Material carrying equipment control instruction generation method and device, equipment and storage medium

Abstract

The invention provides a material carrying equipment control instruction generation method and device, equipment and a storage medium, and relates to the technical field of information. The method comprises the steps that material storage device parameters and material carrying equipment parameters are obtained, wherein the material storage device parameters are used for indicating specification and position information of a material storage device and material storage real-time state information; according to the parameters of the material storage device and the parameters of the material carrying equipment, a space state mapping model is constructed, and the space state mapping model is used for synchronously mapping the real-time states of the material storage device and the material carrying equipment. A material processing request initiated by a user is received, the material processing request is used for indicating the current position information, the target position information and the processing type of the to-be-processed material, and the processing type comprises storing and / or taking out. And in response to the material processing request, generating a control instruction for controlling the material handling equipment based on the spatial state mapping model.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to a method, apparatus, device, and storage medium for generating control instructions for material handling equipment. Background Technology

[0002] In the process of intelligent manufacturing and digital transformation of logistics, automated storage and retrieval systems (AS / RS) are core facilities for improving warehousing efficiency, and the operational accuracy and visual control capabilities of their stacker cranes are crucial. The application of digital twin technology has made stacker crane simulation and control systems based on 3D models a key tool for the design, operation, and optimization of warehousing systems.

[0003] Currently, the common approach is to acquire signals sent by the stacker crane's programmable logic controller (PLC) through an industrial communication module and directly map the position data to the three-dimensional model coordinate system, thereby achieving a visual representation of the operating status.

[0004] However, when the warehousing system can only provide discrete information such as the target shelf number and lacks high-precision position signals, it is difficult to achieve accurate docking control of the three-dimensional model. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for generating control commands for material handling equipment, used to achieve precise docking control of material handling equipment models.

[0006] Firstly, this application provides a method for generating control instructions for material handling equipment. This method is applied to electronic devices. The executing entity of this method can be an electronic device, a component or device applied to the electronic device (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the electronic device, including: Obtain parameters of material storage devices and material handling equipment. The parameters of material storage devices are used to indicate the specifications, location information, and real-time status information of the material storage devices; the parameters of material handling equipment are used to indicate the specifications and real-time location information of the material handling equipment. Based on the parameters of the material storage device and the material handling equipment, a spatial state mapping model is constructed. The spatial state mapping model is used to synchronously map the real-time status of the material storage device and the material handling equipment. Receive material processing requests initiated by users. Material processing requests are used to indicate the current location information, target location information, and processing type of the material to be processed. Processing type includes storage and / or retrieval. In response to a material handling request, control instructions are generated based on a spatial state mapping model to control the material handling equipment. The control instructions are used to indicate the material handling operation that matches the material handling type, as well as the corresponding running trajectory of the material handling equipment. When the material handling equipment runs based on the running trajectory, it does not overlap with the material storage device in space.

[0007] In the first aspect, by acquiring detailed parameters of material storage devices and material handling equipment, and constructing a spatial state mapping model based on these parameters, precise synchronization between physical equipment and the virtual model can be achieved. When a material handling request containing processing type and location information is received, control commands are generated based on this synchronization model, ensuring that the planned material handling equipment's operating trajectory does not experience clipping in the virtual space, thereby effectively preventing physical collisions between the equipment and storage devices such as shelves during actual execution.

[0008] In conjunction with the first aspect, in one possible implementation, the method further includes: Acquire real-time status information of material storage and real-time location information of material handling equipment after the material handling equipment executes control commands; The spatial state mapping model is updated based on the real-time status information of the material storage after the material handling equipment executes control commands, as well as the real-time location information of the material handling equipment.

[0009] In this implementation, after the material handling equipment executes an instruction, the updated material storage status and equipment location information are acquired in real time, and the spatial state mapping model is updated synchronously accordingly. This process enables the model to continuously and accurately reflect the real-time changes in the physical world, providing a dynamic and realistic data foundation for the generation of subsequent control instructions.

[0010] In conjunction with the first aspect, in one possible implementation, the material handling operation includes: Control the travel of the picking device in the material handling equipment.

[0011] In this implementation, the travel of the picking device in the material handling equipment is precisely managed as a core component of the material handling operation. By directly controlling the extension and retraction of the picking device, the physical dimensions of the storage location and the goods can be accurately matched, avoiding storage and retrieval failures or equipment damage caused by insufficient or excessive travel, thereby improving the accuracy and efficiency of each handling operation.

[0012] In conjunction with the first aspect, in one possible implementation, the method further includes: Send control commands to the material handling equipment.

[0013] In this implementation, a closed loop from virtual planning to physical execution is completed by sending generated control commands to the material handling equipment. This step ensures that the model-verified, spatially uninterrupted safety trajectory can directly drive the actual equipment's actions, achieving lossless transmission and execution of the control strategy and establishing a crucial link between digital planning and automated operation.

[0014] In conjunction with the first aspect, in one possible implementation, in response to a material handling request, control instructions for controlling the material handling equipment are generated based on a spatial state mapping model, including: In response to a material handling request, generate multiple candidate control instructions that match the material handling request; The material handling equipment is simulated in a spatial state mapping model based on multiple candidate control commands to obtain the operation results. The operation results indicate whether the material handling equipment and the material storage device overlap in space when the material handling equipment is operated based on the corresponding candidate control commands. Candidate control commands that do not overlap spatially with material handling equipment and material storage devices are identified as control commands for material handling equipment.

[0015] In this implementation, upon responding to a material handling request, multiple candidate control commands are first generated. Then, each candidate command is simulated and verified within a spatial state mapping model. This pre-simulation-screening mechanism proactively identifies and eliminates commands that could lead to spatial overlap from a variety of possible control strategies, ultimately selecting the safe, feasible, optimal, or compliant command. By advancing collision detection to the command generation stage, the risk of runtime interference is fundamentally prevented, improving the safety of system planning. In conjunction with the first aspect, in one possible implementation, in response to a material handling request, a plurality of candidate control instructions matching the material handling request are generated, including: Based on the parameters of the material storage device and the material handling equipment, multiple candidate docking positions of the material handling equipment are determined, and multiple candidate docking position instructions corresponding to each candidate docking position are generated. The candidate docking position instructions include the corresponding candidate docking position. Based on the real-time location information of each candidate docking location and the material handling equipment, the target docking location correction amount corresponding to each candidate docking location is calculated respectively; By using the correction amount of each target docking position, the corresponding candidate docking position command is corrected to obtain multiple candidate control commands that match the material handling request.

[0016] In this implementation, when generating multiple candidate control commands, several possible candidate docking positions are first determined based on the parameters of the material storage device and the handling equipment, forming corresponding preliminary docking position commands. Next, combining the real-time position information of the material handling equipment, the pose correction amount required for each candidate docking position is calculated to compensate for positioning errors or mechanical backlash. Finally, the preliminary commands are calibrated using these correction amounts to obtain optimized candidate control commands. This process, by introducing real-time position feedback and a dynamic correction mechanism, makes the generated candidate commands more closely resemble the actual movement capabilities of the equipment and environmental constraints, improving the rationality and feasibility of the candidate command set. This provides higher-quality input for subsequent simulation and screening stages, thereby improving the overall final accuracy of the control commands and the system performance.

[0017] Secondly, this application provides a device for generating control commands for material handling equipment, comprising: The parameter acquisition module is used to acquire parameters of the material storage device and the material handling equipment. The material storage device parameters are used to indicate the specifications, location information, and real-time status information of the material storage device; the material handling equipment parameters are used to indicate the specifications and real-time location information of the material handling equipment. The model building module is used to build a spatial state mapping model based on the parameters of the material storage device and the material handling equipment. The spatial state mapping model is used to synchronously map the real-time status of the material storage device and the material handling equipment. The request receiving module is used to receive material processing requests initiated by users. The material processing request is used to indicate the current location information, target location information, and processing type of the material to be processed. The processing type includes storage and / or retrieval. The instruction generation module is used to respond to material handling requests and generate control instructions for controlling material handling equipment based on a spatial state mapping model. The control instructions are used to indicate the material handling operation that matches the material handling type and the corresponding running trajectory of the material handling equipment. When the material handling equipment runs based on the running trajectory, it does not overlap with the material storage device in space.

[0018] In conjunction with the second aspect, in one possible implementation, the parameter acquisition module is also used to acquire real-time status information of material storage after the material handling equipment executes control commands, as well as real-time location information of the material handling equipment. The model building module is also used to update the spatial state mapping model based on the real-time status information of material storage after the material handling equipment executes control commands and the real-time location information of the material handling equipment.

[0019] In conjunction with the second aspect, in one possible implementation, the material handling operation includes: Control the travel of the picking device in the material handling equipment.

[0020] In conjunction with the second aspect, in one possible implementation, the instruction generation module is also used to send control instructions to the material handling equipment.

[0021] In conjunction with the second aspect, in one possible implementation, the instruction generation module is also used to generate multiple candidate control instructions that match the material handling request in response to the material handling request. The material handling equipment is simulated in a spatial state mapping model based on multiple candidate control commands to obtain the operation results. The operation results indicate whether the material handling equipment and the material storage device overlap in space when the material handling equipment is operated based on the corresponding candidate control commands. Candidate control commands that do not overlap spatially with material handling equipment and material storage devices are identified as control commands for material handling equipment.

[0022] In conjunction with the second aspect, in one possible implementation, the instruction generation module is further configured to determine multiple candidate docking positions of the material handling equipment based on the parameters of the material storage device and the parameters of the material handling equipment, and generate multiple candidate docking position instructions corresponding to each candidate docking position, wherein the candidate docking position instructions include the corresponding candidate docking position. Based on the real-time location information of each candidate docking location and the material handling equipment, the target docking location correction amount corresponding to each candidate docking location is calculated respectively; By using the correction amount of each target docking position, the corresponding candidate docking position command is corrected to obtain multiple candidate control commands that match the material handling request.

[0023] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0024] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0025] Fifthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, it causes the electronic device to implement the method described in the first aspect.

[0026] The beneficial effects of the second to fifth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0027] Figure 1A schematic diagram illustrating the application environment of a method for generating control commands for material handling equipment provided in this application embodiment; Figure 2 A schematic diagram of a system architecture for generating control instructions for material handling equipment provided in an embodiment of this application; Figure 3 A flowchart illustrating a method for generating control commands for material handling equipment, provided in an embodiment of this application; Figure 4 A flowchart illustrating a method for generating stacker crane control commands provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating the process of creating a digital twin model of a stacker crane provided in an embodiment of this application. Figure 6 This is a schematic diagram of a stacker crane picking instruction simulation process provided in an embodiment of this application; Figure 7 A schematic diagram illustrating the calculation process for the center target docking position provided in this application embodiment; Figure 8 A schematic diagram illustrating the composition of a device for generating control commands for material handling equipment, provided in an embodiment of this application; Figure 9 This is a schematic diagram of the composition of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The following is a detailed description, with reference to the accompanying drawings, of a method, apparatus, device, and storage medium for generating control instructions for material handling equipment provided in this application.

[0029] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0030] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0031] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0032] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0033] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0034] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0035] In the process of intelligent manufacturing and digital transformation of logistics, automated storage and retrieval systems (AS / RS) serve as core infrastructure for improving warehousing efficiency. Their operational performance is highly dependent on the precision and visual control capabilities of stacker cranes, a key execution device. With the widespread application of digital twin technology, stacker crane simulation and control systems based on 3D models have gradually replaced the traditional pure hardware debugging mode, becoming an important tool in the design, operation, and optimization stages of warehousing systems.

[0036] Currently, common methods for implementing digital twins of stacker cranes mainly employ a PLC data pass-through and 3D model mapping architecture. This approach collects PLC signals related to the stacker crane's movement, lifting, and fork extension / retraction via an industrial communication module, and directly maps the acquired real-time position data to the 3D model's coordinate system, thereby achieving a visual representation of the operational status. The prerequisite for implementing this method is the availability of real-time position data, and its core objective is to solve the pose matching problem between the 3D model and the actual equipment.

[0037] However, in practical industrial applications, especially in some older warehousing systems, high-precision position detection modules are often lacking, providing only discrete information such as the target shelf number. This makes it impossible to achieve precise docking control of the model, limiting the adaptability of this technology in the retrofitting of old systems. Furthermore, directly mapping the raw position data output from the PLC to the 3D model fails to fully consider the geometric constraints between the stacker crane's width, shelf spacing, and cargo dimensions. Combined with actual position deviations caused by mechanical transmission clearances, this easily leads to clipping issues during rendering, such as forks colliding with shelves and cargo penetrating the model, severely impacting the credibility of the virtual twin scene. Simultaneously, most solutions design positioning logic for shelves of uniform specifications. When the automated warehouse contains shelf units of different widths, it is difficult to achieve rapid adaptation through parameter configuration, often requiring the redevelopment of coordinate system calibration algorithms, increasing the complexity of system deployment and maintenance.

[0038] To address the aforementioned technical problems, this application provides a method, apparatus, device, and storage medium for generating control commands for material handling equipment. The core idea is to acquire detailed parameters of the material storage device and the material handling equipment, and construct a spatial state mapping model based on these parameters, thereby achieving precise synchronization between the physical equipment and the virtual model. When a material handling request containing processing type and location information is received, control commands are generated based on this synchronization model. This ensures that the planned trajectory of the material handling equipment does not experience clipping in the virtual space, effectively preventing physical collisions between the equipment and storage devices such as shelves during actual execution.

[0039] The embodiments provided in this application will now be described in detail with reference to the accompanying drawings.

[0040] The method for generating control instructions for material handling equipment provided in this application can be applied to, for example... Figure 1 The application environment shown. For example... Figure 1 As shown, the application environment includes: Terminal device 100, server 101, controller 102, material handling equipment 103, and database system 104.

[0041] Terminal device 100 runs an application client that supports the control of material handling equipment. This client provides users with a digital twin monitoring interface for the material handling and storage system and receives material handling requests initiated by users. These requests include the current location information, target location information, and processing type (storage or retrieval) of the material to be processed. Terminal device 100 is user equipment (UE), and its types include, but are not limited to, smartphones, tablets, laptops, desktop computers, Internet of Things (IoT) terminals, and Vehicle-to-Everything (V2X) terminals. The terminal accesses the access network via a wireless air interface and has the capability to carry voice services, data transmission services, and multimedia services. It can also achieve direct communication between different terminals based on device-to-device (D2D) direct connection technology or V2X technology.

[0042] The method for generating control instructions for material handling equipment provided in this application can be applied to server 101. Server 101 runs an application or service for executing this method. The program is at least used for: acquiring parameters of the material storage device and parameters of the material handling equipment; constructing and maintaining a spatial state mapping model based on the parameters; receiving material handling requests from terminal devices; and, in response to the requests, generating control instructions for controlling the material handling equipment 103 based on the spatial state mapping model. These instructions indicate material handling operations matching the material handling type and the operating trajectory of the material handling equipment, and ensure that the equipment operates without spatial overlap with the material storage device.

[0043] In one optional embodiment, the terminal device 100 and the server 101, the server 101 and the database system 104, and the server 101 and the controller 102 can be interconnected via wired or wireless networks. These connections are used to support the transmission of program instructions and data between the components.

[0044] Specifically, the server 101 and the controller 102 are typically connected via industrial Ethernet (such as Ethernet / IP, Profinet) or industrial bus (such as Modbus TCP). The messages transmitted by the server 101 to the controller 102 are control commands, which at least include motion parameters and running trajectories calculated and determined by the spatial state mapping model, such as: target docking position, running path, and travel control parameters of the picking device.

[0045] The controller 102 is a dedicated logic controller (such as a PLC) for the material handling equipment 103. It is used to receive control instructions from the server 101 and parse the instructions into specific drive signals for each actuator of the material handling equipment 103 (such as walking drive, lifting drive, picking device drive mechanism, etc.), thereby controlling the material handling equipment 103 to perform corresponding storage or picking operations.

[0046] Server 101 can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center. Server 101 includes a memory and a processor. The memory stores a program that implements the material handling equipment control instruction generation method provided in this application; the program is called and executed by the processor to implement the method provided in this application. The memory can include, but is not limited to, the following: random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM). The processor can consist of one or more integrated circuit chips. Optionally, the processor can be a general-purpose processor, such as a central processing unit (CPU) or a network processor (NP). Optionally, the processor can implement the material handling equipment control instruction generation method provided in this application by running programs or code.

[0047] Database system 104 is used to persistently store all parameters and historical status information related to material handling control. This includes parameters of material storage devices (specifications, location, real-time storage status), parameters of material handling equipment (specifications, real-time location), and related data of the spatial status mapping model. It can be a relational database (such as MySQL, PostgreSQL), a non-relational database (such as MongoDB, Redis), or a time-series database, for efficient storage and retrieval of system layout, equipment parameters, cargo information, historical operation data, and model status.

[0048] This application embodiment also provides a material handling equipment control command generation system, which can be set in... Figure 1 In the application environment shown, such as Figure 2As shown, the material handling equipment control command generation system 200 may include: The front-end 201 of the material handling equipment control command generation system is used for: The system receives material processing requests initiated by users and sends them to the backend. The material processing request indicates the current location information, target location information, and processing type (storage or retrieval) of the material to be processed. Simultaneously, the frontend also displays a real-time system status visualization interface based on a spatial state mapping model to the user.

[0049] The backend 202 of the material handling equipment control command generation system is used for: The backend handles material processing requests from the frontend and executes core control logic. It comprises multiple functional engines, specifically including: The parameter acquisition engine 2021 is used to acquire parameters of material storage devices and material handling equipment. The Model Building Engine 2022 is used to build a spatial state mapping model based on the acquired parameters, and is responsible for the synchronous update and state maintenance of the model. The instruction generation engine 2023 is used to respond to material handling requests and generate control instructions for controlling material handling equipment based on a spatial state mapping model. Specifically, the engine is used to: generate multiple candidate control instructions that match the request; simulate the operation of the material handling equipment in the spatial state mapping model to evaluate spatial overlap; and determine the candidate instructions that result in no spatial overlap between the equipment and the storage device as the final control instructions. The instruction optimization engine 2024 is used to calculate the target docking position instruction based on the parameters of the material storage device and the material handling equipment when generating candidate control instructions, and to calculate the target docking position correction amount based on the target docking position and the real-time position of the equipment in order to optimize the instruction. The Device Communication Engine 2025 is used to send the generated final control commands to the controller of the material handling equipment.

[0050] In addition, the backend is also responsible for obtaining the latest status information and triggering model updates after the material handling equipment executes instructions.

[0051] Database system 203 is used for: It persistently stores material storage device parameters, material handling equipment parameters, historical and real-time spatial state mapping model data, material handling request records, and the history of generated control commands. It provides data access support for various backend engines.

[0052] It should be noted that the system architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0053] See Figure 3 This is a flowchart illustrating a method for generating control commands for material handling equipment, as provided in an embodiment of this application. Figure 3 As shown, the method for generating control instructions for material handling equipment provided in this application can be implemented through the aforementioned server, specifically including the following steps S300~S303.

[0054] S300, the server obtains parameters of the material storage device and the material handling equipment.

[0055] Material storage device parameters indicate the specifications, location information, and real-time status of the material storage device. Material handling equipment parameters indicate the specifications and real-time location information of the material handling equipment.

[0056] Material handling equipment includes, for example, stacker cranes, and material storage devices include, for example, shelving in an automated warehouse.

[0057] The server retrieves parameters for material storage devices and material handling equipment. The material storage device parameters indicate the specifications, location information, and storage status of the material storage device. Specifications include physical dimensions, such as the width and height of the rack unit. Location information defines the spatial coordinates of each storage unit in a predefined coordinate system. Real-time storage status information indicates whether each storage unit is occupied. The material handling equipment parameters indicate the specifications and location information of the material handling equipment. Specifications include the physical dimensions of the equipment itself, such as width, upgrade platform parameters, and the travel range of its picking devices (e.g., stacker crane forks). Real-time location information is initially assigned or calculated based on preset logic when only the target storage location number is available.

[0058] S301 The server constructs a spatial state mapping model based on the parameters of the material storage device and the material handling equipment.

[0059] The spatial state mapping model is used to synchronously map the real-time state of material storage devices and material handling equipment.

[0060] Based on the acquired parameters of the material storage devices and material handling equipment, the server constructs a spatial state mapping model. This spatial state mapping model is a digital twin model, whose core function is to synchronously map the real-time state of the material storage devices and material handling equipment in the physical world.

[0061] First, the construction process involves building a material handling equipment model using 3D modeling software (such as 3ds Max or Maya) based on the equipment's parameters, and then rigging the model with a skeleton. The purpose of skeleton rigging is to associate the model's geometry with a hierarchical skeletal structure that controls its movement, laying the foundation for accurate motion animation later. Next, the rigged material handling equipment model is imported into a pre-defined 3D engine environment (such as Unreal Engine 5.1) to generate a programmatically driven skeletal mesh. This skeletal mesh is the model's visual and manipulable representation in the virtual scene.

[0062] Subsequently, physical colliders are associated with the generated skeletal mesh within the 3D engine environment. The purpose of associating physical colliders is to assign collision volumes to the model in virtual space that conform to its physical contours, enabling the detection of contact or penetration between the model and other objects (e.g., shelves, goods) during simulation. Simultaneously, the construction process also includes establishing a motion coordinate system for the material handling equipment model within the 3D engine. This motion coordinate system is used to accurately define and characterize the movement path of the material handling equipment in the real operating environment within virtual space. For example, a 3D spline curve laid along an actual track is created in the engine scene, and this spline curve is set as the reference axis of the motion coordinate system.

[0063] Furthermore, constructing the spatial state mapping model involves instantiating the material storage device parameters into corresponding entities in the virtual scene, and precisely arranging these virtual entities along with physical colliders in the space defined by the motion coordinate system based on the position and specification information in the parameters. Finally, through the logical blueprint system and animation blueprint system of the 3D engine, the driving variables of the skeletal mesh, the detection logic of the physical colliders, and the path calculation of the motion coordinate system are linked together to form a spatial state mapping model that can respond to data input in real time, accurately simulate the kinematics and dynamics of physical equipment, and dynamically render its 3D posture.

[0064] S302. The server receives a material handling request initiated by the user.

[0065] The server receives material handling requests initiated by users. Here, "user" refers to a role that needs to operate or monitor the warehouse through a digital twin interface, such as a warehouse manager submitting instructions through a terminal interface.

[0066] A material handling request indicates the current location, target location, and handling type of the material to be handled, including storage and / or retrieval. A material handling request is an instruction that explicitly communicates a specific material handling task to the server. This request must contain several key pieces of information to uniquely define the task: the identifier of the material to be handled and its current location in the material storage device, the target location to which the material needs to be moved, and the handling type of this task. Handling types include two basic operations: storage and retrieval. A storage operation instructs the material to be placed at the target location, while a retrieval operation instructs the material to be removed from its current location.

[0067] S303. In response to a material handling request, the server generates control instructions for controlling the material handling equipment based on the spatial state mapping model.

[0068] Control commands are used to instruct material handling operations that match the material handling type, as well as the running trajectory of the corresponding material handling equipment. When the material handling equipment runs based on the running trajectory, it does not overlap with the material storage device in space.

[0069] First, the server parses the material handling request, extracting the processing type, the current location of the material to be processed, and the target location. Then, based on the precise geometric parameters and real-time status stored in the spatial state mapping model, the server begins planning the operating trajectory of the material handling equipment. This trajectory is a complete motion sequence, which defines in detail a series of spatial position and attitude changes that the material handling equipment needs to perform to complete the request. Examples include horizontal movement to the target location in the target shelf area, vertical lifting and lowering of the platform to a specified height, and the specific stroke of the forks extending and retracting horizontally to access the goods.

[0070] The core principle of generating operational trajectories is to ensure no spatial overlap. This means that during motion simulation in the spatial state mapping model, when the material handling equipment model (including its forks, platforms, and all moving parts) moves according to the trajectory, its geometric model does not intersect or penetrate with the material storage device model or the stored goods model, thus avoiding clipping. This non-overlapping constraint in the virtual space directly maps to and guarantees safe operation without collisions between equipment and shelves or goods in the physical world.

[0071] To achieve this, the process of generating control commands calls upon physical colliders and geometric constraint data from the model to perform collision prediction. For example, when calculating the fork extension stroke, the maximum allowable fork extension length is calculated based on the width of the target storage location, the width of the goods themselves, and the preset safety clearance, and this is used as a hard constraint. Similarly, the lifting height of the platform is also strictly limited within the legal range of the shelf height and the equipment's own travel.

[0072] Ultimately, the server encodes the planned, conflict-free operational trajectory, along with the logical sequence of actions determined by the processing type (storage or retrieval), into a set of specific control instructions that can be parsed by the lower-level actuators. These control instructions are the final basis for instructing the material handling equipment to safely and accurately complete this storage and retrieval operation.

[0073] In this embodiment, by acquiring detailed parameters of the material storage device and the material handling equipment, and constructing a spatial state mapping model based on these parameters, precise synchronization between the physical equipment and the virtual model can be achieved. When a material handling request containing processing type and location information is received, control commands are generated based on the synchronization model, ensuring that the planned material handling equipment trajectory does not experience clipping in the virtual space, thereby effectively preventing physical collisions between the equipment and storage devices such as shelves during actual execution.

[0074] In one embodiment, the method for generating control commands for material handling equipment further includes: S304. The server obtains real-time status information of material storage and real-time location information of material handling equipment after the material handling equipment executes control commands.

[0075] The server obtains information on changes in the material storage status and the expected stopping position of the material handling equipment after executing control commands. This information can be derived and confirmed through the target information and execution logic inherent in the issued control commands themselves.

[0076] The information regarding changes in material storage status after the material handling equipment executes control commands refers to the logical confirmation of the target storage location's occupancy status based on the execution result of the current operation's processing type (storage or retrieval). When a control command is confirmed to have been executed, the server directly updates the logical status according to the processing type specified in the command: if it's a retrieval operation, the target storage location is confirmed to be empty; if it's a storage operation, the target storage location is confirmed to be occupied. This confirmation can be based on feedback from the controller regarding the command completion signal.

[0077] The expected stopping position information of material handling equipment after executing control commands refers to the planned stopping positions of each motion axis of the equipment, which have been calculated and included in the commands when they are generated. The server extracts these position parameters from the control command data it issues or records, including the target stopping coordinates, target lifting height, and fork extension stroke.

[0078] The purpose of acquiring this information is to maintain consistency between the digital twin model and the control system through the logical association between commands and states, without relying on real-time position sensors. The server triggers data updates on the material storage status and the expected location of the equipment via confirmation signals upon command execution, thereby ensuring synchronization between the control logic of the virtual model and the physical system.

[0079] S305. The server updates the spatial state mapping model based on the real-time status information of material storage after the material handling equipment executes control commands and the real-time location information of the material handling equipment.

[0080] Updating the spatial state mapping model is a process of synchronizing logical states with planned locations to the virtual environment. The server first applies material storage status change information to the corresponding virtual storage location in the model. Based on the latest occupancy status of the target storage location confirmed by the status change information, it directly modifies the visualization attributes and data status of that storage location in the digital twin scenario. For example, if the status information confirms that the target storage location has become empty due to a retrieval operation, the server hides or removes the 3D model of the goods on that location in the virtual scene and updates the flag of that location in the internal status table to "empty"; conversely, if it is confirmed to be an occupancy status after a storage operation, the server activates or displays the goods model at the corresponding coordinates and updates the status table to "occupancy".

[0081] Simultaneously, based on the expected stopping position information of the material handling equipment, the server drives the corresponding virtual model of the material handling equipment in the spatial state mapping model. This driving process includes inputting parameters such as the target stopping coordinates, target lifting height, and fork extension stroke contained in the expected stopping position information into the control logic of the virtual model. These parameters are converted into specific values ​​in the model's motion coordinate system, and then, through the animation and logic systems of the 3D engine, the skeletal mesh is controlled to move to the specified coordinate position and adjust its posture to the predetermined lifting and extension states, thereby making the virtual equipment model precisely stationary at the endpoint pre-calculated by the algorithm.

[0082] Furthermore, based on the material handling type corresponding to the control command that has been executed, the server synchronously updates the status attributes of the material handling equipment in the spatial state mapping model. If the material handling type is material retrieval, it means that the material handling equipment has completed the retrieval operation, and the server updates the load status of the equipment in the model to loaded; if the material handling type is material storage, it means that the equipment has completed the release operation, and the server updates its load status to empty. This status update, combined with the equipment's position and attitude updates, constitutes a complete and up-to-date state image of the material handling equipment in the virtual environment.

[0083] The fundamental purpose of updating the spatial state mapping model is to achieve closed-loop synchronization between the digital twin and the control system's command logic without relying on physical sensors. Through this operation, the state of the spatial state mapping model remains consistent with the theoretical results defined by the executed control commands. This ensures the authority and consistency of the virtual model as the core of planning and simulation, providing an accurate and reliable initial state basis for the next collision-free trajectory planning based on the model, thereby maintaining the effective operation of the entire control method in an environment without direct physical feedback.

[0084] In this embodiment, after the material handling equipment executes an instruction, the updated material storage status and equipment location information are acquired in real time, and the spatial state mapping model is updated synchronously accordingly. This process enables the model to continuously and accurately reflect the real-time changes in the physical world, providing a dynamic and realistic data foundation for the generation of subsequent control instructions.

[0085] In one embodiment, the material handling operation includes: Control the travel of the picking device in the material handling equipment.

[0086] Material handling operations include controlling the stroke of the picking device in material handling equipment. In the specific scenario where the material handling equipment is a stacker crane, the picking device specifically refers to the actuator consisting of a platform and forks. Controlling the stroke of the picking device means precisely managing the range of motion and final position of the actuator in the vertical and horizontal directions.

[0087] The lifting stroke of the pallet truck refers to the process of controlling the vertical movement of the pallet truck to a specified shelf height. This control calculates and outputs the target height command for vertical movement of the pallet truck based on the shelf height parameters contained in the target location information in the material handling request and the height coordinates of each shelf defined in the spatial state mapping model. The purpose of controlling the lifting stroke is to align the height of the pallet truck's bearing surface with the target storage location, creating the correct docking plane for horizontal fork access operations.

[0088] Fork extension / retraction stroke refers to the process of controlling the horizontal extension or retraction of the forks from inside the storage rack. This control calculates and outputs the specific length of fork extension required based on the depth of the target storage location, the size of the goods to be stored or retrieved, and a preset safety clearance. The purpose of controlling the extension / retraction stroke is to ensure that the forks can accurately reach inside the storage location to pick up or place goods, while simultaneously ensuring, through stroke limitation, that there is no contact or penetration between the forks, goods, and the rack in either virtual or physical space.

[0089] The lifting and lowering of the loading platform and the extension and retraction of the forks have a strict logical sequence and spatial coordination. Typically, the lifting and lowering of the loading platform must be completed first, ensuring the picking device reaches the correct working height before the forks extend and retract to execute the actual storage and retrieval action. This step-by-step, orderly stroke control constitutes the core material handling operation necessary to complete a single storage or retrieval operation, and is a direct technical means to achieve precise, collision-free operations.

[0090] In this embodiment, the travel of the picking device of the material handling equipment is precisely managed as a core component of the material handling operation. By directly controlling the extension and retraction of the picking device, the physical dimensions of the storage location and the goods can be accurately matched, avoiding storage and retrieval failures or equipment damage caused by insufficient or excessive travel, thereby improving the accuracy and efficiency of a single handling operation.

[0091] In one embodiment, the method for generating control commands for material handling equipment further includes: S306. The server sends control commands to the material handling equipment.

[0092] In this embodiment, a closed loop from virtual planning to physical execution is completed by sending generated control commands to the material handling equipment. This step ensures that the model-verified, spatially uninterrupted safety trajectory can directly drive the actual equipment's actions, achieving lossless transmission and execution of the control strategy and bridging the critical link between digital planning and automated operation.

[0093] In one embodiment, step S303 includes: S3031. In response to a material handling request, the server generates multiple candidate control instructions that match the material handling request.

[0094] Generating candidate control instructions refers to the process of pre-designing multiple potential control schemes that differ in control parameters or action sequences to fulfill a material handling request. The material handling request defines the start point, end point, and type (storage / retrieval) of the operation, while candidate control instructions, under these constraints, propose different feasible paths and action strategies for controlling material handling equipment (such as a stacker crane) to transition from one state to another.

[0095] Each candidate control instruction is a complete set of executable control commands. Its core components include parameters such as the target stopping position of the material handling equipment, the target height of the platform lifting / lowering, the target stroke of the forks extending / retracting, and the velocity curves connecting these target points. Differences between multiple candidate control instructions can manifest in, for example, different total running times due to different acceleration curves, slightly different intermediate paths planned to avoid potential interference, or different action sequences used in multi-axis coordinated motion.

[0096] The purpose of generating these candidate control instructions is to provide diverse input options for subsequent simulation verification. Based on kinematic models and geometric constraint data, the server derives multiple theoretical control schemes that can complete the requested task, thus laying the foundation for selecting the optimal, collision-free execution instruction.

[0097] S3032. The server simulates the operation of material handling equipment in a spatial state mapping model based on multiple candidate control commands and obtains the operation results.

[0098] Simulation is a non-real-time pre-execution process conducted within a digital twin environment. The server sequentially inputs each candidate control command into the spatial state mapping model, driving the virtual material handling equipment model to execute operations strictly according to the motion trajectory, speed, and action sequence defined in the command. During this process, the spatial state mapping model invokes its built-in physics engine and collision detection logic.

[0099] The results are the evaluation conclusions output after each simulation run. The core indication of these results is whether, when the material handling equipment is operated based on the currently tested candidate control commands, the virtual model of the equipment (including its forks, platforms, and all moving parts) and the virtual model of the material storage device (shelves, goods) geometrically intersect or penetrate in three-dimensional space; that is, whether spatial overlap occurs. The results indicate whether the material handling equipment and the material storage device overlap spatially when operated based on the corresponding candidate control commands.

[0100] The purpose of conducting simulations and obtaining results is to verify the safety and feasibility of control commands in a virtual environment before they are actually sent to physical devices. By traversing and testing multiple candidate solutions, the server can identify in advance which commands may cause collision risks, thereby excluding commands with spatial overlap issues and retaining only those command options that perform safely and without interference in the simulation, providing data support for the final decision.

[0101] S3033, The server determines the candidate control commands of the material handling equipment and the material storage device that do not overlap in space as the control commands of the material handling equipment.

[0102] The server analyzes and compares the execution results of all candidate control instructions, and selects the candidate instructions whose execution results clearly indicate that there is no spatial overlap.

[0103] When multiple candidate instructions exist without spatial overlap, the server can select the optimal instruction based on a preset selection strategy (e.g., shortest operation time, lowest energy consumption, or smoothest path). If only one candidate instruction meets the non-overlap condition, it is directly determined as the final instruction. The determined control instruction will carry all necessary control parameters and become the authoritative command guiding the material handling equipment in the physical world to execute this operation.

[0104] This ensures that the final output control commands are verified through virtual simulation and are safe. This mechanism of generating, simulating, and then selecting commands ensures that regardless of the material handling request, the control commands ultimately applied to the physical equipment have undergone collision-free verification in the digital twin. This avoids the risk of interference or accidents during actual operation from the outset, achieving the goal of safe control.

[0105] In one possible implementation, step S3031 includes: S30311. Based on the parameters of the material storage device and the parameters of the material handling equipment, determine multiple candidate docking positions of the material handling equipment, and generate multiple candidate docking position instructions corresponding to each candidate docking position.

[0106] The candidate docking position instruction includes the corresponding candidate docking position.

[0107] Based on the parameters of the material storage device and the material handling equipment, the server determines multiple candidate docking positions required for the material handling equipment to complete the current material handling request, and generates multiple candidate docking position instructions corresponding to each candidate docking position.

[0108] Candidate docking locations refer to multiple alternative coordinate points near the target storage location defined in the material handling request, calculated based on geometric constraints and operational rules, that are suitable for the final docking of material handling equipment. For example, for the same target rack column, different horizontal coordinates calculated using strategies such as rack center alignment or rack left edge alignment can be considered. The process of determining these candidate docking locations involves parallel calculations based on material storage device parameters and material handling equipment parameters, applying different positioning logics or formulas.

[0109] Candidate docking position commands are preliminary control commands generated based on a single candidate docking position. The core of this command is the inclusion of the coordinates of this specific candidate docking position, serving as the target point for controlling the horizontal movement of the material handling equipment. The purpose of generating multiple candidate docking position commands is to introduce diversity at the initial stage of path planning, providing different spatial positioning starting points for subsequent simulation-based selection of the optimal solution, thereby exploring more collision-free possibilities at the algorithmic level.

[0110] S30312. Based on the real-time location information of each candidate docking position and the material handling equipment, calculate the target docking position correction amount corresponding to each candidate docking position.

[0111] The target docking position correction is an adjustment value used to compensate for systematic errors or dynamic disturbances. This correction is calculated individually for each candidate docking position. The calculation process typically uses the vector between the candidate docking position and the current real-time position of the material handling equipment. Calculating a unique correction for each candidate docking position takes into account that the required error compensation may differ when approaching the target from different paths or starting points. The purpose of calculating the target docking position correction is to reduce the final docking error caused by mechanical backlash or control lag, thereby improving positioning accuracy.

[0112] S30313. The corresponding candidate docking position commands are corrected using the correction amount of each target docking position to obtain multiple candidate control commands that match the material handling request.

[0113] Correction is a process of incorporating calculated theoretical adjustments into the original command. For each candidate docking location command, the server algebraically synthesizes (e.g., adds) the original candidate docking location coordinates contained therein with the target docking location correction amount specifically calculated for that location. The synthesized result is the new docking location after error compensation adjustment.

[0114] After correcting the command using the correction amount, multiple complete candidate control commands are obtained. Compared with the original candidate docking position command, the candidate control command not only includes the corrected and more accurate target docking position, but also usually integrates complete motion parameters such as the target height of the platform and the target fork stroke determined based on the material handling request type (store / retrieve).

[0115] S30311. The server calculates the target docking position instruction for the material handling equipment based on the parameters of the material storage device and the material handling equipment. The target docking position instruction includes the target docking position.

[0116] A target docking position instruction is a specific spatial coordinate instruction used to indicate the precise location where material handling equipment needs to move horizontally and eventually stop. The calculation of this instruction depends on the parameters of the material storage device and the material handling equipment.

[0117] Specifically, the server parses the target location information in the material handling request and queries the corresponding target shelf unit characteristics in the material storage device parameters. These characteristics include at least the width of the target shelf and its coordinates in a preset motion coordinate system. Simultaneously, the server obtains the width of the material handling equipment body from the material handling equipment parameters. Based on these geometric parameters, the server calculates the target docking position using a predefined positioning algorithm. For example, one algorithm aligns the central axis of the material handling equipment with the central axis of the target shelf, while also considering offset compensation based on the equipment's own width, thereby deriving the coordinates of the target docking position.

[0118] The calculation process solves the technical problem of how to achieve accurate positioning based solely on shelf features and equipment dimensions when real-time position feedback is lacking, and is the foundation for generating all subsequent motion control.

[0119] S30312. The server calculates the target docking position correction based on the target docking position and the real-time position information of the material handling equipment.

[0120] The target docking position correction amount is used to correct the target docking position command.

[0121] The target docking position correction is an offset value used to fine-tune the initially calculated target docking position command. The calculation of this correction mainly takes into account the errors that may accumulate due to factors such as mechanical backlash during the process of the material handling equipment moving from its current real-time position to the theoretical target position.

[0122] The server acquires the real-time location information of the material handling equipment, which can be derived from the location where the equipment was last confirmed to have stopped. The server calculates the absolute distance between this real-time location and the theoretical target stopping position. Subsequently, the server calculates a correction amount based on a distance-related dynamic compensation rule. For example, the correction amount could be the product of the distance and a small coefficient that represents the average error that might occur per unit distance.

[0123] The calculated target docking position correction will be used to correct the initially generated target docking position command. The correction method usually involves algebraically superimposing the correction amount with the original target coordinates to produce an optimal docking position that is closer to the actual physical requirements after error compensation.

[0124] The purpose of calculating the target docking position correction is to suppress positioning errors that may be amplified by reciprocating motion or long-distance movement through algorithmic compensation, thus preventing error accumulation that could lead to significant misalignment between the virtual model and the physical shelf. This mechanism enhances the long-term stability and reliability of the positioning system even without high-precision closed-loop sensors.

[0125] In this embodiment, upon responding to a material handling request, multiple candidate control commands are first generated, and then each candidate command is simulated and verified in a spatial state mapping model. This pre-simulation-screening mechanism proactively identifies and eliminates commands that could lead to spatial overlap from a variety of possible control strategies, ultimately selecting the safe, feasible, optimal, or compliant command. By advancing collision detection to the command generation stage, the risk of runtime interference is fundamentally prevented, improving the safety of system planning.

[0126] When generating multiple candidate control commands, the process first determines several possible candidate docking positions based on the parameters of the material storage device and the handling equipment, forming corresponding preliminary docking position commands. Next, combining the real-time position information of the material handling equipment, the pose correction amount required for each candidate docking position is calculated to compensate for positioning errors or mechanical backlash. Finally, the preliminary commands are calibrated using these correction amounts to obtain optimized candidate control commands. This process, by introducing real-time position feedback and a dynamic correction mechanism, makes the generated candidate commands more closely resemble the actual movement capabilities of the equipment and environmental constraints, improving the rationality and feasibility of the candidate command set. This provides higher-quality input for subsequent simulation and screening stages, thereby improving the overall final accuracy of the control commands and the system performance.

[0127] The method for generating control instructions for material handling equipment provided in this application will be described below with specific examples. Figure 4 A flowchart illustrating a method for generating stacker crane control commands provided in this application embodiment is shown below. Figure 4 As shown: S400, the server obtains rack parameters and stacker crane parameters.

[0128] The material storage devices are shelving units, and the material handling devices are stacker cranes. The server acquires shelving parameters that characterize the geometric attributes and location information of all shelving units in the automated warehouse, as well as stacker crane parameters that characterize the dimensions and motion constraints of the stacker crane itself. The stacker crane parameters include at least the width of the stacker crane body, its initial position coordinates, and the limit travel range of its loading platform lifting and fork extension. Acquiring these parameters provides a data foundation for subsequent construction of accurate digital twin scenarios and geometric calculations.

[0129] S401: The server constructs a digital twin model of the stacker crane and the rack based on the rack parameters and the stacker crane parameters.

[0130] The server constructs a digital twin model of the stacker crane and the rack based on the basic parameters of the rack and the stacker crane.

[0131] Figure 5 This is a schematic diagram illustrating the process of creating a digital twin model of a stacker crane provided in an embodiment of this application, such as... Figure 5 As shown: S4011: The server performs 3D modeling of stacker crane components based on the stacker crane parameters.

[0132] Using 3D modeling software (e.g., 3ds Max or Maya), create 3D geometric models of each core component of the stacker crane based on the mechanical dimensions described in the stacker crane parameters. These components include at least the metal structure (such as columns, top beams, and bottom beams), the running mechanism (such as drive wheelsets and driven wheelsets), the lifting mechanism (i.e., elevator or loading platform), and the fork extension mechanism (including top forks, middle forks, and bottom forks). The purpose of modeling is to accurately reproduce the geometry of the physical stacker crane in digital space.

[0133] S4012, The server performs three-level skeleton binding on the already modeled 3D model of the stacker crane.

[0134] Skeleton rigging is the process in 3D modeling software of creating and associating a hierarchical skeleton system with a static mesh model. This process constructs a three-level skeleton hierarchy: the root skeleton is associated with the stacker crane as a whole, used to control its horizontal movement along the tracks; the second-level skeleton is associated with the elevator component, specifically for controlling its vertical movement; and the third-level skeleton is associated with the forklift component, specifically for controlling its extension and retraction. The purpose of skeleton rigging is to associate the geometric vertices of the model with bone weights, thereby laying the foundation for generating complex, hierarchical animation effects by driving the skeleton.

[0135] S4013. The server will export the stacker crane model with completed skeleton binding as an FBX format file.

[0136] Generates a standard exchange file containing model geometry, material maps, and complete skeletal hierarchy data. FBX format is a widely supported, universal 3D asset format that facilitates data transfer between different software platforms.

[0137] S4014. The server imports the exported FBX format file into the Unreal Engine 5.1 3D engine.

[0138] Using Unreal Engine's asset import feature, data from FBX files is converted and loaded into the engine project content, becoming a raw asset available for subsequent processing.

[0139] S4015. In Unreal Engine 5.1, generate editable skeletal mesh assets based on imported assets.

[0140] Generating a skeletal mesh is an internal engine process that instantiates and optimizes imported model data. This operation creates an asset containing a skeletal hierarchy, polygonal meshes, and material references. This skeletal mesh is the core object that can be driven by animation blueprints and rendered and interacted with in the game scene.

[0141] S4016. The server associates physical colliders with the generated skeletal mesh.

[0142] Associating physics colliders is the process of assigning physical properties to visual models within the engine. Specifically, it involves adding a "Convex Mesh" type collider to the skeletal mesh. This collider is a simplified convex hull geometry generated based on the model's outline. It is used to accurately detect contact and penetration between the collider and other scene objects such as shelves and goods during simulation runtime, and is a key technical setting for achieving anti-"clipping" simulation.

[0143] As shown in Table 1, the variables required to realize the digital twin model of the stacker crane are defined in the process of associating the generated skeletal mesh with physical colliders: Table 1 Variable Definitions

[0144] The animation blueprint and logic blueprint will be introduced next: The animation blueprint is based on a "state machine + parameter-driven" architecture and is deployed within the animation instance of the skeletal mesh. Its core implementation includes high-precision motion interpolation and boundary constraints. Core node configuration: (1) Create Float type driven variables ElevatorHeight (elevator height, range bound to ElevatorHeightLimit) and ForkStretch (fork extension amount, range bound to ForkStretchLimit), enable the "can be driven by animation notification" property to support real-time modification of logical blueprints.

[0145] (2) Add a TwoBoneIKSolver node and associate it with the elevator skeleton chain (column-elevator connection point-elevator platform) to correct the posture deviation of the elevator during lifting and lowering and avoid collision with the gantry.

[0146] (3) The fork extension adopts the "three-level skeleton linkage" logic: the ForkStretch parameter is mapped to the extension ratio of the upper fork, middle fork and lower fork (proportion coefficient 1:0.8:0.6) through the BlendSpace1D node to simulate the nested extension movement of real forks.

[0147] (4) Add a CollisionQuery node to detect the collision distance with the shelf and goods in real time during the elevator lifting and fork extension. When the distance is less than the safety threshold (default 5cm), the action deceleration is triggered through the Branch node (speed attenuation coefficient 0.3).

[0148] Animation Mixed Logic: (1) The three motion channels of walking, lifting, and stretching are marked with state through AnimNotifyState, and the AdditiveAnimation mode is used to realize the synchronous execution of multiple actions (such as synchronous lifting during walking).

[0149] (2) The action transition adopts the EaseIn / Out interpolation algorithm and sets the transition time to 0.2s to avoid rendering stuttering caused by sudden action changes.

[0150] Boundary protection mechanism: Add a variable listener node to the EventGraph. When ElevatorHeight reaches the maximum or minimum value of ElevatorHeightLimit, the SetEnable node will be automatically triggered to turn off the corresponding skeletal drive signal, and a boundary trigger notification will be sent to the logical blueprint via DispatchFunction.

[0151] The logic blueprint, as the core control unit of the stacker crane, integrates spline navigation, motion scheduling, and external communication functions, and is implemented as follows: Component configuration and association: (1) After adding SplineComponent, enable the "Edit Spline Points" property to support manual adjustment of spline node positions in the UE editor. The node spacing is proportional to the shelf column spacing (default 1:1 mapping). At the same time, add SplineMeshComponent to associate with the track model to achieve visual alignment between spline and physical track.

[0152] (2) In addition to associating the skeleton mesh with the animation blueprint, SkeletalMeshComponent adds PhysicsConstraintComponent to constrain the motion degrees of freedom of the root bone (only allowing movement along the spline X-axis, elevator Z-axis lifting and lowering, and fork X-axis extension and retraction), restricting invalid movements.

[0153] (3) Add ModbusTCPClientComponent, configure IP address and port number to realize bidirectional communication with PLC: read PLC target shelf number, working mode and other signals at regular intervals (100ms cycle), and send feedback information such as stacker crane current position and action status.

[0154] Core logic implementation: (1) Signal parsing and scheduling: When ExternalSignalStatus is True, the action flow is switched through the SwitchCase node according to StackerWorkMode (pickup / delivery); at the same time, MotionLockStatus is set to True to prevent interruption of the current action.

[0155] (2) Spline navigation control: Call the GetLocationAtDistanceAlongSpline node of SplineComponent to convert the calculated target coordinates Xt into spline distance parameters. The Lerp node enables the stacker to move smoothly from the current position to the target position. The movement speed is configured through FloatCurve (three curves: start acceleration, constant speed, and stop deceleration).

[0156] (3) Action Coordination Control: BindFunction binds the variables of the logical blueprint to the driveable variables of the animation blueprint to realize the real-time assignment of ElevatorHeight and ForkStretch; for example, when the stacker crane reaches the target position, the logical blueprint sends the target height value through the SetElevatorHeight node, and the animation blueprint automatically executes the lifting action.

[0157] (4) Anti-mold verification logic: Before the fork extension and retraction action is executed, the current cargo width Gw is obtained by querying the GoodsWidthDatabase through the MapLookup node, and the maximum allowable extension and retraction of the fork ForkMaxStretch=ForkStretchLimit.X-Gw-0.1m (with a 10cm safety gap reserved). If ForkStretch exceeds this value, it will be forcibly truncated and an alarm will be triggered.

[0158] Status feedback and updates: When the action is completed (such as successful pickup), the custom event OnTaskCompleted is triggered by EventDispatch. This event notifies the AGV scheduling system to pick up the goods and calls the UpdatePanelData function to update data such as shelf occupancy status and stacker crane working status in the UE system panel.

[0159] When driving the stacker crane's 3D model to perform lifting, extending, and other actions, the parametric real-time driving method used by the animation blueprint can be replaced by an animation montage blending solution. In this alternative, instead of relying entirely on real-time skeletal driving via variables such as ElevatorHeight and ForkStretch, a series of discrete animation clips (i.e., animation montages) covering different travel ranges are pre-created. For example, keyframe animations of the loading platform rising to a specific height or the forks extending to a specific length are recorded. The control logic selects the most matching pre-recorded animation montage for playback and blending based on the calculated target height or travel.

[0160] S4017, The server creates the spline reference axis and the shelf unit set.

[0161] In the 3D scene of Unreal Engine 5.1, the server creates a 3D spline curve along the virtual mapping path of the stacker crane's physical travel track, serving as the spline reference axis. The starting point of this spline reference axis corresponds to the starting point of the physical track, with its coordinate set to 0, and the ending point corresponds to the ending point of the physical track, with its coordinate set to L (i.e., the total length of the spline, proportionally mapped to the length of the physical track). This spline reference axis will serve as the reference coordinate system for the stacker crane's horizontal movement.

[0162] Simultaneously, based on the acquired shelf parameters, the server constructs a shelf unit feature set in memory. This feature set is stored in a two-dimensional dictionary data structure, with the keys being the shelf layer number Z and column number Y, and the values ​​being a value containing the shelf width W and the coordinate X of the shelf's left edge on the spline reference axis. s The binary tuple. That is, the storage format is: ShelfDict[Z][Y] = (W, X s Where Z is the shelf layer index, Y is the shelf column index, W is the width of the shelf unit, and Xs is the coordinate of the left edge of the shelf unit on the spline reference axis. This data structure allows for quick retrieval of the corresponding geometric parameters by shelf number, providing data support for subsequent positioning calculations. The shelf feature dictionary can be replaced with an Excel spreadsheet import mode: shelf parameters are obtained by reading an Excel file.

[0163] When generating the target docking position for the stacker crane, the rack center coordinates can be used for direct indexing. In this alternative, the data structure of the rack unit feature set is expanded or modified, pre-calculating and storing the center coordinates for each rack unit; for example, the data format is recorded as ShelfDict[Z][Y] = X c , where X c This represents the coordinates of the shelf's center on the spline reference axis. When calculating the target docking position, the server directly queries and retrieves this pre-stored center coordinate X.c As the base target value, the stacker crane width is then used to make necessary offset compensation.

[0164] S402, The server receives a pickup request for the target goods.

[0165] After the digital twin model of the stacker crane and the rack is completed and put into operation, the server receives material retrieval operation requests initiated by users through the digital twin model.

[0166] The pickup request explicitly indicates the location information of the target goods. For example, it is encoded and transmitted as a combination of the shelf number (Z) and column number (Y) of the target shelf. After receiving the request, the server parses the data packet and extracts the target shelf number (Z). t ,Y t And the explicit processing type is retrieval.

[0167] S403. The server responds to the pickup request and generates stacker crane control instructions.

[0168] Figure 6 This is a schematic diagram of a stacker crane picking instruction simulation process provided in an embodiment of this application, such as... Figure 6 As shown: S4031, The server loads the required data.

[0169] Before generating specific action command sequences, the server ensures that the stacker crane control logic in the digital twin environment has been initialized. This includes loading necessary data such as the rack feature dictionary and cargo size database, and initializing the stacker crane's motion lock state to False, indicating that the equipment is in a ready state to receive new commands.

[0170] S4032. The server listens for and confirms external job trigger signals.

[0171] The server continuously checks the status of external signal input. When the status is True, it indicates that a valid job request has been received, and the server confirms that the job has been triggered; if it is False, it remains in a waiting state until the signal becomes True.

[0172] S4033, The server generates multiple candidate control commands.

[0173] First, the server determines multiple candidate docking positions based on rack and equipment parameters (e.g., based on different alignment strategies or position variations with slight offsets) and generates preliminary instructions containing each candidate docking position. Then, based on each candidate docking position and the stacker crane's current real-time position, the server calculates the corresponding target docking position correction. Finally, the server uses these corrections to refine the corresponding preliminary instructions and, combined with information such as the target rack height and cargo dimensions, generates a complete set of multiple candidate control instructions matching the picking request. Each candidate control instruction includes the corrected docking coordinates, target lifting height, and fork extension / retraction stroke.

[0174] S4034. The server simulates and filters instructions in the digital twin model.

[0175] The server sequentially generates multiple candidate control commands and drives the virtual stacker crane model to simulate operation within the spatial state mapping model. In each simulation, based on the command parameters, the server controls the virtual model to perform horizontal movement, lifting, and fork extension / retraction actions from its current position to the target point, and continuously monitors in real time whether geometric penetration (i.e., "penetration") occurs between the virtual stacker crane model (including its forks and loading platform) and the virtual racks and cargo models. After the simulation is completed, the server records the execution result for each candidate control command, indicating whether executing the command will result in spatial overlap.

[0176] S4035, The server determines the final control command and drives the virtual execution.

[0177] The server analyzes the simulation results of all candidate control commands and filters out commands whose results indicate "no spatial overlap". If multiple such commands exist, one can be selected as the final control command based on preset optimization criteria (such as shortest time); if there is only one, it is directly determined as the final control command. Subsequently, the server drives the virtual stacker crane model in the digital twin environment to execute the action sequence defined by the final control command completely and coherently to verify its overall coordination and confirm that the picking action is completed in the simulation environment.

[0178] In step S4033, the server needs to determine multiple candidate docking locations. Figure 7 This is a schematic diagram of the central docking position calculation process provided in the embodiments of this application, such as... Figure 7 As shown: S4036. The server receives and parses the target shelf number in the pickup request.

[0179] The server parses the received pickup request and extracts the unique location identifier of the target shelf, which is usually indicated by the shelf number (Z). t ) and column number (Y t This can be represented in combination form.

[0180] S4037. The server obtains the geometric parameters of the target shelf.

[0181] The server determines the target shelf number (Z) t ,Y t ), query a pre-built set of shelf unit features. Retrieve the width W of the target shelf from this feature set. t and the coordinate X of its left edge on the spline reference axis. st .

[0182] S4038, The server reads the stacker crane width parameter and the current real-time position coordinates.

[0183] The server obtains the physical width D of the stacker crane body from the stacker crane parameters, and obtains the current coordinate X of the stacker crane on the spline reference axis from the stacker crane's real-time status information. c If it is the start state of the task, X c This refers to the initial position coordinates of the stacker crane.

[0184] S4039. Based on the acquired parameters, the server calculates the target docking coordinates of the stacker crane.

[0185] The server substitutes the acquired parameters into a predefined positioning algorithm formula to calculate the target coordinates X that the stacker crane needs to stop at. t The calculation formula is: X t =X st +(W t / 2)-(D / 2)+ΔX, where ΔX is the correction amount based on the current distance, ΔX=k×|X t -X c | / L, k is a preset coefficient, k∈[0.01,0.05], L is the total length of the spline.

[0186] S4040. The server verifies the validity of the calculated target docking coordinates.

[0187] The server determines the calculated target coordinates X. t Check if the coordinate is within the valid range [0, L] of the spline reference axis. If Xt is within the valid range, output the coordinate to the subsequent control module; if it is outside the valid range, trigger a position anomaly alarm and terminate the current instruction generation process.

[0188] S404: The server sends control commands to the physical stacker crane and drives it to perform the picking operation.

[0189] After the simulation in the digital twin environment is verified to be correct, the server will send the final stacker crane control commands to the stacker crane equipment in the physical world and drive it to perform the actual picking operation.

[0190] The server sends control commands to the stacker crane's programmable logic controller (PLC) through its integrated industrial communication interface. Utilizing standard industrial communication protocols (such as Modbus / TCP), the server transmits control commands, including target docking coordinates, target lifting height, target fork extension / retraction stroke, and action sequence timing, to the stacker crane controller via an industrial network.

[0191] Upon receiving the instruction, the physical stacker crane controller parses it and converts it into control signals to drive each actuator, thus controlling the stacker crane to complete the picking operation.

[0192] S405. The server updates the state of the digital twin model based on the result of the instruction execution.

[0193] The server first deduces and updates the material storage status based on the confirmed control command type. In the digital twin model, the server marks the virtual storage location corresponding to the target storage location number of this command as idle, and removes the 3D model of the goods at that location from the 3D visualization scene.

[0194] Simultaneously, the server updates the status of the virtual stacker crane according to the target stopping positions of each axis of the stacker crane as predetermined in the control command. The server adjusts the coordinates of the virtual stacker crane model on the spline reference axis to the target docking coordinates in the command, adjusts the height of the loading platform to the target lifting height in the command, and adjusts the extension and retraction state of the forks to the retracted position defined in the command, so that the posture of the virtual model is completely consistent with the expected stopping posture of the physical equipment after executing the command.

[0195] Furthermore, based on the completed retrieval operation, the server updates the stacker crane's load status attribute in the digital twin model from empty to loaded. After the stacker crane's load status is updated to loaded, the server sends a task trigger signal to the AGV scheduling system to automatically remove the goods from the stacker crane's loading platform. The MotionLockStatus is only set to False after the entire task MotionLockStatus is completed, indicating that the stacker crane is idle and ready to receive new instructions.

[0196] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0197] This application embodiment can divide the material handling equipment control command generation device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0198] In some embodiments, this application also provides an apparatus for generating control instructions for material handling equipment. This apparatus may include one or more functional modules for implementing the method for generating control instructions for material handling equipment as described in the above embodiments.

[0199] For example, Figure 8 This is a schematic diagram illustrating the composition of a device for generating control commands for material handling equipment, as provided in an embodiment of this application. Figure 8 As shown, the material handling equipment control command generation device 500 includes: a parameter acquisition module 501, a model construction module 502, a request receiving module 503, and a command generation module 504.

[0200] The parameter acquisition module 501 is used to acquire parameters of the material storage device and the material handling equipment. The material storage device parameters indicate the specifications, location information, and real-time status information of the material storage device. The material handling equipment parameters indicate the specifications and real-time location information of the material handling equipment.

[0201] The model building module 502 is used to build a spatial state mapping model based on the parameters of the material storage device and the material handling equipment. The spatial state mapping model is used to synchronously map the real-time status of the material storage device and the material handling equipment.

[0202] The request receiving module 503 is used to receive material processing requests initiated by users. The material processing request is used to indicate the current location information, target location information, and processing type of the material to be processed. The processing type includes storage and / or retrieval.

[0203] The instruction generation module 504 is used to generate control instructions for controlling the material handling equipment based on the spatial state mapping model in response to the material handling request. The control instructions are used to indicate the material handling operation that matches the material handling type and the corresponding running trajectory of the material handling equipment. When the material handling equipment runs based on the running trajectory, it does not overlap with the material storage device in space.

[0204] In one embodiment, the parameter acquisition module 501 is further configured to acquire real-time status information of material storage after the material handling equipment executes the control command, as well as real-time location information of the material handling equipment.

[0205] The model building module 502 is also used to update the spatial state mapping model based on the real-time status information of material storage after the material handling equipment executes control commands and the real-time location information of the material handling equipment.

[0206] In one embodiment, the material handling operation includes: Control the travel of the picking device in the material handling equipment.

[0207] In one embodiment, the instruction generation module 504 is also used to send control instructions to the material handling equipment.

[0208] In one embodiment, the instruction generation module 504 is further configured to generate a plurality of candidate control instructions that match the material handling request in response to the material handling request.

[0209] The material handling equipment is simulated in a spatial state mapping model based on multiple candidate control commands to obtain the operation results. The operation results indicate whether the material handling equipment and the material storage device overlap in space when the material handling equipment is operated based on the corresponding candidate control commands.

[0210] Candidate control commands that do not overlap spatially with material handling equipment and material storage devices are identified as control commands for material handling equipment.

[0211] In one embodiment, the instruction generation module 504 is further configured to determine multiple candidate docking positions of the material handling equipment based on the parameters of the material storage device and the parameters of the material handling equipment, and generate multiple candidate docking position instructions corresponding to each candidate docking position, wherein the candidate docking position instructions include the corresponding candidate docking position.

[0212] Based on the real-time location information of each candidate docking location and the material handling equipment, the target docking location correction amount corresponding to each candidate docking location is calculated.

[0213] By using the correction amount of each target docking position, the corresponding candidate docking position command is corrected to obtain multiple candidate control commands that match the material handling request.

[0214] When implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a possible schematic diagram of the electronic device involved in the above embodiments. For example... Figure 9 As shown, the electronic device 600 includes: a processor 602, a communication interface 603, and a bus 604. Optionally, the electronic device 600 may also include a memory 601.

[0215] Processor 602 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 602 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 602 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0216] Communication interface 603 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0217] The memory 601 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0218] In one possible implementation, the memory 601 can exist independently of the processor 602. The memory 601 can be connected to the processor 602 via a bus 604 and is used to store instructions or program code. When the processor 602 calls and executes the instructions or program code stored in the memory 601, it can implement the method for generating control instructions for material handling equipment provided in this embodiment of the invention.

[0219] In another possible implementation, the memory 601 can also be integrated with the processor 602.

[0220] Bus 604 can be an extended industry standard architecture (EISA) bus, etc. Bus 604 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0221] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0222] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the aforementioned computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The aforementioned computer-readable storage medium can also be an external storage device of the aforementioned service invocation device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the aforementioned service invocation device. Further, the aforementioned computer-readable storage medium can include both internal storage units of the aforementioned service invocation device and external storage devices. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned service invocation device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0223] This application also provides computer instructions. All or part of the processes in the above method embodiments can be executed by computer instructions to instruct related hardware (such as computers, processors, network devices, and terminals). The program can be stored in the aforementioned computer-readable storage medium.

[0224] This application also provides a computer program product that, when run on a computer, causes the above-described method embodiments to be executed.

[0225] This application also provides a chip system. The chip system may be composed of chips or may include chips and other discrete devices, without limitation. The chip system includes a processor and a transceiver. All or part of the processes in the above method embodiments can be completed by this chip system, such as the chip system being used to implement the functions performed by the network devices or terminals in the above method embodiments.

[0226] In one possible design, the chip system further includes a memory for storing program instructions and / or data. When the chip system is running, the processor executes the program instructions stored in the memory to enable the chip system to perform the functions performed by the network device or terminal in the above method embodiments.

[0227] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for generating control commands for material handling equipment, characterized in that, include: Obtain parameters of the material storage device and the material handling equipment. The parameters of the material storage device are used to indicate the specifications, location information, and real-time status information of the material storage device. The parameters of the material handling equipment are used to indicate the specifications and real-time location information of the material handling equipment. Based on the parameters of the material storage device and the material handling equipment, a spatial state mapping model is constructed. The spatial state mapping model is used to synchronously map the real-time state of the material storage device and the material handling equipment. Receive a material processing request initiated by a user. The material processing request is used to indicate the current location information, target location information, and processing type of the material to be processed. The processing type includes storage and / or retrieval. In response to the material handling request, control instructions for controlling the material handling equipment are generated based on the spatial state mapping model. The control instructions are used to indicate the material handling operation that matches the material handling type and the corresponding running trajectory of the material handling equipment. When the material handling equipment runs based on the running trajectory, it does not overlap with the material storage device in space.

2. The method according to claim 1, characterized in that, The method further includes: The real-time status information of the material storage after the material handling equipment executes the control command, and the real-time location information of the material handling equipment are obtained. The spatial state mapping model is updated based on the real-time status information of the material storage after the material handling equipment executes the control command and the real-time location information of the material handling equipment.

3. The method according to claim 1, characterized in that, The material handling operation includes: Control the travel of the picking device of the material handling equipment.

4. The method according to claim 1, characterized in that, The method further includes: Send the control command to the material handling equipment.

5. The method according to any one of claims 1-4, characterized in that, In response to the material handling request, the generation of control instructions for controlling the material handling equipment based on the spatial state mapping model includes: In response to the material handling request, a plurality of candidate control instructions matching the material handling request are generated; Based on the multiple candidate control commands, the material handling equipment is simulated in the spatial state mapping model to obtain the operation results. The operation results indicate whether the material handling equipment and the material storage device overlap in space when the material handling equipment is operated based on the corresponding candidate control commands. The candidate control commands that do not overlap spatially with the material handling equipment and the material storage device are determined as the control commands for the material handling equipment.

6. The method according to claim 5, characterized in that, The step of generating multiple candidate control instructions matching the material handling request in response to the material handling request includes: Based on the parameters of the material storage device and the parameters of the material handling equipment, multiple candidate docking positions of the material handling equipment are determined, and multiple candidate docking position instructions corresponding to each candidate docking position are generated. The candidate docking position instructions include the corresponding candidate docking position. Based on the real-time location information of each candidate docking location and the material handling equipment, the target docking location correction amount corresponding to each candidate docking location is calculated respectively; The corresponding candidate docking position commands are corrected using the correction amounts of each target docking position to obtain multiple candidate control commands that match the material handling request.

7. A device for generating control commands for material handling equipment, characterized in that, include: The parameter acquisition module is used to acquire parameters of the material storage device and parameters of the material handling equipment. The parameters of the material storage device are used to indicate the specifications, location information, and real-time status information of the material storage device; the parameters of the material handling equipment are used to indicate the specifications and real-time location information of the material handling equipment. The model building module is used to build a spatial state mapping model based on the parameters of the material storage device and the material handling equipment. The spatial state mapping model is used to synchronously map the real-time state of the material storage device and the material handling equipment. The request receiving module is used to receive material processing requests initiated by users. The material processing request is used to indicate the current location information, target location information, and processing type of the material to be processed. The processing type includes storage and / or retrieval. The instruction generation module is used to respond to the material handling request and generate control instructions for controlling the material handling equipment based on the spatial state mapping model. The control instructions are used to indicate the material handling operation that matches the material handling type and the corresponding running trajectory of the material handling equipment. When the material handling equipment runs based on the running trajectory, it does not overlap with the material storage device in space.

8. An electronic device, characterized in that, The device includes a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computer device to implement the method for generating material handling equipment control instructions as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer-executable instructions that, when executed on a computer, cause the computer to perform the method for generating material handling equipment control instructions as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to execute the method for generating material handling equipment control instructions as described in any one of claims 1 to 6.

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