Systems and methods for analyzing the performance of autonomous robotic systems

Abstract

To provide a system and method for estimating the performance of an autonomous robot system configured to fulfill multiple line commands.SOLUTION: The autonomous robotic system includes multiple autonomous guided vehicles configured to transport one or more cases in an environment to fulfill multiple line commands. Some variations include: obtaining first input data; generating a simulation model based at least in part on the first input data; determining a travel duration of each of the multiple autonomous guided vehicles based on the execution of the simulation model; generating an analytical model based at least in part on the travel duration; and estimating the performance of the autonomous robotic system based on the execution of the analytical model.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001]

[0001] This disclosure generally relates to the field of autonomous robotic systems. In particular, this disclosure relates to the analysis of the performance of autonomous robotic mobile execution systems. [Background technology]

[0002]

[0002] With the rapid growth of e-commerce companies, the demand for improved warehouse management is increasing. At a high level, warehouse management includes determining warehouse layout, scheduling workers, managing inventory, and executing orders. Recently, rising costs, labor shortages, increasing customer demand, and other supply chain issues have made warehouse management more challenging. Some organizations are beginning to utilize autonomous robotic mobile execution systems to overcome these challenges.

[0003]

[0003] Typically, autonomous robotic mobile execution systems comprise autonomous guided vehicles configured to automate tasks of storing and retrieving goods and materials within an environment (e.g., a warehouse). Such systems significantly improve the productivity and efficiency of warehouses. However, existing autonomous robotic mobile execution systems have several limitations. Firstly, existing systems are designed to perform single-line instructions (e.g., instructions involving multiple units of the same product). Secondly, because the autonomous guided vehicles of existing systems are configured to transport entire shelves of instructions (e.g., entire shelves of products) between workstations and storage areas within a warehouse, there are significant limitations on shelf height and weight.

[0004]

[0004] Therefore, there is a need for improved autonomous robotic mobile execution systems designed to perform multi-line commands (e.g., commands involving units of different products) without imposing limitations on shelf height and weight. For this purpose, there is also an unmet need to quickly and accurately analyze the performance of these improved robotic mobile execution systems. [Brief explanation of the drawing]

[0005] [Figure 1]

[0005] Figure 1 shows an exemplary representation of an existing autonomous robot system comprising autonomous guided vehicles configured to transport shelves within an environment. [Figure 2]

[0006] Figure 2 shows an example of an improved autonomous guided vehicle for transporting individual cases between various locations in a warehouse. [Figure 3]

[0007] Figure 3 provides an illustration of considerations made when designing an improved autonomous robot system comprising autonomous guided vehicles configured to transport cases within a warehouse. [Figure 4]

[0008] Figure 4 provides a general illustration of techniques for analyzing the performance of the improved autonomous robot system described herein. [Figure 5]

[0009] Figure 5 illustrates an exemplary variation of a system for analyzing the performance of an improved autonomous robot system. [Figure 6A]

[0010] Figure 6A is a flowchart showing an exemplary simulation-based method implemented by a simulation module to determine the steady-state average travel duration of an autonomous guided vehicle. [Figure 6B] Figure 6B is a flowchart showing an exemplary simulation-based method implemented by a simulation module to determine the steady-state average travel duration of an autonomous guided vehicle. [Figure 7]

[0011] Figure 7 illustrates an exemplary analysis model constructed by an analysis module for analyzing the performance of an improved autonomous robot system. [Figure 8]

[0012] Figure 8 illustrates an exemplary network generated to solve the analysis model generated in Figure 7. [Figure 9]

[0013] Figure 9 is a flowchart illustrating an exemplary method for analyzing the performance of an improved autonomous robotic system. [Figure 10]

[0014] Figure 10 illustrates an exemplary scenario for implementing an improved autonomous robotic system in an exemplary warehouse. [Figure 11]

[0015] Figure 11 shows a comparison of the steady-state performance obtained from the techniques described herein and discrete-event simulations. [Modes for carrying out the invention]

[0006]

[0016] Non-limiting examples of various embodiments and variations of systems and methods for analyzing the performance of autonomous robotic systems are described herein and illustrated in the accompanying drawings.

[0007]

[0017] As used herein, “autonomous robotic system” may include an autonomous guided vehicle configured to automate tasks (e.g., storing and / or retrieving goods and materials to perform instructions) within an environment (e.g., a warehouse). More specifically, the “autonomous robotic system” described herein may include an autonomous guided vehicle that performs instructions within an environment given the specifications of the environment (e.g., the layout of a warehouse) and the specifications and / or configuration of resources within the environment (e.g., the number of resources in the warehouse, the operating hours of the resources in the warehouse, the configuration of the resources in the warehouse, etc.).

[0008]

[0018] Therefore, as used herein, “performance of an autonomous robotic system” can refer to the performance of the autonomous guided vehicle and the performance of the environment (e.g., the performance of resources in the environment) so that the automated task is completed (e.g., commands are carried out).

[0009]

[0019] This specification describes a system and method for estimating the performance of an autonomous robotic system configured to perform multi-line commands. The autonomous robotic system comprises a plurality of autonomous guided vehicles configured to perform multi-line commands by transporting one or more cases within an environment. In some variations, the method includes acquiring first input data; generating a simulation model based at least in part on the first input data; determining the travel duration of each of the plurality of autonomous guided vehicles based on the execution of the simulation model; generating an analysis model based at least in part on the travel durations; and estimating the performance of the autonomous robotic system based on the execution of the analysis model. The first input data includes data associated with the operation of each of the plurality of autonomous guided vehicles and data representing the layout of the environment.

[0010]

[0020] According to one embodiment, a computer implementation method is provided for estimating the performance of an autonomous robot system configured to perform multi-line instructions. The autonomous robot system comprises a plurality of autonomous guided vehicles configured to perform multi-line instructions by transporting one or more cases within an environment. The method comprises acquiring first input data via a user interface. The first input data includes data associated with the operation of each of the plurality of autonomous guided vehicles and data representing the layout of the environment. The method further comprises generating a simulation model configured to simulate the operation of each of the plurality of autonomous guided vehicles within the environment, at least in part on the first input data. The method further comprises determining the duration of movement of each of the plurality of autonomous guided vehicles from a first location among one or more locations within the environment to a second location among one or more locations within the environment, based on the execution of the simulation model. The method further comprises generating an analysis model configured to analyze the performance of the autonomous robot system, at least in part on the duration of movement, and estimating the performance of the autonomous robot system, based on the execution of the analysis model.

[0011]

[0021] In some variations, estimating the performance of an autonomous robotic system involves calculating the throughput time required to execute multi-line instructions. In addition, or instead, estimating the performance of an autonomous robotic system involves calculating the proportion of time that one or more resources in the environment are utilized to execute multi-line instructions.

[0012]

[0022] In some variations, the data associated with the operation of each of the multiple autonomous guided vehicles includes, for each autonomous guided vehicle, at least one of the following: instructions from an instruction queue from which one or more cases are retrieved by the autonomous guided vehicle; indications from the instruction queue in a single movement that show how many of the one or more cases are retrieved; and the movement speed of the autonomous guided vehicle.

[0013]

[0023] In some variations, determining the travel duration includes determining, for each of several autonomous guidance vehicles, at least one of the following: a first travel duration from the current position of the autonomous guidance vehicle to the position of one or more cases; a second travel duration from the position of one or more cases to the position of a workstation; a third travel duration from the position of the workstation to a storage position; and a fourth travel duration from the storage position to a charging position. In some variations, determining the travel duration further includes sampling the first travel duration, the second travel duration, the third travel duration, and the fourth travel duration for several different types of line instructions, and determining the average travel duration based on the sampling.

[0014]

[0024] In some variations, the analytical model is a shared token multiclass semi-open queuing network. Generating the analytical model may further involve representing the process of matching one of several autonomous guided vehicles to a first multi-line instruction as a first synchronization node, the process of one or more cases being picked up by the autonomous guided vehicles as a second infinite service node, the process of the one or more cases being moved by the autonomous guided vehicles to the workstation as a third infinite service node, the process of a picker picking products from one or more cases at the workstation as a fourth service node, and the process of the autonomous guided vehicles being moved from the workstation to the storage location as a fifth infinite service node.

[0015]

[0025] In some variations, estimating the performance of an autonomous robotic system involves performing an approximate mean analysis of a shared token multiclass semi-open queuing network.

[0016]

[0026] According to another embodiment, a system is provided for estimating the performance of an autonomous robot system configured to perform multi-line commands. In some variations, the system comprises a user interface for acquiring first input data and at least one controller communicatively coupled to the user interface. The first input data includes data associated with the operation of each of a plurality of autonomous guided vehicles and data representing the layout of the environment. The autonomous robot system comprises a plurality of autonomous guided vehicles configured to transport one or more cases within the environment to perform multi-line commands. The controller is configured to generate a simulation model configured to simulate the operation of each of the plurality of autonomous guided vehicles in the environment, at least in part on the first input data; to determine the travel duration of each of the plurality of autonomous guided vehicles from a first position among one or more locations in the environment to a second position among one or more locations in the environment, based on the execution of the simulation model; to generate an analysis model configured to analyze the performance of the autonomous robot system, at least in part on the travel duration; and to estimate the performance of the autonomous robot system, based on the execution of the analysis model.

[0017]

[0027] In some variations, the controller is configured to estimate the performance of the autonomous robot system by calculating the throughput time required to execute multi-line instructions. Alternatively, the controller is configured to estimate the performance of the autonomous robot system by calculating the proportion of time that one or more resources in the environment are utilized to execute multi-line instructions.

[0018]

[0028] In some variations, the data associated with the operation of each of the multiple autonomous guided vehicles includes, for each autonomous guided vehicle, at least one of the following: commands from a command queue from which one or more cases are retrieved by the autonomous guided vehicle; indications from the command queue in a single movement that show how many of the one or more cases are retrieved; and the movement speed of the autonomous guided vehicle.

[0019]

[0029] In some variations, the controller may be configured to determine, for each of several autonomous guidance vehicles, at least one of the following: a first travel time from the current position of the autonomous guidance vehicle to the position of one or more cases; a second travel time from the position of one or more cases to the position of a workstation; a third travel time from the position of the workstation to a storage position; and a fourth travel time from the storage position to a charging position. The controller may further be configured to sample the first, second, third, and fourth travel times for different types of multi-line instructions and determine the average travel time based on the sampling.

[0020]

[0030] In some variations, the analytical model is a shared token multiclass semi-open queuing network. The controller may be configured to generate the analytical model by representing the process of matching one of several autonomous guided vehicles to a first multi-line instruction as the first synchronization node, the process of one or more cases being picked up by the autonomous guided vehicles as the second infinite service node, the process of the autonomous guided vehicles moving the picked-up one or more cases to the workstation as the third infinite service node, the process of a picker picking products from one or more cases at the workstation as the fourth service node, and the process of the autonomous guided vehicles moving from the workstation to storage as the fifth infinite service node.

[0021]

[0031] In some variations, the controller is further configured to perform an approximate mean analysis of a shared token multiclass semi-open queuing network to estimate the performance of the autonomous robot system.

[0022]

[0032] Autonomous robotic systems are widely used in warehouse management, particularly in the context of handling goods and materials within warehouses. However, as discussed above, existing autonomous robotic systems have several limitations. For example, existing autonomous robotic systems have autonomous guided vehicles configured to transport entire shelves containing products, which imposes limitations on shelf height and weight. This can reduce space utilization and negatively impact the efficiency of the autonomous robotic system's performance. Furthermore, if a command requires the execution of multiple different product units (referred to herein as "multi-line commands"), transporting an entire shelf means the autonomous guided vehicle will have to move between various locations in the warehouse (e.g., processing stations, central storage, dynamic storage, charging stations, etc.) multiple times. This increases the duration of movement, reducing the efficiency of the autonomous robotic system and impacting the efficiency of the warehouse's performance.

[0023]

[0033] Figure 1 shows an exemplary representation (e.g., a 2D representation) of an existing autonomous robotic system comprising an autonomous guided vehicle configured to transport shelves within an environment (e.g., a warehouse). As seen in Figure 1, the autonomous guided vehicle is configured to transport shelves between workstations or processing stations (collectively referred to herein as workstations 102), such as 102a, 102b, 102c, and 102d, and between a central storage facility 104 and a dynamic storage facility, such as the dynamic storage facility 106 shown in Figure 1.

[0024]

[0034] To overcome the challenges of existing autonomous robot systems, autonomous robot systems have been developed that feature autonomous guided vehicles that transport individual cases from shelves between various locations in a warehouse (e.g., processing stations, central storage, dynamic storage, charging stations, etc.). Figure 2 shows an exemplary autonomous guided vehicle transporting individual cases between various locations in a warehouse. Such autonomous guided vehicles are particularly efficient for performing multi-line instructions. In contrast to single-line instructions (e.g., instructions requiring multiple units of the same product), multi-line instructions are instructions for different products that may need to be stored and retrieved together. For example, multi-line products could be a group of products sold under different brand names that customers can distinguish. These instructions may require various units of different products. An improved autonomous robot system, featuring an autonomous guided vehicle (e.g., the autonomous guided vehicle shown in Figure 2) transporting individual cases within an environment (e.g., a warehouse), can perform multi-line instructions more efficiently than existing systems. As used herein, the term “Improved Autonomous Robot System” refers to an autonomous robot system including an autonomous guided vehicle, such as the one shown in Figure 2, configured to transport cases rather than entire shelves, so that commands are carried out given specifications of an environment (e.g., a warehouse) and / or specifications and / or configuration of resources within the environment. The autonomous guided vehicle transporting cases within a warehouse is also referred to herein as the “Robot.”

[0025]

[0035] However, when developing such improved autonomous robotic systems (for example, those equipped with autonomous guided vehicles to transport individual cases within a warehouse), it is crucial to analyze the performance of these autonomous robotic systems. Such analysis helps in determining the warehouse layout, resource allocation within the warehouse, operational design of the autonomous guided vehicles, and design of the autonomous robotic systems.

[0026]

[0036] Existing technologies for analyzing the performance of existing autonomous robot systems cannot be directly applied to these improved autonomous robot systems due to various technical challenges. For example, some existing technologies use discrete-event simulation to analyze the performance of autonomous robot systems. However, discrete-event simulation requires the analysis of a large amount of sample data to extract the possibilities of various events in order to determine the expected performance of the autonomous robot system. This makes discrete-event simulation computationally inefficient.

[0027]

[0037] Recently, techniques for implementing queuing networks have been used to analyze the performance of existing autonomous robot systems. While these techniques are more efficient (e.g., computationally efficient) than discrete-event simulations, they still present technical challenges. Firstly, existing methodologies for implementing queuing networks assume that the average travel time for autonomous guided vehicles between different locations in a warehouse is the same. Furthermore, these existing methodologies assume that the time required for autonomous guided vehicles to process instructions at a workstation (i.e., processing time at the workstation) is the same. In reality, the average travel time for autonomous guided vehicles and the processing time at the workstation depend on the instructions performed by the autonomous guided vehicle (e.g., the number of lines in the instructions). Therefore, such assumptions can affect the accuracy of the analysis.

[0028]

[0038] Secondly, existing methodologies for implementing queuing networks assume that the number of lines in an instruction (e.g., the number of products and / or items in a single instruction) follows the same probability density function. More specifically, existing methodologies assume that the probability density function of the number of lines in an instruction follows a geometric distribution. In reality, the number of lines in an instruction can follow any suitable form of distribution. Therefore, this assumption can further affect the accuracy of the analysis.

[0029]

[0039] Thirdly, existing methodologies for implementing queuing networks are configured to simply calculate the average travel time of an autonomous guided vehicle during an operational process (e.g., during the operation of an autonomous guided vehicle in an environment (e.g., a warehouse)). Essentially, these existing methodologies simply solve the common path problem for autonomous robot systems. In practice, when an improved autonomous robot system is deployed with an autonomous guided vehicle transporting cases from shelves, there are two other issues to consider besides the common path problem. Figure 3 gives an example of the considerations made when designing an improved autonomous robot system with an autonomous guided vehicle configured to transport cases in a warehouse. In Figure 3, for example, the capacity of the autonomous guided vehicle 352 is 3 cases. In other words, the autonomous guided vehicle 352 can transport a maximum of 3 cases in a single move. However, the instruction 354 to be performed involves 5 cases (represented, for example, 356 in Figure 3). Therefore, the autonomous guided vehicle 352 must make at least two moves to perform the instruction. Therefore, one consideration in such a situation is how to assign the cases to different moves. This is called the “assignment problem.” For example, as seen in Figure 3, one solution is to assign Case 1, Case 2, and Case 3 to the first move, and Case 4 and Case 5 to the second move. Similarly, another solution is to assign Case 5, Case 2, and Case 3 to the first move, and Case 1 and Case 4 to the second move. Another consideration in this situation is what the sequence of picking the cases within a move should be. This is called the “case retrieval problem.” For example, if Case 1 and Case 4 are assigned to the second move, in one solution, Case 1 can be picked up first, and Case 4 can be picked up after Case 1. Similarly, in another solution, Case 4 can be picked up first, and Case 1 can be picked up after Case 4. Considering the above problems, analyzing the average travel time of an autonomous guided vehicle using existing methodologies can be difficult and time-consuming.

[0030]

[0040] For the reasons mentioned above, existing methodologies are computationally inefficient and inaccurate for analyzing the performance of improved autonomous robotic systems, which include autonomous guided vehicles that store, retrieve, and transport cases within an environment (e.g., a warehouse). This specification describes a system and method for computationally fast and accurate analysis of the performance of these improved autonomous robotic systems. The technique described herein utilizes a combination of simulation modeling and analytical modeling to analyze the performance of improved autonomous robotic systems. Broadly speaking, the technique described herein generates a simulation model to simulate the operation of the autonomous guided vehicle. The duration of movement of the autonomous guided vehicle to various locations within the warehouse can be determined based on the execution of the simulation model. The technique described herein generates an analytical model based at least in part on the determined movement duration. The analytical model is then analyzed to estimate and / or predict the performance of the improved autonomous robotic system. Compared to existing methodologies, the technique described herein can achieve an accuracy of approximately 90 percent when estimating the performance of improved autonomous robotic systems. Furthermore, compared to existing methodologies, the techniques described herein are computationally faster (e.g., approximately 1000 times faster) when investigating the impact of command arrival configurations (e.g., average command arrival speed, probability density functions for commands of different line numbers, etc.) and resource specifications and availability (e.g., location and availability of autonomous guided vehicles, charging stations, picking stations, etc.) on the performance of improved autonomous robot systems.

[0031]

[0041] Figure 4 provides a rough example of the techniques described herein for analyzing the performance of an improved autonomous robotic system. As seen in Figure 4, the system and method described herein acquires input 462. Input 462 includes data representing the layout of the environment (e.g., warehouse layout), such as input 462a (i.e., system layout). Input 462 further includes data associated with the operation of each autonomous guided vehicle, such as input 462b (i.e., robot motion algorithm, multi-line instruction distribution, average instruction arrival speed). Input 462 also includes data associated with resources in the warehouse (e.g., resource specifications and configuration), such as input 462c (i.e., resource specifications, resource service time). The system and method described herein generates and runs a simulation and analysis composite model (e.g., model 464). Based on the execution of model 464, the system and method described herein outputs an estimated performance of the autonomous robotic system. Output 466 may include the instruction throughput time 466a, the maximum throughput of the autonomous robot system 466b, and the resource utilization rate in the warehouse 466c.

[0032] Exemplary System

[0042] Figure 5 illustrates an exemplary variation of system 500 for analyzing the performance of the improved autonomous robot system 582. A rough implementation of system 500 is described with reference to Figure 4 above. System 500 includes a user interface 572 configured to take inputs (e.g., input 462 as described in relation to Figure 4) and transmit outputs (e.g., output 466 as described in relation to Figure 4). The user interface 572 is coupled communicatively to a controller 574. In some variations, the outputs taken from the user interface 572 may be used by the improved autonomous robot system 582 to optimize the operation of the autonomous guided vehicle 584. Similarly, in such variations, the outputs taken by the user interface 572 may be used to optimize the layout and configuration of resources in a warehouse 592.

[0033]

[0043] The user interface 572 may allow users and / or computing devices to input data related to the layout of the environment (e.g., warehouse layout), the operation of the autonomous guided vehicle, and the specifications and configuration of resources within the environment (e.g., warehouse). More specifically, the user interface 572 may allow users and / or computing devices to input data representing the layout of the environment, such as a visual representation of the warehouse representing charging stations, workstations, central storage, dynamic storage, etc. The input data may also include data related to the operation of the autonomous guided vehicle, such as the distribution of lines in an instruction, the arrival speed of an instruction, the instruction in which a case is picked by the autonomous guided vehicle, an indication showing how many cases are picked in one move, and the speed at which the autonomous guided vehicle moves. The input data may also include data related to the specifications and / or configuration of resources within the environment, such as the configuration of a charging station, the service time of a charging station, the location of a picker, the service time of a picker, and the idle location of the autonomous guided vehicle. The input may be in any appropriate format (e.g., text, audio, images, video, numbers, or a combination thereof).

[0034]

[0044] In some cases, the user interface 572 may be rendered on any suitable computing device. Non-exclusive examples of computing devices include computers (e.g., desktops, personal computers, laptops, etc.), tablets, e-readers (e.g., Apple iPad®, Samsung Galaxy® Tab, Microsoft Surface®, Amazon Kindle®, etc.), mobile devices, and smartphones (e.g., Apple iPhone®, Samsung Galaxy®, Google Pixel®, etc.). Computing devices may be connected to the controller 574 via a network (e.g., the Internet, a local area network (LAN), a wide area network (WAN), etc.).

[0035]

[0045] In some variations, the controller 574 may include one or more servers and / or one or more processors operating on a cloud platform (e.g., Microsoft Azure®, Amazon® Web Services, IBM® Cloud Computing, etc.). The servers and / or processors may be any suitable processing devices configured to operate and / or execute a set of instructions or code, and may include one or more data processors, image processors, graphics processing units, digital signal processors, and / or central processing units. The servers and / or processors may be, for example, general-purpose processors, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc.

[0036]

[0046] In some variations, the controller 574 may include a processor (e.g., a CPU). The processor may be any suitable processing device configured to operate and / or execute a set of instructions or codes, and may include one or more data processors, image processors, graphics processing units, physical processing units, digital signal processors, and / or central processing units. The processor may be, for example, a general-purpose processor, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc. The processor may be configured to operate and / or execute application processes and / or other modules, processes and / or functions associated with the system and / or the network associated therewith. The underlying device technology may be provided in various component types (e.g., MOSFET technology such as complementary metal-oxide-semiconductor (CMOS), bipolar technology such as emitter-coupled logic (ECL), polymer technology (e.g., silicon-conjugated polymers and metal-conjugated polymer metal structures), and a mix of analog and digital, etc.).

[0037]

[0047] In some examples, the controller 574 may be configured to analyze the performance of the improved autonomous robot system. For example, the controller 574 may be configured to implement one or more modules of the system 100. One or more modules include a simulation module 574a and an analysis module 574b. Modules 574a and 574b may include instructions to perform one or more of the following: (1) generate a simulation model for simulating the operation of an autonomous guided vehicle of the improved autonomous robot system; (2) run the simulation model; (3) determine the duration of movement of the autonomous guided vehicle; (4) generate an analysis model for analyzing the performance of the improved autonomous robot system; and (5) analyze the analysis model for estimating the performance of the improved autonomous robot system.

[0038]

[0048] The controller 574 (for example, the controller's processor) may include instructions and / or software code for executing modules 574a and 574b. In some examples, the processor may execute both modules. In some examples, the instructions and / or software code may include separate calls to separate modules. A call to the first module may redirect the processing performed by the controller 574 to execute the instructions contained in that first module. After the execution of the first module, if the instructions and / or software code include a call to a second module, the processing may be redirected to execute the instructions contained in the second module. In some examples, the controller 574 may execute each module 574a and module 574b sequentially. Alternatively, the controller 574 may execute both modules 574a and module 574b simultaneously. In some examples, both modules 574a and module 574b may be combined into a single module. These modules 574a and module 574b and their functions are described in detail below.

[0039]

[0049] The output from the controller 574 is sent to the user interface 572. The output can be in any appropriate format (e.g., text, audio, video, images, numbers, or a combination thereof). The output may include performance metrics of the improved autonomous robot system, such as the throughput time of the improved autonomous robot system, the maximum throughput of the improved autonomous robot system, and the utilization rate of resources in the environment (e.g., a warehouse). The output can be used to determine the optimized layout of the warehouse 592, design the improved autonomous robot system 582, design the autonomous guided vehicle 584, determine the optimized configuration of resources in the warehouse 592, determine the optimized specifications of resources in the warehouse 592, and so on.

[0040] Simulation module

[0050] To analyze the performance of the improved autonomous robot system, a simulation module (for example, structurally and / or functionally similar to simulation module 574a in Figure 5) is configured to implement a simulation-based approach. In particular, the simulation modules described herein are configured to generate and run simulation models to determine the steady-state average travel duration of an autonomous guided vehicle for all movement processes involved in the operation of the autonomous robot system. For example, the simulation module is configured to generate and run simulation models to determine the travel duration of an autonomous guided vehicle for retrieving a case in a specific command, moving a case to a designated workstation, moving a case to a previous storage location (for example, moving an empty case from the workstation to the area where the case was first retrieved), moving to a battery charging station, and moving from the battery charging station to a station.

[0041]

[0051] The simulation model is generated and executed so that the average steady-state travel duration of the autonomous vehicle does not depend on the allocation of resources in the environment (e.g., the allocation of resources such as autonomous guided vehicles, pickers, charging stations, and chargers in a warehouse). Instead, these average steady-state travel durations depend on (1) the case retrieval policy within a travel (e.g., the sequence in which cases are retrieved within a travel), (2) the case allocation policy between travels (e.g., the number of cases allocated to each travel to perform an instruction), (3) the route planning instructions generated for the autonomous guided vehicle, (4) the travel speed of the autonomous guided vehicle, and (5) the layout of the environment (e.g., the layout of the warehouse).

[0042]

[0052] Figures 6A and 6B are flowcharts illustrating an exemplary simulation-based method implemented by the simulation module to determine the average steady-state travel duration of an autonomous guided vehicle. The simulation model is generated by the simulation module as shown in Figures 6A and 6B. At 612, an instruction to be performed arrives in the environment (e.g., arrives in a warehouse) and waits in the instruction queue. At 616, the simulation module randomly assigns an idle autonomous guided vehicle to the first waiting instruction.

[0043]

[0053] The command may require distributing multiple cases to various locations in the environment (e.g., a central storage area, a dynamic storage area in a warehouse). As discussed above, the maximum number of cases that an autonomous guided vehicle can carry in a single move may be limited (e.g., based on the capacity of the autonomous guided vehicle). Therefore, the autonomous guided vehicle may need to make multiple moves to complete the command. In 618, the autonomous guided vehicle determines a command to retrieve a case, and then in 620, the autonomous guided vehicle moves from its current location to the case that is the target of this move, which is stored on a shelf, according to a specific command. The command to retrieve a case, and / or the command for the autonomous guided vehicle to move to the target case, may be optimized or random. Alternatively, the command to retrieve a case may be centrally determined by a central system and communicated to the autonomous guided vehicle in question.

[0044]

[0054] In 622, the autonomous guided vehicle picks up all cases to be transported in a single move from the shelf and then transports those cases to a designated workstation. The number of workers in the workstation can be limited. In 634, the autonomous guided vehicle enters the workstation buffer and waits its turn.

[0045]

[0055] In step 626, when the picker removes a product from the case, in step 628, the autonomous guidance vehicle returns the empty case to its original storage location. The command to transport the empty case to its original storage location (referred to herein as the “case storage process”) may be random or optimized. In step 630, the autonomous guidance vehicle checks whether there are any remaining cases required to carry out the command. If there are remaining cases, the autonomous guidance vehicle returns to step 620. If there are no remaining cases, the command is released.

[0046]

[0056] At 636, the autonomous guided vehicle checks whether the remaining battery level is below a predefined threshold. If it is, the autonomous guided vehicle may need to go to a charging station. The number of autonomous guided vehicles that a charging station can service simultaneously may be limited. Therefore, if the number of autonomous guided vehicles at a charging station exceeds a certain limit, the autonomous guided vehicle may enter a workstation buffer at 646 and wait its turn. After charging is complete, the autonomous guided vehicle returns to the shelf storage area. At 636, if the remaining battery level is above the threshold, the autonomous guided vehicle enters an idle state and waits to be assigned another instruction (for example, at 644).

[0047]

[0057] After the simulation model is generated, the simulation module runs the simulation model to sample the movement duration of the autonomous guided vehicle. For example, the movement durations in steps 620, 622, 628, 638, and 642 are sampled for different types of commands and different types of movements that execute those commands. The simulation model runs out when the average of the movement durations collected from the samples in steps 620, 622, 628, 638, and 642 converges. For example, the simulation model may run out when the confidence interval of the average movement durations in steps 620, 622, 628, 638, and 642 falls below 1% of each of the respective average values. The converged movement durations are sent to the analysis module as input.

[0048] Analysis Module

[0058] To analyze the performance of the improved autonomous robot system, an analysis module (structurally and / or functionally similar to, for example, analysis module 574b in Figure 5) is configured to implement an analysis-based approach. In particular, the analysis modules described herein are configured to analyze the impact of multi-line instruction distribution, instruction arrival configuration, resource specifications and availability, resource service time, etc., on the performance of the improved autonomous robot system. More specifically, the mean travel duration is determined by the simulation module. Using this mean travel duration determined by the simulation module, the analysis module analytically determines the impact of the number of resources in the environment (e.g., robots, pickers, chargers), average instruction arrival rate, multi-line instruction distribution, and service time distribution at workstations and exchange stations on the performance of the improved autonomous robot system. As described in detail below, the analysis module constructs a shared token multi-class semi-open queuing network (SOQN) to analyze the impact of the above-described processes on the performance of the improved autonomous robot system. In some variations, the analysis module solves the SOQN by applying approximate mean analysis.

[0049]

[0059] Assumptions for generating the analysis model

[0050]

[0060] To generate the analysis model, the analysis module makes the following assumptions:

[0051]

[0061] (1) Instruction arrival follows a Poisson distribution. The average instruction arrival rate is defined as λ. Arriving instructions are served by autonomous guided vehicles on a first-come, first-served basis. Instructions may have multiple lines. Instructions are classified into different classes based on the number of lines in the instruction. The number of lines in an instruction ranges from 1 to N. l Let's assume it changes up to N. l There should be instructions of different classes. In this disclosure, O={1,...N} l} is N lIt is used to represent a set of indexes of instruction classes. For each r ∈ O, the probability that an arriving instruction belongs to class r is defined as p(r), and the average instruction arrival rate is λ r and is expressed as, and the number of lines in an instruction is N r is defined as, and the number of movements required to execute an instruction is NT r is defined in the present disclosure, T r ={1,...NT r} is used to represent a set of indexes of NT r movements during the execution of an instruction of class r.

[0052]

[0062] (2) Assume that there are N w workstations in the environment (for example, in a warehouse). The set of indexes of N w workstations can be represented as W = {1,...N w}.

[0053]

[0063] After the picker takes out the product from the case, the autonomous guided vehicle transports the case (for example, an empty case) from the workstation to the storage area (for example, the original storage area). When the autonomous guided vehicle returns the case to the original storage position (that is, the position where the case was first taken out), it waits at the final point of the storage process. The autonomous guided vehicle does not stop at a predetermined stopping point but follows the service completion point (POSC) stopping point policy. In other words, when the autonomous guided vehicle returns the case to the original storage position, instead of moving to a predetermined stopping point to wait for the next instruction, the autonomous guided vehicle waits at the final position until it matches a new / next instruction.

[0054]

[0064] The same autonomous guided vehicle completes the command. However, there is an upper limit to the number of cases that the autonomous guided vehicle can carry at one time. Therefore, if the number of cases in a command exceeds the capacity of the autonomous guided vehicle, multiple movements may be required to complete the command. These movements may include a case retrieval process (e.g., the process of retrieving cases from their original storage location), a workstation processing process (e.g., the process of processing cases at a workstation, such as a picker removing products from cases), and a case storage process (e.g., the process of returning empty cases to their original storage location for storage).

[0055]

[0065] The average processing time and coefficient of variation for a single case on a workstation are given by u, respectively. wi and cv wi It is shown as follows.

[0056]

[0066] Generation of analytical models

[0057]

[0067] Figure 7 illustrates an exemplary analytical model constructed by the analytical module described herein. The analytical model is used to analyze the performance of an improved autonomous robotic system. In some variations, the analytical model is a shared token multiclass semi-open queuing network (SOQN). In other words, the autonomous guided vehicle is constructed as a shared token. Furthermore, the autonomous guided vehicle is modeled as multiple types of customers in the queuing network, and since these customers execute different command lines from one another, a multiclass queuing network is constructed. In addition, commands are also modeled as customers in the queuing network. However, once a command is completed, it is assumed that the command leaves the queuing network, and the autonomous guided vehicle (likewise modeled as a customer) is assumed not to leave the queuing network, thus constructing a semi-open queuing network.

[0058]

[0068] An analytical model (for example, a shared token multiclass SOQN) can be generated by representing one or more operational processes of an improved autonomous robotic system as respective nodes. Some exemplary nodes include a synchronization node, an infinite service node, and a service node. A "service node" is thought to contain servers, which are resources configured to process or service customers. In some variations generating the analytical model, it is assumed that a server can serve one customer at a time. Thus, the capacity of a service node is assumed to be defined by the number of servers available to process or service customers. An "infinite service node" is a service node with an infinite number of servers.

[0059]

[0069] In some variations, generating the analytical model involves representing each of the movement processes, such as the process of an autonomous guided vehicle retrieving one or more cases, the process of the autonomous guided vehicle moving one or more retrieved cases to a workstation, and the process of the autonomous guided vehicle moving from the workstation to a storage location (e.g., the original location from which the cases were retrieved), as an infinite service node. This is because each of the autonomous guided vehicles can move instantly, as if there were an infinite number of servers (i.e., autonomous guided vehicles) serving customers within the service node, regardless of the number of autonomous guided vehicles (e.g., customers) arriving at each infinite service node simultaneously. Furthermore, generating the analytical model involves representing the process of a picker retrieving products from one or more cases at the workstation as a service node.

[0060]

[0070] In addition, or instead, generating the analysis model involves representing the process of matching autonomous guided vehicles to multiple line instructions as a synchronous node. As discussed above, the queuing network consists of both multiple autonomous guided vehicles and multiple instructions, both of which are customers. The synchronous node is configured to contain two queues. Each of the two queues within the synchronous node is considered both a server and a customer to each other.

[0061]

[0071] The generated analysis model is described below.

[0062]

[0072] As shown in Figure 7, in this exemplary analytical model, there are N autonomous guided vehicles within the improved autonomous robot system. When a command arrives in the system, it waits in command queue 782 and is then matched with an available autonomous guided vehicle. The autonomous guided vehicles act as shared tokens and can serve different multi-line commands. The matching process is modeled as a synchronous station (also referred to as a "synchronous node"), where there are two queues, namely command queue 782 and available autonomous guided vehicle queue 784, with at least one of the two queues being empty. As discussed above, the queuing network is constructed with both multiple autonomous guided vehicles and multiple commands, and both the multiple autonomous guided vehicles and multiple commands are customers. Thus, a synchronous node contains two queues (i.e., command queue 782 and available autonomous guided vehicle queue 784). Each of the two queues within the synchronous node is considered both a server and a customer for the other.

[0063]

[0073] In this analysis model, for each r∈O and t∈T rRegarding this, the autonomous guided vehicle picks the required cases from the shelves by movement t with specific instructions that can be random or optimized during the execution of class r instructions. The case retrieval process (e.g., the process of assigning a sequence in which cases are retrieved during movement and the process of retrieving cases according to this sequence) is modeled as an infinite service node (IS) (e.g., a node with infinite servers) because, once the autonomous guided vehicle matches an instruction, it can move immediately without waiting. The average movement time during this case retrieval process depends on the type of instruction r and the current movement t, and the average movement time for this node is

[0064]

number

[0065] That is the case.

[0066]

[0074] The autonomous guided vehicle moves from the last shelf visited in the retrieval process to a designated processing station (e.g., a designated workstation). For each r∈O,w i For ∈W, the instructions of class r are given on workstation w i The probability of selecting is,

[0067]

number

[0068] It is shown as follows: In an environment (for example, a warehouse), N w If there are N workstations, this migration process is w Modeled as an infinite number of service nodes. From shelf storage area to workstation w i The average time required to travel to is

[0069]

number

[0070] It is shown as follows.

[0071]

[0075] Autonomous guided vehicles, workstation w i Upon arrival, the product is added to a waiting queue, waits its turn, and then the picker retrieves the desired product from the case. If there is only one picker per workstation, the process is N with a single server at each node. w It is modeled as a number of service nodes (for example, a node with a finite number of servers). For each r∈O and t∈T r Regarding the average process time of autonomous guided vehicles,

[0072]

number

[0073] This also depends on the type of instruction r and the current movement t.

[0074]

[0076] After the process station service ends, the autonomous guided vehicle must return all picked cases to their original locations and store them according to specific instructions, which can be random or optimized. This movement process is also N w It is modeled as an infinite number of service nodes, where each r∈R and t∈T. r ,w i For ∈W, the average travel time during this process is:

[0075] [Number]

[0076] It is defined as follows.

[0077]

[0077] After the case storage process, probability

[0078]

number

[0079] Therefore, the autonomous guided vehicle may need to start with the case retrieval process and continue moving after t movements. Probability

[0080]

number

[0081] Then, after the movement, the autonomous guided vehicle enters an idle state and moves to autonomous guided vehicle queue 784 of the synchronization node, and the command is issued from the autonomous robot system (i.e., the warehouse has carried out the command). Probability

[0082]

number

[0083] Therefore, the autonomous guided vehicle needs to be recharged after moving. All of these probabilities depend on the current service commands and movement of the autonomous guided vehicle.

[0084]

[0078] When the autonomous guided vehicle is charging, the autonomous guided vehicle moves from the station to the charging station. This process is performed for an average travel duration

[0085]

number

[0086] It can also be modeled as an infinite service node.

[0087]

[0079] After arriving at the charging station, the autonomous guided vehicle joins the waiting queue and waits for its turn to charge. Assuming there is only one charging station, the process is N c It can be modeled as a service node with a server. The average charging time for one autonomous guided vehicle is

[0088]

number

[0089] That is the case.

[0090]

[0080] After charging is complete, the autonomous guided vehicle will have an average travel time

[0091]

number

[0092] The vehicle returns to the station, which is modeled as an infinite service node. The autonomous guided vehicle then enters an idle state and enters autonomous guided vehicle queue 784 of the synchronization node.

[0093]

[0081] Analysis of the operation of an autonomous guided vehicle using an analysis model

[0094]

[0082] The characteristics of each service node in the exemplary analysis model described in relation to Figure 7 (i.e., the shared token multiclass semi-open queuing network described above) are analyzed as follows:

[0095]

[0083] If we assume that the autonomous guided vehicle can carry as many of the cases required by the command as possible in each movement, then for each r∈O, the number of movements that may be required to perform command r (NT) r )teeth,

[0096]

number

[0097] It can be defined as follows.

[0098]

[0084] Here, N r r is the number of lines of type r instructions, C is the capacity of cases handled by the autonomous guided vehicle, and [.] is the rounding function. For each r∈O, t∈T rRegarding this, the number of cases required for an r-type instruction in movement t is NC r,t It can be defined as follows, and this can be calculated as equation (2).

[0099]

number

[0100]

[0085] Each r∈O, t∈T r Regarding this, the probability of continuing to the next move after completing move t.

[0101]

number

[0102] teeth,

[0103]

number

[0104] It can be defined as follows.

[0105]

[0086]

[0106]

number

[0107] The average travel time, including the time factor, can be calculated from samples obtained from the simulation module.

[0108]

[0087] The probability that an autonomous guided vehicle carrying an r-type command will head to charge after completing movement t.

[0109]

number

[0110] To calculate the mean time of movement (ATT) for an autonomous guided vehicle to execute an r-type command during movement t, r,t We need to calculate this, which is,

[0111]

number

[0112] That is the case.

[0113]

[0088] Battery consumption is assumed to be linearly related to travel time. The average battery consumption for an autonomous guided vehicle to perform commands is the average energy consumed when performing different command types over various travels.

[0114]

number

[0115]

[0089] Here, dr represents the percentage of battery depletion during movement.

[0116]

[0090] The probability that an autonomous guided vehicle carrying an r-type command will head to charge after completing movement t.

[0117]

number

[0118] teeth,

[0119]

number

[0120] This is shown in [the document].

[0121]

[0091] This is because a fully charged battery reaches a predefined battery threshold (th cThis is the reciprocal of the average number of instructions that can be supported before reaching ). If an autonomous guided vehicle still has remaining moves to complete instructions,

[0122]

number

[0123] It is set to 0.

[0124]

[0092] r∈O, t∈T r Regarding this, when executing an r-type instruction after completing movement t, the probability that the autonomous guided vehicle will become idle and go to autonomous guided vehicle queue 784 of the synchronization node is:

[0125]

number

[0126] That is the case.

[0127]

[0093] Regarding the r-type instruction in t movement, workstation w i Average service time in

[0128]

number

[0129] and the coefficient of variance

[0130]

number

[0131] This can be calculated based on input parameters (for example, the average service rate and the variance coefficient for pickers to pick items from cases), as shown in equations (8) and (9) below.

[0132]

number

[0133]

[0094] Analysis of the performance of an autonomous robot system based on an analytical model

[0134]

[0095] The performance of an autonomous robot system can be analyzed based on the execution of an analytical model. In some variations, analytical methods such as approximate mean analysis (AMVA) may be performed on the analytical model to estimate the performance of the autonomous robot system. More specifically, AMVA may be performed to solve the shared token multiclass semi-open queuing network described above.

[0135]

[0096] Based on the approach described herein, the processing times in the workstation and charging station follow a general distribution, so the queuing network described above can be considered to belong to the non-product form of queuing network. There is no exact solution to such a queuing method. Therefore, the shared token multiclass queuing network generated above can be solved using single-chain multiclass approximate mean analysis (AMVA).

[0136]

[0097] Before performing AMVA, for each r∈O, it is necessary to calculate the normalized average number of visits (also known as the visit rate) to all service nodes by autonomous guided vehicles executing instructions of type class r. The visit rate of SOQN is

[0137]

number

[0138] If selected to be normalized as follows,

[0139]

[0098] Here, V sync This indicates the rate of visits by autonomous guided vehicles to the synchronization node.

[0140]

number

[0141] This indicates the visit rate of autonomous guided vehicles executing class r-type instructions to the synchronization node. The probability of an r-type instruction to perform movement t is:

[0142]

number

[0143] It is calculated as follows.

[0144]

[0099] In all movements, the autonomous guided vehicle goes through the retrieval process, so the visit rate to the retrieval node of the autonomous guided vehicle executing a class r type instruction

[0145]

number

[0146] P r,t It is equal to . For other nodes in the movement, the visit rate is calculated based on equation (12). For subprocesses in the charging procedure, the visit rate of all charging nodes involved can be calculated as equation (13).

[0147]

number

[0148]

[0100] In some variations, AMVA can be used to solve SOQN using the following three steps.

[0149]

[0101] Step 1: By removing the synchronization station from the SOQN (e.g., the SOQN shown in FIG. 7), a closed queuing network (CQN) is created. This is shown in FIG. 8. This CQN can be analyzed with a single-chain multi-class AMVA. AMVA generates TH1, which is the throughput of the CQN using N robots.

[0150]

[0102] Step 2: By replacing the synchronization nodes in the SOQN with load-dependent service nodes, a second CQN is created. This service node is shown as node S + 1, assuming there are S nodes in the first CQN. Node S + 1 has a service rate u(n) = λ when n > 1, where n robots are at the station. The network is stable only when λ < TH1. When n = 1, the service rate is

[0151]

Number

[0152] as follows. Next, the same AMVA methodology can be used to analyze this second CQN. The output of the AMVA methodology is the expected waiting time i at different workstations w

[0153]

Number

[0154] and WT c at the charging station and the expected number of autonomous guided vehicles in the autonomous guided vehicle queue 784 at the synchronization node shown as N sync and the probability of n autonomous guided vehicles at workstation w i as follows.

[0155]

Number

[0156] The probability P of n autonomous guided vehicles at a charging station. c (n) and (n) are included.

[0157]

[0103] Step 3: The solution procedure involves analyzing the synchronization node in isolation and representing the average length of instructions in the instruction queue of the synchronization node, L O Calculate.

[0158]

[0104] Step 4 may be performed to estimate the performance of the autonomous robot system.

[0159]

[0105] Step 4: Based on the results obtained from Steps 2 and 3, calculate the instruction throughput time and resource utilization.

[0160]

[0106] Utilization rate of autonomous guidance vehicles (ρ r This calculates the percentage of autonomous guided vehicles that are busy and unavailable for command assignment. This can be calculated as equation (14).

[0161]

number

[0162]

[0107] Workstation utilization rate

[0163]

number

[0164] This calculates the percentage of time that one or more autonomous guided vehicles are present at the workstation. This can be calculated as equation (15).

[0165]

number

[0166]

[0108] The charging station has N c Since there are several chargers, the charger utilization rate at a charging station can be calculated using equation (16).

[0167]

number

[0168]

[0109] Instruction throughput time THT r This calculates the duration from the arrival of an r-type instruction until the departure of an r-type instruction. This takes into account the external instruction wait time, the average travel time of the autonomous guided vehicle for the retrieval and storage process, the service time at different workstations, and the wait time at different workstations. This can be calculated as equation (17). The overall instruction throughput can be calculated as equation (18).

[0169]

number

[0170]

[0110] In this way, the performance of the improved autonomous robot system can be calculated computationally quickly and accurately.

[0171] Exemplary Method

[0111] Figure 9 is a flowchart illustrating an exemplary method 900 for analyzing the performance of an improved autonomous robotic system. In step 902, the method 900 includes obtaining first input data via a user interface (for example, structurally and / or functionally similar to the user interface 572 in Figure 5). The first input data may include data associated with the operation of the autonomous guided vehicle (e.g., case retrieval policy, case assignment policy, route planning instructions, etc.) and data representing the layout of the environment. For example, the first input data may include (1) a case retrieval policy in a movement (e.g., the sequence in which cases are retrieved in a movement), (2) a case assignment policy between movements (e.g., the number of cases assigned for each movement to perform an instruction), (3) route planning instructions generated for the autonomous guided vehicle, (4) the speed of the autonomous guided vehicle, and (5) the layout of the environment (e.g., the layout of a warehouse).

[0172]

[0112] In 904, the method includes generating a simulation model via a controller (for example, structurally and / or functionally similar to controller 574 in Figure 5). In some variations, the simulation model may be generated via a module, such as simulation module 574a as described herein. More specifically, method 900 may generate a simulation model by performing the method described in Figures 6A and 6B.

[0173]

[0113] In 906, method 900 includes determining the travel duration of an autonomous guided vehicle based on the execution of the simulation model generated in 904. For example, method 900 may include determining the travel duration of an autonomous guided vehicle moving to a target shelf in order to retrieve a case in a computed instruction; determining the travel duration of an autonomous guided vehicle to transport the retrieved case to a designated workstation; determining the travel duration of an autonomous guided vehicle to transport an empty case from the workstation to its original storage location; determining the travel duration of an autonomous guided vehicle moving to a charging station; and / or determining the travel duration of an autonomous guided vehicle moving from the charging station to its stopping point (e.g., the original case retrieval area). In some variations, travel durations for different types of instructions and travel durations for different types of movements performing the instructions are sampled to determine an average travel duration. In some variations, the execution of the simulation model ends when the average travel durations for different samples converge.

[0174]

[0114] In step 908, the method outputs the results obtained from running the simulation model. The output from the simulation model (e.g., the mean transition duration of the steady state of different processes) is provided as input to the analysis model (e.g., in step 908). In step 912, the method includes generating the analysis model via a controller (e.g., structurally and / or functionally similar to controller 574 in Figure 5). In some variations, the analysis model may be generated via a module such as analysis module 574b described herein.

[0175]

[0115] In some variations, the analytical model can be a Shared Token Multiclass Semi-Open Queuing Network (SOQN). In a Shared Token Multiclass Semi-Open Queuing Network (SOQN), autonomous guided vehicles act as shared tokens that can serve different types of instructions. Instructions can serve if they match with an idle case-handling autonomous guided vehicle. This matching process is considered a synchronous node in the SOQN. Autonomous guided vehicles and instructions may wait for each other. Therefore, there may be two queues at this node: an autonomous guided vehicle queue and an instruction queue.

[0176]

[0116] When an autonomous guided vehicle is assigned to a command type, it moves to retrieve the required case from the shelf. Since the autonomous guided vehicle can move immediately without waiting once it matches the command, this case retrieval process is modeled as an infinite service node (IS). As discussed above, the average travel time is calculated by the simulation model.

[0177]

[0117] The autonomous guided vehicle moves from the last shelf visited in the retrieval process to a designated processing station. This process is modeled using an infinite number of service nodes equal to the number of workstations in the environment (e.g., a warehouse). The probability that the type of instruction selects a workstation is assumed to be input. As discussed above, the average travel time is calculated by the simulation model.

[0178]

[0118] The processes within a workstation are modeled using service nodes (e.g., pickers) with a limited number of servers. The number of service nodes corresponds to the number of workstations, and the number of servers within each node corresponds to the number of pickers within the workstation. The average number of cases carried in a move can be determined. The average time and coefficient of variation required for a picker to pick products from a case can be provided. Alternatively, the time distribution for a picker to retrieve products from a case can be provided.

[0179]

[0119] The case storage process is modeled using an infinite number of service nodes equal to the number of workstations in the warehouse. As discussed above, the average travel time is calculated by the simulation model.

[0180]

[0120] After the case storage process, the autonomous guided vehicle has three possible action branches: continuing to move to the next move starting from the matching process, becoming idle and joining the autonomous guided vehicle queue at the synchronization node, and proceeding to charge. The probability of these branches can be calculated based on factors such as the instruction type, current movement information, battery consumption rate per meter, and battery charge threshold.

[0181]

[0121] When an autonomous guided vehicle is heading to charge, it travels from a station to a charging station. This process is modeled as an infinite service node. As discussed above, the average travel time is calculated by the simulation model.

[0182]

[0122] The service at the charging station is modeled as a service node with a limited number of servers equal to the number of chargers. The average time and coefficient of variation required to charge an autonomous guided vehicle can be input. Alternatively, a charging time distribution can be provided.

[0183]

[0123] After being fully charged, the autonomous guided vehicle returns to the shelf storage area (e.g., the case retrieval area). It then enters an idle state and enters the autonomous guided vehicle queue of the synchronization node. As discussed above, the average travel time is calculated by the simulation model.

[0184]

[0124] In step 914, the method includes estimating the performance of the autonomous robot system based on the execution of the analysis model generated in step 912. In some variations, estimating the performance of the autonomous robot system may include performing an approximate mean analysis methodology on the generated analysis model. For example, the method may include calculating the visit rate of each service node for different types of commands and the various movements required to perform these commands. The method may further include removing the synchronous nodes and constructing a closed queuing network (CON) for the remaining nodes. The method may also include calculating the throughput of this CON by leveraging an approximate mean analysis (AMVA) method. For example, this may include replacing the synchronous nodes with load-dependent service nodes based on the throughput calculated by leveraging the AVVA method on the CON. The service rate of this load-dependent service node depends on the number of autonomous guided vehicles in this node. Next, the method may include constructing a second closed queuing network (CON2) for the load-dependent service nodes and complementary nodes. The method may include solving CON2 using AMVA and calculating the utilization rate of resources (autonomous guided vehicles, processing stations, and charging stations).

[0185]

[0125] In this way, the performance of the autonomous robot system can be analyzed computationally quickly and accurately.

[0186]

[0126] Therefore, as described above, Method 900 employs a simulation-based approach to calculate the steady-state average travel duration of all travel processes involved in the operation of the autonomous robotic system. In Figures 6A and 6B, there is a clock symbol near the travel processes determined using the simulation-based approach. The average value does not change with the allocation of resources (autonomous guided vehicle, picker, charger) in the environment (e.g., warehouse) and the command arrival speed. Instead, the average value depends on several factors, namely (1) the case retrieval policy within a travel, (2) the case allocation policy between travels, (3) the planned route of the autonomous guided vehicle, (4) the characteristics of the autonomous guided vehicle such as travel speed, and (5) the warehouse layout. Therefore, if these factors do not change, it is not necessary to repeatedly calculate the steady-state average travel duration.

[0187]

[0127] In summary, the techniques described herein estimate the performance of an autonomous robotic system by the following:

[0188]

[0128] To generate a simulation model based on the operation process of the autonomous robot system and the warehouse layout. In the simulation, each r∈O and t∈T is shown by the clock symbol in Figures 6A and 6B. r For each of these, the duration of movement for different movement processes is recorded. The simulation ends when the average of the collected samples converges.

[0189]

[0129] Based on the operation of the autonomous robot system, the following elements are taken into consideration: (1) the average travel duration calculated in the previous step, (2) the number of resources (autonomous guided vehicles, pickers, chargers), (3) the average command arrival speed and multi-line command distribution, and (4) the service time distribution at the workstation and charging station.

[0190]

[0130] Using these components, a shared token multiclass service-oriented queuing network (SOQN) is constructed for an autonomous robotic system in the warehouse. An approximate mean analysis (AMVA) methodology is used to solve the SOQN model.

[0191] example

[0131] The accuracy of estimations using the techniques described herein is verified using discrete-event simulations. The proposed SOQN solution is tested in the application scenario shown in Figure 10. In this example, the warehouse has 60 shelves. Three workstations are distributed in the warehouse. The time it takes for a picker to retrieve a product from a case follows a uniform distribution U[20,25] seconds. A charging station is located at the top of the warehouse. The charging time for a single autonomous guided vehicle follows a uniform distribution U[25,30] minutes. The battery charging threshold is th c The probability is set to 20%. The speed of the autonomous guidance vehicle is set to 0.5 m / s. The maximum capacity of the autonomous guidance vehicle is set to 5. The maximum number of instruction lines is set to 6. The probabilities of instruction lines 1 through 6 are set to [0.1, 0.2, 0.2, 0.1, 0.1, 0.3], respectively. It is assumed that the arrival of instructions follows a Poisson distribution.

[0192]

[0132] In this example, it is assumed that the probability of a case being selected by a command is equal for all cases stored in the warehouse. After a command is assigned to the autonomous guided vehicle, a case retrieval command within a movement is randomly generated. In addition, case assignments between different movements are also randomly set. The autonomous guided vehicle moves along a path. The autonomous guided vehicle is described in F. Duchon et al., "Path Planning with Modified a Star Algorithm for a Mobile Robot", Procedia Engineering, Vol. 96, pp. 59-69, January 2014, doi:10.1016 / j.proeng.2014.12.098 *Use a path planning algorithm.

[0193]

[0133] The discrete event system model is constructed using AnyLogic® software. Ten replicas are run with 1000 hours of operation time per replica, resulting in a 95% confidence interval where the full width at half maximum is within 2% of the mean.

[0194]

[0134] The techniques described herein are compared with steady-state performance (instruction throughput time and autonomous guidance vehicle utilization rate) obtained from discrete event simulations with different warehouse resource specifications, including the number of autonomous guidance vehicles, the number of chargers at charging stations, the number of pickers at different workstations, and the average instruction arrival rate per minute. As shown in Figure 11, the accuracy of the techniques described herein exceeds 90% for both instruction throughput time and autonomous guidance vehicle utilization rate.

[0195]

[0135] In addition, obtaining steady-state performance through discrete event simulation using AnyLogic® software takes an average of 300 seconds. The technique described herein requires only 0.1 seconds for the AMVA method to solve the constructed SOQN, and the computation time required to calculate the movement duration is approximately 15 seconds.

[0196]

[0136] In the foregoing description, certain terms have been used for illustrative purposes to provide a complete understanding of the invention. However, it will be apparent to those skilled in the art that certain details are not necessary to carry out the invention. Accordingly, the foregoing description relating to certain embodiments of the invention is presented for illustrative and explanatory purposes. These are not intended to be exhaustive or to limit the invention to the exact form disclosed, and it will be apparent from the above teachings that many modifications and variations are possible. The embodiments have been selected and described to illustrate the principles of the invention and their practical applications, so that those skilled in the art can utilize the invention and its various embodiments with various modifications to suit the specific use envisioned. The following claims and their equivalents are intended to define the scope of the invention.

Claims

1. A computer implementation method for estimating the performance of an autonomous robot system configured to perform a plurality of line commands for transporting one or more cases for each different product, wherein the autonomous robot system comprises a plurality of autonomous guided vehicles configured to transport the one or more cases in an environment and perform the plurality of line commands, and the computer implementation method is The first input data is obtained via a user interface, wherein the first input data includes data associated with the operation of each of the plurality of autonomous guided vehicles and data representing the layout of the environment. A simulation model is generated which is configured to simulate the operation of each of the plurality of autonomous guided vehicles in the environment, based at least in part on the first input data. Based on the execution of the simulation model, the duration of movement of each of the plurality of autonomous guided vehicles from a first position among one or more positions in the environment to a second position among one or more positions in the environment is determined. To generate an analysis model configured to analyze the performance of the autonomous robot system based at least partially on the duration of movement, A computer implementation method comprising estimating the performance of the autonomous robot system based on the execution of the aforementioned analysis model.

2. The computer implementation method according to claim 1, wherein estimating the performance of the autonomous robot system includes calculating the throughput time for executing the plurality of line commands.

3. The computer implementation method according to claim 1, wherein estimating the performance of the autonomous robot system includes calculating the proportion of the use of one or more resources in the environment to perform the plurality of line commands.

4. The data associated with the operation of each of the aforementioned multiple autonomous guided vehicles is: For each autonomous guidance vehicle, The autonomous guidance vehicle retrieves one or more of the cases, and the commands from the command queue, An indication from the command queue in a single move, showing how many of the one or more cases mentioned above will be retrieved, The computer implementation method according to claim 1, comprising at least one of the following: the speed of movement of the autonomous guided vehicle.

5. Determining the duration of the aforementioned movement is, For each of the aforementioned multiple autonomous guidance vehicles, The first travel time from the current position of the autonomous guidance vehicle to the position of one or more cases, A second travel time from the position of one or more cases to the position of the workstation, The third travel time from the workstation's position to the storage position, The computer implementation method according to claim 1, comprising determining at least one of the fourth travel times from the storage position to the charging position.

6. Determining the duration of the aforementioned movement further involves, For the multiple line instructions of different types, the first travel time, the second travel time, the third travel time, and the fourth travel time are sampled. The computer implementation method according to claim 5, further comprising determining the average travel time based on the aforementioned sampling.

7. The computer implementation method according to claim 1, wherein the analysis model is a shared token multiclass semi-open queuing network.

8. The generation of the aforementioned analysis model further involves, The process of matching one of the aforementioned multiple autonomous guidance vehicles to a first set of line commands is represented as a first synchronization node. The process of retrieving one or more cases by the autonomous guidance vehicle is represented as a second infinite service node, The process of moving the one or more extracted cases together with the autonomous guided vehicle to the workstation is represented as a third infinite service node, The process by which a picker retrieves a product from one or more cases in the workstation is represented as a fourth service node, The computer implementation method according to claim 7, further comprising representing the process of moving from the workstation to the storage location by the autonomous guidance vehicle as a fifth infinite service node.

9. The computer implementation method according to claim 7, wherein estimating the performance of the autonomous robot system comprises performing an approximate mean analysis of the shared token multiclass semi-open queuing network.

10. A system for estimating the performance of an autonomous robot system configured to perform multiple line commands for transporting one or more cases for different products, A user interface for acquiring first input data, wherein the first input data includes data associated with the operation of each of a plurality of autonomous guided vehicles and data representing the layout of the environment, and the autonomous robot system comprises a plurality of autonomous guided vehicles configured to transport one or more cases within the environment and to perform the plurality of line commands. At least one controller that is communicatively coupled to the user interface, A simulation model is generated that is configured to simulate the operation of each of the plurality of autonomous guided vehicles in the environment, based at least in part on the first input data. Based on the execution of the simulation model, the duration of movement of each of the plurality of autonomous guided vehicles from a first position among one or more positions in the environment to a second position among one or more positions in the environment is determined. An analysis model is generated that is configured to analyze the performance of the autonomous robot system based at least partially on the duration of movement, A system comprising a controller configured to estimate the performance of the autonomous robot system based on the execution of the aforementioned analysis model.

11. The system according to claim 10, wherein the at least one controller is configured to calculate the throughput time for executing the plurality of line commands and to estimate the performance of the autonomous robot system.

12. The system according to claim 10, wherein the at least one controller is configured to estimate the performance of the autonomous robot system by calculating the proportion of the use of one or more resources in the environment to perform the plurality of line commands.

13. The data associated with the operation of each of the aforementioned multiple autonomous guidance vehicles is, for each autonomous guidance vehicle, The autonomous guidance vehicle retrieves one or more of the cases, and the commands from the command queue, An indication from the command queue in a single move, showing how many of the one or more cases mentioned above will be retrieved, The system according to claim 10, comprising at least one of the following: the speed of movement of the autonomous guided vehicle.

14. The aforementioned at least one controller further, For each of the aforementioned multiple autonomous guidance vehicles, The first travel time from the current position of the autonomous guidance vehicle to the position of one or more cases, A second travel time from the position of one or more cases to the position of the workstation, The third travel time from the workstation's position to the storage position, The system according to claim 10, configured to determine at least one of the fourth travel times from the storage position to the charging position.

15. The aforementioned at least one controller further, For the multiple line instructions of different types, the first travel time, the second travel time, the third travel time, and the fourth travel time are sampled. The system according to claim 14, configured to determine the mean travel time based on the sampling.

16. The system according to claim 10, wherein the analysis model is a shared token multiclass semi-open queuing network.

17. The aforementioned at least one controller is The process of matching one of the aforementioned multiple autonomous guidance vehicles to a first set of line commands is represented as a first synchronization node. The process of retrieving one or more cases by the autonomous guidance vehicle is represented as a second infinite service node. The process of moving the one or more retrieved cases to the workstation by the autonomous guided vehicle is represented as a third infinite service node. In the aforementioned workstation, the process by which a picker retrieves products from one or more cases is represented as a fourth service node. The process of moving the autonomous guided vehicle from the workstation to the storage location is represented as a fifth infinite service node. The system according to claim 16, configured to generate the aforementioned analysis model.

18. The system according to claim 16, wherein the at least one controller is further configured to perform an approximate mean analysis of the shared token multiclass semi-open queuing network to estimate the performance of the autonomous robot system.

19. The computer implementation method according to claim 1, wherein storage and retrieval are performed for each of the multiple line commands.

20. The system according to claim 10, wherein storage and retrieval are performed for each case by the plurality of line commands.

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