Warehouse robot simulation method, system, device, equipment and storage medium

Abstract

The application discloses a warehouse robot simulation method, system, device, equipment and storage medium, and relates to the technical field of intelligent logistics and software testing. The method comprises the following steps: a simulation platform sends simulation environment information and a to-be-executed business task to a robot control system through a homogeneous interface layer; the robot control system runs a to-be-tested algorithm, calculates device control instructions for executing the business task according to the simulation environment information, and sends the device control instructions to the simulation platform through the homogeneous interface layer; the simulation platform determines a target future event corresponding to the device control instructions, advances a simulation clock based on the target future event, and sends an updated device state to the robot control system through the homogeneous interface layer; the robot control system runs the to-be-tested algorithm, generates next-step device control instructions according to the simulation environment information and the device state, and feeds back the next-step device control instructions to the simulation platform through the homogeneous interface layer, and the process is repeated until a monitoring end condition is met.

Description

Technical Field

[0001] This application relates to the fields of intelligent logistics and software testing technology, specifically to a warehouse robot simulation method, system, device, equipment, and storage medium. Background Technology

[0002] The Robot Control System (RCS) is the core of a modern intelligent warehouse automation system, responsible for directing and scheduling all automated equipment (such as autonomous mobile robots, elevators, conveyor lines, etc.) to work together efficiently. Before the RCS system is officially deployed to the customer's site, its core algorithms (such as task scheduling, path planning, and traffic control) must undergo comprehensive and reliable testing and verification to ensure their correctness, stability, and performance.

[0003] Currently, the industry mainly uses physical simulation software for simulation testing, which requires users to "reconstruct and model" the algorithm logic of RCS in the simulation environment, that is, to rebuild the simulation model based on the algorithm principle. However, this method tests the "model of the algorithm" rather than the "algorithm itself", which is very different from the real operating environment, resulting in low test fidelity. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, device, equipment, and storage medium for warehousing robot simulation.

[0005] To achieve the above objectives, the first aspect of this application provides a warehouse robot simulation method, the method comprising: The simulation platform sends simulation environment information and the business tasks to be executed to the robot control system through a homogeneous interface layer; The robot control system runs the algorithm to be tested, calculates the device control instructions for performing business tasks based on the simulation environment information, and sends the device control instructions to the simulation platform through the homogeneous interface layer; The simulation platform determines the target future event corresponding to the device control command, advances the simulation clock based on the target future event, and sends the updated device status to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm under test, generates the next equipment control command based on the simulation environment information and equipment status, and feeds it back to the simulation platform through the homogeneous interface layer. This process is repeated until the monitoring termination condition is met.

[0006] In this embodiment, the simulation platform determines the target future event corresponding to the device control command, including: the simulation platform receiving the execution duration and congestion duration corresponding to the device control command sent by the robot control system through the homogeneous interface layer; the device behavior simulator of the simulation platform calculating the predicted execution duration based on the execution duration and congestion duration, and determining the predicted execution result corresponding to the device control command; and the device behavior simulator of the simulation platform extracting the target future event from the future event list based on the predicted execution duration and predicted execution result.

[0007] In this embodiment of the application, the method further includes: the robot control system running the algorithm to be tested, calculating the planned paths of multiple warehouse robots performing business tasks based on the simulation environment information, and the execution time required for each warehouse robot to execute the planned path; the robot control system performing traffic management based on the planned paths of all warehouse robots, and determining the congestion time for each warehouse robot to execute the planned path based on the traffic management results; the robot control system summarizing the execution time and congestion time corresponding to all warehouse robots, and sending them to the simulation platform through the homogeneous interface layer.

[0008] In this embodiment of the application, advancing the simulation clock based on a target future event includes: when the event scheduling engine of the simulation platform processes a target future event, advancing the simulation clock to the event occurrence time corresponding to the target future event.

[0009] In this embodiment, the simulation platform sends simulation environment information and pending business tasks to the robot control system through a homogeneous interface layer. This includes: the simulation platform initializing multiple sets of parallel simulation environment information based on the configuration file input by the user, and sending the simulation environment information to the robot control system through the homogeneous interface layer; and the upstream system simulator of the simulation platform sending pending business tasks to the robot control system through the homogeneous interface layer.

[0010] In this embodiment of the application, the method further includes: for each set of simulation environment information, the simulation platform collects interaction logs, device status and task lifecycle during the execution of business tasks, and calculates corresponding evaluation indicators based on the interaction logs, device status and task lifecycle; the simulation platform summarizes the evaluation indicators corresponding to multiple sets of simulation environment information and generates a comparative analysis report.

[0011] A second aspect of this application provides a warehouse robot simulation system, including a simulation platform, a homogeneous interface layer, and a robot control system. The simulation platform sends simulation environment information and the business task to be executed to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm to be tested, calculates the equipment control instructions for executing the business task based on the simulation environment information, and sends the equipment control instructions to the simulation platform through the homogeneous interface layer. The simulation platform determines the target future event corresponding to the equipment control instructions, advances the simulation clock based on the target future event, and sends the updated equipment status to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm to be tested, generates the next equipment control instruction based on the simulation environment information and equipment status, and feeds it back to the simulation platform through the homogeneous interface layer. This process is repeated until the monitoring termination condition is met.

[0012] A third aspect of this application provides a warehouse robot simulation device, comprising: a memory configured to store instructions; a processor configured to retrieve instructions from the memory and to implement a warehouse robot simulation method when executing the instructions.

[0013] The fourth aspect of this application provides a warehouse robot simulation device, including: a warehouse robot simulation apparatus.

[0014] The fifth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to execute a warehouse robot simulation method.

[0015] The above solution, through a homogeneous interface layer, enables the simulation platform to directly send simulation environment information and pending business tasks to the robot control system. The robot control system runs the algorithm under test, calculates equipment control commands based on this information, and feeds them back to the simulation platform for simulation. The entire process eliminates the need to remodel the algorithm logic; instead, the algorithm directly takes effect upon receiving the simulation environment information and business tasks, generating control commands. This truly tests the algorithm itself, rather than its model, thus more closely reflecting its performance in actual operation and fundamentally ensuring the accuracy and reliability of the test results. Furthermore, this solution uses a target future event to advance the simulation clock. This discrete event-driven accelerated simulation mechanism can compress peak business operation periods lasting several hours or even tens of hours into just a few minutes, significantly improving testing efficiency.

[0016] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 A schematic diagram illustrating the process of a warehouse robot simulation method according to an embodiment of this application is shown. Figure 2 This schematically illustrates a first simplified flowchart of a warehouse robot simulation method according to an embodiment of this application; Figure 3 This schematically illustrates a second simplified flowchart of a warehouse robot simulation method according to an embodiment of this application; Figure 4 A schematic diagram of a warehouse robot simulation device according to an embodiment of this application is shown. Figure 5 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Figure 1 The illustration shows a schematic flowchart of a warehouse robot simulation method according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a warehouse robot simulation method, which may include the following steps: Step 101: The simulation platform sends simulation environment information and the business tasks to be executed to the robot control system through the homogeneous interface layer.

[0020] The simulation platform is a virtual environment system built on computer technology, capable of simulating various real-world scenarios. It provides a virtual space for testing, algorithm verification, and performance evaluation of the robot control system, without relying on actual physical environments or robot hardware. The homogeneous interface layer is a unified interface layer for data interaction between the simulation platform and the robot control system. It ensures that the two systems can transmit and communicate data in a consistent and standardized manner, thus forming a high-fidelity, high-efficiency closed-loop "software-in-the-loop" testing environment. The robot control system is responsible for receiving external information, running specific algorithms, and generating control commands based on this information and algorithms to drive the robot to perform corresponding actions, thereby achieving functions such as material handling.

[0021] Optionally, within the simulation platform, a virtual warehousing environment, including map topology, shelves, and initial equipment states, is constructed based on testing requirements and the robot's application scenario, thereby generating complete simulation environment information. Simultaneously, the simulation platform defines the business tasks to be executed, such as specifying the target location the robot needs to reach and the objects to be moved. Subsequently, the simulation platform encapsulates the generated simulation environment information and the business tasks to be executed according to the data format defined by the homogeneous interface layer, and then sends the encapsulated data to the robot control system according to the communication protocol defined by the homogeneous interface layer. Regarding the communication protocol, an HTTP-based RESTful API can be used, or it can be replaced with other high-performance communication protocols, such as gRPC or WebSocket. gRPC provides more efficient serialization and HTTP / 2-based streaming communication, achieving better performance in large-scale simulations. WebSocket is more suitable for scenarios with high requirements for real-time and low-latency bidirectional communication.

[0022] For example, users write configuration files according to their testing requirements. After the simulation platform starts, it reads the user-input configuration file, initializes multiple sets of parallel simulation environment information accordingly, and sends them to the robot control system through a homogeneous interface layer. Through the configuration file, the simulation platform can easily construct arbitrarily complex warehouse layouts and extreme operating conditions (such as thousands of robots operating concurrently, congestion on specific paths, or collective equipment failures), so as to fully verify the robustness and boundary handling capabilities of the algorithm under test, thereby improving the quality of the final deployed software.

[0023] Furthermore, after the upstream system simulator starts, it generates business tasks to be executed based on preset business rules and scenario requirements, and sends them to the robot control system through the homogeneous interface layer. This solution simulates real business tasks through the upstream system simulator, enabling a more accurate evaluation of the robot's performance in actual business scenarios, thereby improving the practicality and effectiveness of the simulation.

[0024] Step 102: The robot control system runs the algorithm to be tested, calculates the device control instructions for performing business tasks based on the simulation environment information, and sends the device control instructions to the simulation platform through the homogeneous interface layer.

[0025] The algorithm under test is a set of computational rules designed for specific tasks of the robot, such as task scheduling algorithms, path planning algorithms, and traffic control algorithms. Before being put into practical application, it needs to be tested and verified in a simulation environment to ensure the reliability and effectiveness of the algorithm.

[0026] Optionally, after receiving the simulation environment information and the business task to be executed through the homogeneous interface layer, the robot control system parses and verifies them. Then, it runs the algorithm to be tested, calculates the device control instructions for executing the business task based on the simulation environment information, and sends the instructions to the simulation platform according to the communication protocol specified by the homogeneous interface layer.

[0027] Step 103: The simulation platform determines the target future event corresponding to the device control command, advances the simulation clock based on the target future event, and sends the updated device status to the robot control system through the homogeneous interface layer.

[0028] Among them, the target future event is an event that is expected to occur in the simulation environment, determined by the device control instructions, and reflects the state that the device will reach at a certain future time after executing the instructions. The simulation clock is a mechanism in the simulation platform used to simulate the passage of time, enabling various state changes in the simulation environment to proceed in an orderly manner in time.

[0029] Optionally, after receiving device control commands through the homogeneous interface layer, the simulation platform parses and verifies them. Then, based on the content of the command and the current state of the simulation environment, it determines the target future event corresponding to the command.

[0030] Furthermore, based on the determined target future event, the simulation platform calculates the time interval required from the current moment to the occurrence of the target future event. Then, triggered by an external event or an internal event scheduling engine, the simulation clock advances according to this time interval. During this advancement, the simulation platform updates the status of each device and environmental parameters in the simulation environment to simulate the impact of time on the devices and the environment. After the simulation clock advances to the moment the target future event occurs, the simulation platform updates the status of relevant devices in the simulation environment according to the specific circumstances of the target future event, including but not limited to the robot reaching a new location and changes in battery power. Finally, the simulation platform sends the updated device status information to the robot control system through a homogeneous interface layer.

[0031] In one feasible implementation, the robot control system runs the algorithm to be tested, calculates the planned paths of multiple warehouse robots performing business tasks based on the simulation environment information, and the execution time required for each warehouse robot to execute the planned path; the robot control system performs traffic management based on the planned paths of all warehouse robots, and determines the congestion time for each warehouse robot to execute the planned path based on the traffic management results; the robot control system summarizes the execution time and congestion time corresponding to all warehouse robots, and sends them to the simulation platform through the homogeneous interface layer.

[0032] Execution time refers to the time required for a device control command to complete its intended action under ideal conditions, without considering the impact of external factors on the execution process. Congestion time refers to the additional time delay that may occur during execution due to factors such as other devices, obstacles, or resource competition.

[0033] First, the robot control system calculates and plans a path for each warehouse robot using the algorithm under test, based on the acquired simulation environment information and business tasks (such as the starting and ending points of cargo handling).

[0034] On one hand, for each planned path, the robot control system estimates the execution time required for the warehouse robot to execute the planned path, taking into account parameters such as the robot's movement speed. On the other hand, the robot control system performs traffic management based on the planned paths of all warehouse robots. For example, it uses traffic management algorithms to comprehensively consider factors such as the relative position, direction of movement, and speed between robots to predict potential congestion. When a potential congestion point is detected, the traffic management algorithm formulates corresponding avoidance strategies, such as having some robots stop and wait, thereby determining the congestion time for each robot during the execution of its planned path.

[0035] Finally, the robot control system will summarize the execution time and congestion time of each warehouse robot and send them to the simulation platform through the homogeneous interface layer, so that the simulation platform can determine the target future events corresponding to the equipment control commands based on the execution time and congestion time.

[0036] In the above scheme, the robot control system can calculate and plan paths for each warehouse robot based on simulation environment information and determine congestion duration by comprehensively considering traffic management results. This comprehensive planning method can avoid collisions and congestion between robots, optimize robot travel routes, and improve robot operating efficiency.

[0037] In one feasible implementation, the simulation platform determines the target future event corresponding to the device control command, including: the simulation platform receiving the execution duration and congestion duration corresponding to the device control command sent by the robot control system through a homogeneous interface layer; the device behavior simulator of the simulation platform calculating the predicted execution duration based on the execution duration and congestion duration, and determining the predicted execution result corresponding to the device control command; and the device behavior simulator of the simulation platform extracting the target future event from the future event list based on the predicted execution duration and predicted execution result.

[0038] Specifically, after receiving the execution time and congestion time data, the device behavior simulator adds them together to obtain the predicted execution time. Simultaneously, the simulator also analyzes the predicted execution results after the device performs its tasks, based on the device's performance characteristics and the specific requirements of the device control commands, such as whether the robot has reached a new location or whether the battery level has changed.

[0039] Furthermore, the device behavior simulator accesses a list of future events in the simulation platform based on the predicted execution duration and the predicted execution result. Within this list, the simulator defines a time range based on the predicted execution duration and filters out events that may occur within this range. Then, combining the predicted execution result, it further extracts target future events associated with the device control commands from the filtered events. For example, if the predicted execution result is that the robot successfully reaches a certain shelf location, then target future events might include changes in the state of goods on that shelf, interactions between the robot and other robots, etc.

[0040] In the above scheme, the simulation platform calculates the predicted execution time by combining the execution time of the integrated equipment control commands with the congestion time. Combining these two factors allows for a more accurate simulation of the equipment's operating state in real-world scenarios, making the predicted execution time closer to reality. Based on the predicted execution time and execution results, the target future event is determined, and it can be accurately extracted from the future event list, ensuring the simulation's time synchronization accuracy.

[0041] In one feasible implementation, when the event scheduling engine of the simulation platform processes a target future event, it advances the simulation clock to the event occurrence time corresponding to the target future event.

[0042] Specifically, when the event scheduling engine within the simulation platform processes a target future event, it advances the simulation clock to the time point corresponding to the occurrence of the target future event. For example, if the target future event is expected to occur at the 10th second of the simulation time, the event scheduling engine will advance the simulation clock directly from the current time to 10 seconds.

[0043] This setup differs significantly from the fixed-step approach used in traditional simulations. Traditional methods advance the simulation clock at preset time intervals regardless of whether significant events occur. This approach, however, advances the simulation clock based on target future events, precisely pushing the time to the point in time when critical events occur. This avoids unnecessary time steps, improves time utilization efficiency, and makes the simulation process more compact and efficient.

[0044] Step 104: The robot control system runs the algorithm to be tested, generates the next equipment control command based on the simulation environment information and equipment status, and feeds it back to the simulation platform through the homogeneous interface layer. This process is repeated until the monitoring termination condition is met.

[0045] Specifically, after receiving the updated device status from the simulation platform, the robot control system runs the algorithm under test again to calculate the new device status and simulation environment information, generating the next device control command. This data interaction and feedback process is then repeated: the robot control system transmits the new device control command to the simulation platform through the homogeneous interface layer, the simulation platform updates the device status, and then feeds it back to the robot control system. This cycle repeats, forming a complete, dynamic decision-making and execution closed loop until the monitoring termination condition is met. The monitoring termination condition can be the completion of virtual time or the number of tasks, which is not limited here.

[0046] Furthermore, for scenarios involving multiple parallel simulation environments, a data acquisition and analysis reporting mechanism can be set up. The specific steps are as follows: The simulation platform simultaneously starts running multiple simulation environments, allowing business tasks to begin execution in each environment. During task execution, the simulation platform records the interaction information between components in real time, generates interaction logs, and continuously monitors the operating status of devices in the simulation environment. Simultaneously, the entire process of the business task from start to finish is tracked to obtain the task lifecycle. Based on this, the collected interaction logs, device status, and task lifecycle are deeply analyzed to calculate corresponding evaluation indicators, such as task completion rate, robot utilization rate, and deadlock count, and a structured comparative analysis report is generated. Through this comparative analysis report, not only can problems be quickly reproduced and root cause analysis performed, but it also provides strong data support for algorithm optimization and solution planning.

[0047] The above solution, through a homogeneous interface layer, enables the simulation platform to directly send simulation environment information and pending business tasks to the robot control system. The robot control system runs the algorithm under test, calculates equipment control commands based on this information, and feeds them back to the simulation platform for simulation. The entire process eliminates the need to remodel the algorithm logic; instead, the algorithm directly takes effect upon receiving the simulation environment information and business tasks, generating control commands. This truly tests the algorithm itself, rather than its model, thus more closely reflecting its performance in actual operation and fundamentally ensuring the accuracy and reliability of the test results. Furthermore, this solution uses a target future event to advance the simulation clock. This discrete event-driven accelerated simulation mechanism can compress peak business operation periods lasting several hours or even tens of hours into just a few minutes, significantly improving testing efficiency.

[0048] For example, to help understand the implementation process of the warehouse robot simulation method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 2 , Figure 2 A first simplified flowchart of a warehouse robot simulation method is provided, specifically: (1) Define simulation test cases: Define the scope and objectives of the test, including maps, tasks, and device performance.

[0049] (2) Create simulation test: Establish a control group and termination criteria to provide standards for subsequent test comparisons and termination.

[0050] (3) Start the simulation: Multiple test environments can be instantiated in parallel to improve testing efficiency and coverage.

[0051] (4) Initialize the simulation environment: Synchronize map and device information, and configure RCS acceleration to ensure the consistency and efficiency of the testing environment.

[0052] (5) Perform simulation closed-loop testing: The simulation engine simulates tasks sent by the upstream system, such as container delivery tasks.

[0053] The RCS makes decisions based on the received tasks and issues specific control commands.

[0054] The simulation engine receives instructions, calculates the time consumption, and generates future events.

[0055] Simulated clocks are driven by future events.

[0056] The simulation engine executes events and feeds back updated device status and other information to the RCS through the homogeneous interface layer, forming a closed-loop test system.

[0057] RCS receives feedback and makes the next decision.

[0058] (6) Monitoring termination conditions: The test termination conditions are monitored in real time, such as reaching the virtual time or the number of tasks. Once the conditions are met, the test is terminated.

[0059] (7) Aggregate data and generate analysis reports: Collect data during the testing process, conduct comparative analysis of key performance indicators across multiple control groups, and generate detailed analysis reports.

[0060] For example, to help understand the implementation process of the warehouse robot simulation method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 3 , Figure 3 A second simplified flowchart of a warehouse robot simulation method is provided, specifically: The test case and test management module defines test cases and passes them to the simulation orchestration and scheduling module.

[0061] The simulation orchestration and scheduling module instantiates and schedules simulation instances in the server cluster based on test cases.

[0062] The simulation engine sends tasks to the RCS instance under test through a homogeneous interface layer (RESTful API).

[0063] The RCS instance under test issues commands to the simulation engine through the homogeneous interface layer (RESTful API). The device behavior simulator in the simulation engine simulates device behavior according to the commands, advances the simulation clock, and feeds back the updated state to the RCS under test through the homogeneous interface layer (RESTful API).

[0064] The data acquisition and analysis module collects relevant logs and data, analyzes them, and generates reports.

[0065] Figure 1 This is a flowchart illustrating a warehouse robot simulation method in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0066] In one embodiment, a warehouse robot simulation system is provided, including a simulation platform, a homogeneous interface layer, and a robot control system. The simulation platform sends simulation environment information and the business task to be executed to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm to be tested, calculates the equipment control instructions for executing the business task based on the simulation environment information, and sends the equipment control instructions to the simulation platform through the homogeneous interface layer. The simulation platform determines the target future event corresponding to the equipment control instructions, advances the simulation clock based on the target future event, and sends the updated equipment status to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm to be tested, generates the next equipment control instruction based on the simulation environment information and equipment status, and feeds it back to the simulation platform through the homogeneous interface layer. This process is repeated until the monitoring termination condition is met.

[0067] In one embodiment, such as Figure 4 As shown, a warehouse robot simulation device 400 is provided, including an information sending module 401, a test algorithm running module 402, a simulation clock advancing module 403, and a monitoring end condition determination module 404, wherein: The information sending module 401 is used by the simulation platform to send simulation environment information and business tasks to be executed to the robot control system through the homogeneous interface layer.

[0068] The algorithm under test running module 402 is used to run the algorithm under test in the robot control system, calculate the device control instructions for executing business tasks based on the simulation environment information, and send the device control instructions to the simulation platform through the homogeneous interface layer.

[0069] The simulation clock advance module 403 is used by the simulation platform to determine the target future event corresponding to the device control command, advance the simulation clock based on the target future event, and send the updated device status to the robot control system through the homogeneous interface layer.

[0070] The monitoring termination condition determination module 404 is used to run the algorithm to be tested in the robot control system, generate the next equipment control command based on the simulation environment information and equipment status, and feed it back to the simulation platform through the homogeneous interface layer. This process is repeated until the monitoring termination condition is met.

[0071] The warehouse robot simulation device includes a processor and a memory. The aforementioned information sending module 401, the algorithm running module 402, the simulation clock advancement module 403, and the monitoring end condition determination module 404 are all stored in the memory as program units. The processor executes the aforementioned program modules stored in the memory to implement the corresponding functions.

[0072] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and the simulation method for the warehouse robot can be implemented by adjusting the kernel parameters.

[0073] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0074] This application provides a storage medium on which a program is stored, which, when executed by a processor, implements the above-described warehouse robot simulation method.

[0075] This application provides a processor for running a program, wherein the program executes the above-described warehouse robot simulation method during runtime.

[0076] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor A01, a network interface A02, a memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores simulation environment information, etc. The network interface A02 is used for communication with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements a warehouse robot simulation method.

[0077] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0078] This application provides a computer (electronic) device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above warehouse robot simulation methods.

[0079] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing a program that initializes a method for simulating a warehouse robot.

[0080] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0085] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0086] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0087] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0088] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A warehouse robot simulation method, characterized in that, The method includes: The simulation platform sends simulation environment information and the business tasks to be executed to the robot control system through a homogeneous interface layer; The robot control system runs the algorithm to be tested, calculates the device control instructions for executing the business task based on the simulation environment information, and sends the device control instructions to the simulation platform through the homogeneous interface layer; The simulation platform determines the target future event corresponding to the device control command, advances the simulation clock based on the target future event, and sends the updated device status to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm under test, generates the next device control command based on the simulation environment information and the device status, and feeds it back to the simulation platform through the isomorphic interface layer. This process is repeated until the monitoring end condition is met.

2. The warehouse robot simulation method according to claim 1, characterized in that, The simulation platform determines the target future event corresponding to the device control command, including: The simulation platform receives the execution time and congestion time corresponding to the equipment control commands sent by the robot control system through the homogeneous interface layer; The device behavior simulator of the simulation platform calculates the predicted execution time based on the execution time and the congestion time, and determines the predicted execution result corresponding to the device control command; The device behavior simulator of the simulation platform extracts the target future event from the list of future events based on the predicted execution duration and the predicted execution result.

3. The warehouse robot simulation method according to claim 2, characterized in that, The method further includes: The robot control system runs the algorithm under test and calculates the planned paths of multiple warehouse robots that perform the business tasks, as well as the execution time required for each warehouse robot to execute the planned path, based on the simulation environment information. The robot control system performs traffic management based on the planned paths of all the warehouse robots, and determines the congestion duration for each warehouse robot to execute the planned path based on the traffic management results. The robot control system aggregates the execution time and congestion time of all the warehouse robots and sends them to the simulation platform through the isomorphic interface layer.

4. The warehouse robot simulation method according to claim 1, characterized in that, The simulation clock is driven by the target future events, including: When the event scheduling engine of the simulation platform processes the target future event, it advances the simulation clock to the event occurrence time corresponding to the target future event.

5. The warehouse robot simulation method according to claim 1, characterized in that, The simulation platform sends simulation environment information and pending business tasks to the robot control system through a homogeneous interface layer, including: The simulation platform initializes multiple sets of parallel simulation environment information according to the configuration file input by the user, and sends the simulation environment information to the robot control system through the homogeneous interface layer; The upstream system simulator of the simulation platform sends the business tasks to be executed to the robot control system through the homogeneous interface layer.

6. The warehouse robot simulation method according to claim 5, characterized in that, The method further includes: For each set of simulation environment information, the simulation platform collects interaction logs, device status, and task lifecycle during the execution of the business task, and calculates corresponding evaluation indicators based on the interaction logs, device status, and task lifecycle. The simulation platform aggregates evaluation indicators corresponding to multiple sets of simulation environment information and generates a comparative analysis report.

7. A warehouse robot simulation system, characterized in that, The system includes a simulation platform, a homogeneous interface layer, and a robot control system. The simulation platform sends simulation environment information and the business task to be executed to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm to be tested, calculates the device control command to execute the business task based on the simulation environment information, and sends the device control command to the simulation platform through the homogeneous interface layer. The simulation platform determines the target future event corresponding to the device control command, advances the simulation clock based on the target future event, and sends the updated device status to the robot control system through the homogeneous interface layer. The robot control system runs the algorithm under test, generates the next device control command based on the simulation environment information and the device status, and feeds it back to the simulation platform through the isomorphic interface layer. This process is repeated until the monitoring end condition is met.

8. A warehouse robot simulation device, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the warehouse robot simulation method according to any one of claims 1 to 6.

9. A warehouse robot simulation device, characterized in that, include: The warehouse robot simulation device according to claim 8.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, the instruction causes the processor to be configured to perform the warehouse robot simulation method according to any one of claims 1 to 6.

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