Mode 1;

[0058] Figure 19 Flowchart for location determination;

[0059] Figure 20 This is the flowchart for Mode 2;

[0060] Figure 21 Write a flowchart to the file;

[0061] Figure 22 A visual illustration of the crawl point test;

[0062] Figure 23 A visual diagram for testing the placement point. Detailed Implementation

[0063] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0064] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0065] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0066] Example 1:

[0067] This invention provides an intelligent decision-making method for unmanned steel grabbers in bulk material scenarios at scrap yards. Addressing the selection of grabbing and placement points for unmanned steel grabbers in bulk material scenarios, it designs two algorithms: a grabbing point optimization algorithm based on three-dimensional feature distribution and a placement point optimization algorithm based on manual rules, thereby optimizing the unmanned steel grabber operation process.

[0068] When selecting gripping points, this algorithm comprehensively considers both the single-bucket gripping rate and the single-grab movement time to improve operational efficiency. For the single-bucket gripping rate, the algorithm introduces concepts such as gripping window and mass distribution value to analyze the three-dimensional characteristic distribution of bulk materials. Simultaneously, it considers the movement time of the steel grabber during gripping, comprehensively considering both static and dynamic data of the steel grabber, and performing dual-objective optimization for these two indicators.

[0069] When selecting placement points, this algorithm comprehensively considers both human experience and safety regulations. Regarding human experience, it considers placing points from the rear of the truck bed towards the front. Regarding safety regulations, considering the limitations of hardware equipment in scrap yards, it proposes optimizing placement points by checking from front to back for any obstructions from material piles.

[0070] This invention enables unmanned steel grabbers to quickly generate suitable grab and placement points during autonomous operation, improving overall workflow efficiency. During operation, no human intervention is required. Based on static data such as material pile data and dynamic data such as the grabber's position and posture, the grabber can autonomously output selected grab and placement points, achieving autonomous operation. When selecting grab points, the three-dimensional feature information of the material pile is primarily considered, identifying the point with the highest degree of protrusion as a candidate grab point. When selecting placement points, both human experience and actual operational safety rules are considered to ensure correct placement without safety hazards.

[0071] Example 2:

[0072] In this embodiment, the overall process of the capture point selection algorithm is as follows: Figure 1 As shown, the calculation of the grab point comprehensively considers both the single-bucket grab rate and the single grab movement time. First, 3D data of the bulk material is acquired. Based on the grabbable range, the grid data of the grabbable material pile is filtered out. The grab window size is set according to the opening area of ​​the grabber bucket, and the characteristic values ​​of the bulk material mass distribution within the grab window are calculated. The grab window is slid along a defined path using a sliding step size to cover and record the entire bulk material area. Regarding the single grab movement time, the algorithm comprehensively considers both static and dynamic data of the grabber to calculate the movement time from the grab point to the placement point. A dual-objective optimization is performed on these two indicators, and the grab point with the largest weighted sum is output.

[0073] The algorithm flow for selecting capture points is as follows: Figure 2As shown. The grab point algorithm first acquires inputs including empty coordinates, error messages, truck and stockpile grid data, and feed points, and saves all data to a file. Next, it calculates the correctness of the input grid data values. If the input is an outlier, it outputs an error message and terminates the calculation; otherwise, it updates the stockpile data based on the maximum and minimum operating range of the grabber. The updated stockpile grid data is then filled into the height vector and stored. A grab window is generated based on the size of the grabber's grab bucket. With a fixed sliding step size, it slides from the lower left corner of the height matrix, moving from left to right and from bottom to top, to obtain the optimal grab window information. The updated grid data, grab point information, truck and other data are stored in a file, and the calculation ends.

[0074] In one specific embodiment, the file writing process is as follows: Figure 3 As shown, when scene data is obtained, it needs to be written to a file for storage to facilitate subsequent archiving and recording.

[0075] In one specific embodiment, the grid data inspection process is as follows: Figure 4 As shown, after obtaining the grid data, it is necessary to determine whether the data source is correct and to process it by checking for outliers.

[0076] In one specific embodiment, the process for updating stockpile data is as follows: Figure 5 As shown, the grid data is processed, and the grid information within the working range of the steel grabber is filtered and saved according to the working range of the steel grabber, while the rest of the information is removed.

[0077] In one specific embodiment, the height data filling process is as follows: Figure 6 As shown, the stockpile data is filled into a two-dimensional height matrix for easy subsequent use.

[0078] In one specific embodiment, the optimal window filtering process is as follows: Figure 7 As shown, firstly, the feature value data of all grabbing windows are calculated, and then the data of the optimal grabbing window is obtained. Specifically, the calculation of all grabbing hook information involves calculating the data of all grabbing windows according to the sliding step size rule, as shown below. Figure 8 As shown, calculating the capture window information represents calculating the feature value data of each capture window. The specific process is as follows: Figure 9 As shown. The process for calculating the feature value of each capture sub-window is as follows: Figure 10 As shown, the process for calculating the feature values ​​of the capture window is as follows: Figure 11 As shown.

[0079] Example 3:

[0080] In this embodiment, the placement point selection process is as follows: Figure 12As shown, when selecting placement points, the system first initializes the data by acquiring real-time data of the material pile, truck location information, and steel grabber information. Then, it calculates the points on the centerline of the truck bed based on a certain sliding step size. Next, it updates the available placement point information based on the maximum and minimum operating range of the steel grabber. The system checks if the truck is full; if so, the calculation stops. Otherwise, it selects a placement point generation mode as needed. Mode 1 calculates the average height of the grid within the placement window from back to front, checking if it exceeds a threshold. If it does, it slides forward to continue calculating; otherwise, it outputs the point. Mode 2 checks if the material pile within the placement window exceeds the truck bed height threshold from front to back. If it does, it's considered to obscure subsequent data, and the previous point is selected as the placement point, with the information stored in a file.

[0081] Placement point selection algorithm as follows Figure 13 As shown, the correctness of the input grid data value is first calculated. If the input is an abnormal value, an error message is output and the calculation ends. Otherwise, the points on the centerline of the truck are calculated to determine whether the truck is full. If it is full, the calculation stops. Otherwise, the corresponding process is performed according to the placement point generation mode. Finally, the placement point information is output.

[0082] In one specific embodiment, the grid data inspection process is as follows: Figure 14 As shown, after obtaining the grid data, it is necessary to determine whether the data source is correct and to process it by checking for outliers.

[0083] In one specific embodiment, the point calculation process is as follows: Figure 15 As shown, the points on the central axis of the truck bed are calculated and kept for later use. Figure 15 The process for determining whether the window placement position exceeds the carriage boundary is as follows: Figure 16 As shown.

[0084] In one specific embodiment, the process for determining whether a truck is full is as follows: Figure 17 As shown, the calculation and determination of whether the material pile data inside the truck bed exceeds the height of the truck bed is performed, where the determination of whether the grid has intersections represents whether the grid is within the rectangle.

[0085] In one specific embodiment, such as Figure 18 As shown, Mode 1 involves determining whether a placement point has been found by working backwards from the back of the carriage, and checking for grid intersections. Figure 19 As shown.

[0086] In one specific embodiment, such as Figure 20 As shown, Mode 2 determines whether the number of grids exceeding the height of the truck bed exceeds a threshold, and then optimizes the placement points from back to front.

[0087] In one specific embodiment, the file writing process is as follows: Figure 21As shown, the data is written to a file for storage, which facilitates subsequent archiving and recording.

[0088] In one specific embodiment, actual tests were conducted on the selection of gripping points, and the algorithm was verified for material piles of different sizes and shapes, such as... Figure 22 As shown, red represents the position of the grabbing window, and the colored 3D broken line represents the material pile. It can be seen that the grabbing window is selected at the protruding part of the material pile, which meets the grabbing efficiency requirements and the operating range limitations.

[0089] During actual testing at the placement points, algorithmic verification was performed on data from different angles and different load levels in the carriages. For example... Figure 23 As shown, the red rectangle represents the placement window, the blue rectangle represents the carriage, and the small grid inside represents the existing material pile inside the carriage, which shows that the safe placement requirements are met.

[0090] Example 4:

[0091] An electronic device, including a memory and a processor;

[0092] The memory is used to store computer programs;

[0093] The processor is configured to implement the method described in Embodiment 1 when executing the computer program.

[0094] Example 5:

[0095] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0096] Example 6:

[0097] A computer program product includes a computer program that, when executed by a processor, implements the method described in Example 1.

[0098] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances of "this embodiment" do not necessarily refer to the same embodiment.

[0099] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0100] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0101] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.

[0102] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0103] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0104] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0105] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart decision-making method for steel grabbers in bulk material handling scenarios at scrap yards, characterized in that: include: S1: Grab Point Selection: Taking into account both the single-bucket grab rate and the single-grab movement time, the grab point is selected. Specifically, this includes: introducing a grab window and mass distribution values ​​to analyze the three-dimensional characteristic distribution of the bulk material and calculating the single-bucket grab rate; calculating the steel grabber movement time during grabbing; and performing dual-objective optimization by comprehensively considering both the single-bucket grab rate and the single-grab movement time, selecting the point with the highest weighted score as the grab point. The grab point selection steps are as follows: A1: Obtain 3D data of bulk materials; A2: Filter the grabbable material pile grid data according to the grabbable range; A3: Set the grabbing window size according to the opening area of ​​the grab bucket of the steel grabber, and calculate the characteristic value of the mass distribution of bulk material within the grabbing window; A4: By sliding the grab window according to a pattern using a sliding step size, calculate the characteristic value of the bulk material mass distribution in the entire bulk material area; A5: Calculate the travel time from the grab point to the placement point; A6: Perform dual-objective optimization based on the bulk material mass distribution characteristics of the entire bulk material area and the movement time from the grab point to the placement point, calculate the weighted sum, and output the grab point with the largest score; S2: Placement Site Selection: The placement site is selected by comprehensively considering manual operation experience and safety operation requirements, including the following two selection modes: Mode 1: Based on human experience, the placement point is selected from the rear of the truck bed to the front. Mode 2: In response to the requirements of safe operation rules and considering the defects of hardware equipment in the scrap yard, determine from front to back whether there is any obstruction of the detection by the material pile, and select the placement point accordingly; The steps for selecting the placement point are as follows: B1: Acquire real-time data of the stockpile, truck location information, and steel grabber information, and initialize them; B2: Calculate the points on the centerline of the truck bed based on a certain sliding step length; B3: Update the available placement point information based on the maximum and minimum operating range of the steel grabber; B4: Determine if the truck is full. If it is full, stop the calculation. Otherwise, select the placement point generation mode as needed, including mode one and mode two. Mode 1 calculates the average height of the grid within the placement window from back to front, checks if it exceeds the threshold, and if it does, slides forward to continue the calculation; otherwise, it outputs the current point as the placement point. Mode 2 involves checking from the front to the back of the carriage whether the material pile inside the placement window exceeds the carriage height threshold. If it does, it is assumed that it will block the data behind it, and the previous point is selected as the placement point.

2. The intelligent decision-making method for steel grabbers in bulk scrap yard scenarios according to claim 1, characterized in that: The specific steps for A2-A4 are as follows: Calculate the distance from the stockpile grid to the steel grabber. If the distance is outside the maximum or minimum operating range of the steel grabber, then remove the stockpile grid data for that grid, thus filtering the stockpile grid data. Fill the selected stockpile grid data into the height vector; A grabbing window is generated based on the size of the grab bucket of the steel grabber. The sliding step size is fixed. Starting from the lower left corner of the height matrix, the window slides from left to right and from bottom to top to find the grabbing window information with the largest mass distribution characteristic value of the bulk material. This is the optimal grabbing window information.

3. The intelligent decision-making method for steel grabbers in bulk material handling scenarios at scrap steel yards according to claim 1, characterized in that: During the selection of grab points and placement points, after obtaining grid data, the system first checks whether the data source is correct and whether there are any outliers. If the data source is incorrect or there are outliers, the process ends and errors are handled first.

4. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the intelligent decision-making method for steel grabbing machines in bulk material scenarios at scrap yards as described in any one of claims 1-3 when executing the computer program.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the intelligent decision-making method for steel grabbing machines in bulk material scenarios at scrap yards as described in any one of claims 1-3.

6. A computer program product, characterized in that: The invention includes a computer program that, when executed by a processor, implements the intelligent decision-making method for steel grabbers in a bulk material handling scenario at a scrap yard as described in any one of claims 1-3.

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