Method and apparatus for dynamic task allocation for automated guided vehicles

Abstract

A method for dynamically assigning tasks to an unmanned transport robot, performed by a computing device, is disclosed. The method may include: a step of searching for a first movement path by setting the task completion position of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and the task start position of the first task as a destination point in response to a task request for a first task; a step of searching for a second movement path by setting the waiting position of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and the task start position of the first task as a destination point in response to a task request for the first task; a step of determining an optimal movement path among the first movement path and the second movement path; and a step of assigning the first task to the unmanned transport robot having the optimal movement path.

Description

Technology Field

[0001] The present invention relates to an automated guided vehicle, and more specifically, to a method and apparatus for assigning tasks to an automated guided vehicle. Background Technology

[0002] Automated Guided Vehicles (AGVs) are widely used in logistics and manufacturing sites to transport various materials and products. AGVs move along designated paths according to work requests and perform tasks such as receiving, shipping, and returning.

[0003] Existing automated guided vehicle (AGV) management systems generally utilize a method where AGVs process tasks in the order in which work requests are received. However, this sequential processing method is highly likely to result in inefficient movement paths for the AGVs. For example, if an AGV performs receiving tasks continuously, there is a possibility that the waiting time for the AGV will increase or that outbound operations will be delayed. Additionally, if the AGV moves in the order of work requests, unnecessary back-and-forth movement within the logistics warehouse may increase, potentially extending the overall transport time.

[0004] In addition, if an automated guided vehicle (AGV) repeatedly visits the same area according to the order of work requests, there is an increase in unnecessary path movement, which can lead to problems such as increased battery consumption, reduced transport efficiency, and delays in work processing. Furthermore, in environments where multiple AGVs operate simultaneously, this simple sequential work assignment method causes traffic congestion, and there is a risk that the productivity of the entire system will decrease due to the overloading of a specific AGV.

[0005] Therefore, there is a demand for technology that dynamically assigns tasks to unmanned transport robots by considering factors such as movement paths. Prior art literature

[0006] Republic of Korea Published Patent 10-2024-0122284 The problem to be solved

[0007] The present disclosure is conceived in response to the aforementioned background technology and aims to provide a method and apparatus for dynamic task assignment for an unmanned transport robot. means of solving the problem

[0008] A method for dynamically assigning tasks to an unmanned transport robot, performed by a computing device for realizing the aforementioned tasks, is disclosed. The method may include: a step of searching for a first movement path by setting the work completion position of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and the work start position of the first task as a destination point in response to a work request for a first task; a step of searching for a second movement path by setting the waiting position of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and the work start position of the first task as a destination point in response to a work request for the first task; a step of determining an optimal movement path among the first movement path and the second movement path; and a step of assigning the first task to the unmanned transport robot having the optimal movement path.

[0009] Alternatively, the step of determining the optimal movement path among the first movement path and the second movement path may include: determining the movement path having the shortest expected path distance among the first movement path and the second movement path as the optimal movement path.

[0010] Alternatively, the step of determining the optimal path among the first path and the second path may include: determining the estimated time of arrival at the work location of the first path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated travel time of the first path; determining the estimated time of arrival at the work location of the second path using the shortest estimated travel time of the second path; and determining the path of the unmanned transport robot having the fastest estimated time of arrival at the work location as the optimal path.

[0011] Alternatively, the step of determining the estimated time of arrival at the work location of the first movement path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated time of travel of the first movement path may include: the step of determining the estimated time of completion of the work using the travel time from the current work location to the work completion location, the work preparation time, and the work execution time of at least one unmanned transport robot performing the work; the step of determining the shortest estimated time of travel using the first movement path; and the step of determining the estimated time of arrival at the work location of the first movement path by adding the shortest estimated time of travel of the first movement path to the estimated time of completion of the work.

[0012] Alternatively, the method may further include: a step of checking whether there is a second task among previously assigned tasks to an unmanned transport robot assigned a first task that has motion connectivity to the first task; and a step of adjusting the task order of the first task so that the first task and the second task are performed sequentially in response to the confirmation that the second task exists.

[0013] Alternatively, the first operation and the second operation may have an inbound and outbound operation relationship through the operation connectivity.

[0014] Alternatively, the method may further include the step of determining whether to reassign the first task using the result of comparing the remaining battery level of the unmanned transport robot assigned the first task with a predetermined task limit battery level.

[0015] Alternatively, the method may further include: a step of determining whether to reassign the first task using the result of comparing the remaining battery level of the unmanned transport robot assigned the first task with the expected battery consumption due to the first task.

[0016] Alternatively, the method may further include: a step of reassigning the first task when the amount of work performed by the unmanned transport robot assigned the first task exceeds a predetermined task assignment limit.

[0017] Alternatively, the method may further include: a step of determining whether to reassign the first task using the result of comparing the optimal movement path with a predetermined movement limit criterion when the amount of work performed by the unmanned transport robot assigned the first task exceeds a predetermined movement limit criterion.

[0018] A computer program stored on a computer-readable storage medium for realizing the aforementioned task is disclosed. The computer program includes instructions for one or more processors to perform a dynamic task assignment method for an unmanned transport robot, and the method may include: a step of searching for a first movement path by setting the task completion position of at least one unmanned transport robot performing the task among a plurality of unmanned transport robots as a starting point and the task start position of the first task as a destination point in response to a task request for a first task; a step of searching for a second movement path by setting the waiting position of at least one unmanned transport robot waiting for the task among the plurality of unmanned transport robots as a starting point and the task start position of the first task as a destination point in response to a task request for the first task; a step of determining an optimal movement path among the first movement path and the second movement path; and a step of assigning the first task to an unmanned transport robot having the optimal movement path.

[0019] A computing device is disclosed for performing a dynamic task assignment method for an unmanned transport robot to realize the aforementioned task. The computing device comprises: a processor; wherein the processor searches for a first movement path by setting the task completion position of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and the task start position of the first task as an arrival point in response to a task request for a first task, searches for a second movement path by setting the waiting position of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and the task start position of the first task as an arrival point, determines an optimal movement path among the first movement path and the second movement path, and assigns the first task to the unmanned transport robot having the optimal movement path. Effects of the invention

[0020] The present disclosure may provide a method and apparatus for dynamic task assignment for an unmanned transport robot. Brief explanation of the drawing

[0021] FIG. 1 is a block diagram illustrating a computing device that performs dynamic task assignment for an unmanned transport robot according to some embodiments of the present disclosure. FIG. 2 is a block diagram illustrating an exemplary unmanned transport robot task assignment system according to some embodiments of the present disclosure. FIG. 3 is a drawing for illustrating an embodiment of performing task assignment based on operational connectivity between tasks according to some embodiments of the present disclosure. FIG. 4 is another drawing for illustrating an embodiment of performing task assignment based on operational connectivity between tasks according to some embodiments of the present disclosure. FIG. 5 is a flowchart of a dynamic task assignment method for an unmanned transport robot according to some embodiments of the present disclosure. FIG. 6 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented. Specific details for implementing the invention

[0022] Various embodiments and / or aspects are now disclosed with reference to the drawings. For illustrative purposes, numerous specific details are disclosed in the following description to aid in a general understanding of one or more aspects. However, it will be apparent to those skilled in the art that these aspects may be practiced without such specific details. The following description and the accompanying drawings describe specific exemplary aspects of one or more aspects in detail. However, these aspects are exemplary, and some of the various methods in the principles of the various aspects may be used, and the descriptions are intended to include all such aspects and their equivalents. Specifically, terms such as “exemplary,” “example,” “aspect,” and “example” as used herein may not be interpreted as implying that any described aspect or design is superior or advantageous over other aspects or designs.

[0023] Hereinafter, identical or similar components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. Furthermore, in describing the embodiments disclosed in this specification, detailed descriptions of related prior art are omitted if it is determined that such detailed descriptions may obscure the essence of the embodiments disclosed in this specification. Additionally, the attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification, and the technical concept disclosed in this specification is not limited by the attached drawings.

[0024] The terms used herein are for describing the embodiments and are not intended to limit the disclosure. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, "comprises" and / or "comprising" do not exclude the presence or addition of one or more other components in addition to the components mentioned.

[0025] Although terms such as "first," "second," etc. are used to describe various elements or components, it goes without saying that these elements or components are not limited by these terms. These terms are used merely to distinguish one element or component from another. Therefore, it goes without saying that the first element or component mentioned below may be the second element or component within the technical scope of this disclosure.

[0026] Unless otherwise defined, all terms used in this specification (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0027] Furthermore, the term "or" is intended to mean an implicit "or" rather than an exclusive "or." That is, unless otherwise specified or evident from the context, "X uses A or B" is intended to mean one of the natural implicit substitutions. In other words, if X uses A; if X uses B; or if X uses both A and B, "X uses A or B" may apply to any of these cases. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the enumerated related items.

[0028] And, the term “at least one of A or B” should be interpreted to mean “a case including only A,” “a case including only B,” or “a combination of A and B.”

[0029] Those skilled in the art should recognize that the various exemplary logical blocks, configurations, modules, circuits, means, logics, and algorithmic steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly exemplify the interchangeability of hardware and software, various exemplary components, blocks, configurations, means, logics, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented in hardware or software depends on the specific application and design constraints imposed on the overall system. Skilled technicians may implement the described functionality in various ways for each specific application. However, such decisions regarding implementation should not be construed as going beyond the scope of this disclosure.

[0030] The description of the presented embodiments is provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

[0031] And, terms such as “~ etc.” as in “A, B, etc.” should be interpreted to mean “cases containing only A,” “cases containing only B,” or “cases composed of A and B.”

[0032] When it is stated that one component is “connected” or “connected” to another component, it should be understood that it may be directly connected or connected to that other component, or that there may be other components in between. On the other hand, when it is stated that one component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

[0033] The suffixes “module” and “part” for components used in the following description are assigned or used interchangeably solely for the ease of drafting the specification and do not inherently possess distinct meanings or roles.

[0034] The purpose and effects of the present disclosure, and the technical configurations for achieving them, will become clear by referring to the embodiments described in detail below in conjunction with the accompanying drawings. In describing the present disclosure, if it is determined that a detailed description of known functions or configurations might unnecessarily obscure the essence of the present disclosure, such detailed description will be omitted. Furthermore, the terms described below are defined considering their functions in the present disclosure, and these may vary depending on the intentions or practices of the user or operator.

[0035] However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Therefore, such definitions should be based on the content throughout this specification.

[0036] FIG. 1 is a block diagram illustrating a computing device that performs dynamic task assignment for an unmanned transport robot according to some embodiments of the present disclosure.

[0037] As illustrated in FIG. 1, the computing device (100) may include a processor (110), memory (130), and a network unit (150). The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In some embodiments of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0038] The processor (110) may be composed of one or more cores and may include processors for data analysis and processing, deep learning, such as a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU). The processor (110) may read a computer program stored in memory (130) and perform data conversion, computation, generation, etc., to perform a dynamic task assignment method for an unmanned transport robot according to some embodiments of the present disclosure. The processor (110) may implement an unmanned transport robot task assignment system (1000) for performing a dynamic task assignment method for an unmanned transport robot. The processor (110) may perform steps for performing a dynamic task assignment method for an unmanned transport robot described below by the unmanned transport robot task assignment system (1000). At least one of the CPU, GPGPU, and TPU of the processor (110) can process operations for performing a dynamic task assignment method for an unmanned transport robot. For example, the CPU and GPGPU can together process operations for performing a dynamic task assignment method for an unmanned transport robot. In addition, in some embodiments of the present disclosure, processors of a plurality of computing devices can be used together to process data conversion, computation, generation, learning of network functions, and data classification using network functions for performing a dynamic task assignment method for an unmanned transport robot. In addition, a computer program executed in a computing device according to some embodiments of the present disclosure may be a CPU, GPGPU, or TPU executable program. In some examples, the computing device (100) or the processor (110) may include a System on a Chip (SoC).In some examples, the computing device (100) or processor (110) may operate based on embedded memory. In some examples, the processor (110) may include memory (130).

[0039] According to some embodiments of the present disclosure, the memory (130) may store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150). For example, the memory (130) may store data generated during the process of performing a dynamic task assignment method for an unmanned transport robot by the processor (110). Additionally, the memory (130) may store data received from the outside during the process of performing a dynamic task assignment method for an unmanned transport robot by the processor (110). However, not limited thereto, the memory (130) may store various information for performing a dynamic task assignment method for an unmanned transport robot according to some embodiments of the present disclosure.

[0040] According to some embodiments of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may operate in conjunction with web storage that performs the storage function of the memory (130) over the internet. In some examples, the memory (130) may be included in the processor (110). The description of the memory described above is merely illustrative and the present disclosure is not limited thereto.

[0041] A network unit (150) according to some embodiments of the present disclosure may use any known wired or wireless communication system.

[0042] The network unit (150) can transmit and receive information, user interfaces, etc., processed by the processor (110) through communication with other terminals. For example, the network unit (150) can provide a user interface generated by the processor (110) to a client (e.g., user terminal). In addition, the network unit (150) can communicate with an unmanned transport robot to control the unmanned transport robot. In addition, the network unit (150) can receive external input from a user authorized by the client and transmit it to the processor (110). At this time, the processor (110) can process operations such as outputting, modifying, changing, or adding information provided through the user interface based on the external input from the user received from the network unit (150).

[0043] Specifically, for example, the network unit (150) can transmit and receive various information and signals to perform a dynamic task assignment method for an unmanned transport robot according to some embodiments of the present disclosure. For example, the network unit (150) can transmit and receive information and signals that generate a task for the unmanned transport robot. The network unit (150) can transmit and receive information and signals that assign the generated task to the unmanned transport robot. In addition, the network unit (150) can transmit some data generated during the process of performing the dynamic task assignment method for the unmanned transport robot described below to the outside for storage in a database.

[0044] Meanwhile, a computing device (100) according to some embodiments of the present disclosure may include a server as a computing system that transmits and receives information through communication with a client. In this case, the client may be any type of terminal capable of accessing the server. For example, the computing device (100) which is the server may receive a query from a user terminal and generate a single information processing result corresponding to the query. In this case, the computing device (100) which is the server may provide a user interface including the processing result to the user terminal. In this case, the user terminal may output the user interface received from the computing device (100) which is the server and receive or process information through interaction with the user.

[0045] In an additional embodiment, the computing device (100) may include any type of terminal that receives data resources generated from any server and performs additional information processing.

[0046] In an additional embodiment, the computing device (100) may include a central vehicle controller of an unmanned transport robot.

[0047] FIG. 2 is a block diagram illustrating an exemplary unmanned transport robot task assignment system according to some embodiments of the present disclosure. FIG. 3 is a diagram illustrating an embodiment for performing task assignment based on operational connectivity between tasks according to some embodiments of the present disclosure. FIG. 4 is another diagram illustrating an embodiment for performing task assignment based on operational connectivity between tasks according to some embodiments of the present disclosure.

[0048] In some examples, the unmanned transport robot task assignment system (1000) may be implemented by a server that controls multiple unmanned transport robots. In another example, the unmanned transport robot task assignment system (1000) may be implemented by a user terminal that receives data resources generated by any server and performs additional information processing. In yet another example, the unmanned transport robot task assignment system (1000) may be implemented by a central vehicle controller of the unmanned transport robot. However, it is not limited thereto, and the unmanned transport robot task assignment system (1000) may be implemented in various ways.

[0049] In some examples, an Automated Guided Vehicle (AGV) may be any type of robot that moves without a driver and performs logistics tasks. In this specification, an AGV may be used interchangeably with an Autonomous Logistics Robot, an Automated Guided Forklift (AGF), an Autonomous Mobile Robot (AMR), etc. An AGV may be controlled by an AGV task assignment system (1000) to move and / or perform pre-assigned tasks.

[0050] Hereinafter, with reference to FIGS. 2 to 4, an exemplary embodiment is described in which an unmanned transport robot task assignment system (1000) and its components perform dynamic task assignment for an unmanned transport robot.

[0051] As illustrated in FIG. 2, the unmanned transport robot task assignment system (1000) may include a movement path-based task assignment unit (200), a motion connectivity-based task assignment unit (300), a battery status-based task assignment unit (400), and a task performance-based task assignment unit (500). The configuration of the unmanned transport robot task assignment system (1000) illustrated in FIG. 2 is merely a simplified example. In some embodiments of the present disclosure, the unmanned transport robot task assignment system (1000) may include other configurations for performing a computing environment of a computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0052] According to some embodiments of the present disclosure, the movement path-based work assignment unit (200) can search for a first movement path by responding to a work request for a first work, setting the work completion location of at least one unmanned transport robot performing a work among a plurality of unmanned transport robots as the starting point, and setting the work start location of the first work as the destination point. Additionally, the movement path-based work assignment unit (200) can search for a second movement path by responding to a work request for a first work, setting the waiting location of at least one unmanned transport robot waiting for a work among a plurality of unmanned transport robots as the starting point, and setting the work start location of the first work as the destination point.

[0053] Specifically, the movement path-based task assignment unit (200) may assign tasks by considering the movement paths of the unmanned transport robots, instead of prioritizing the assignment of a requested task to an unmanned transport robot that is waiting among a plurality of unmanned transport robots. For example, when a task request is received requesting a logistics task to be performed by an unmanned transport robot, the movement path-based task assignment unit (200) may search for the respective movement paths of the plurality of unmanned transport robots. The movement path-based task assignment unit (200) may search for a movement path in which the unmanned transport robots in operation move to perform a newly assigned task after completing a previously assigned task. For example, when a task request for a first task is received, the movement path-based task assignment unit (200) may search for a first movement path by responding to the task request for the first task, setting the task completion location of the unmanned transport robot as the starting point and the task start location of the first task as the arrival point. Here, the first movement path may refer to a path along which unmanned transport robots are moving to perform a newly requested task. Additionally, the movement path-based task assignment unit (200) may search for a movement path to perform a newly assigned task from the location where the waiting unmanned transport robots are waiting. For example, the movement path-based task assignment unit (200) may search for a second movement path by responding to a task request for the first task, setting the waiting location of at least one unmanned transport robot waiting for the task among a plurality of unmanned transport robots as the starting point, and setting the task start location of the first task as the destination point. Here, the second movement path may refer to a movement path along which the waiting unmanned transport robots are moving to perform a newly requested task.

[0054] According to some embodiments of the present disclosure, the movement path-based task assignment unit (200) may determine an optimal movement path among a first movement path and a second movement path. Additionally, the movement path-based task assignment unit (200) may assign a first task to an unmanned transport robot having an optimal movement path.

[0055] Specifically, the movement path-based work assignment unit (200) can determine an optimal movement path among the movement paths of multiple unmanned transport robots in order to efficiently assign work. In some examples, the optimal movement path may be determined based on distance or travel time. For example, the movement path-based work assignment unit (200) can determine the movement path having the shortest expected path distance among the first movement path and the second movement path as the optimal movement path. Specifically, for example, the movement path-based work assignment unit (200) can determine the first movement path having the shortest expected path distance as the optimal movement path when one of the first movement paths of the unmanned transport robots in operation has the shortest expected path distance. As another example, the movement path-based work assignment unit (200) can determine the second movement path having the shortest expected path distance as the optimal movement path when one of the second movement paths of the unmanned transport robots in standby has the shortest expected path distance.

[0056] In some examples, the movement path-based work assignment unit (200) can determine the movement path having the fastest estimated time of arrival at the work location among the first movement path and the second movement path as the optimal movement path. Specifically, the movement path-based work assignment unit (200) can determine the unmanned transport robot to which the work is to be assigned by additionally considering the estimated time of work completion of at least one unmanned transport robot performing the work. The movement path-based work assignment unit (200) can determine the estimated time of work completion of at least one unmanned transport robot performing the work among a plurality of unmanned transport robots. For example, the movement path-based work assignment unit (200) can determine the estimated time of work completion by using the travel time from the current work location to the work completion location of the unmanned transport robots performing the work, the work preparation time (e.g., additional x / y / z position and angle alignment), and the work execution time (e.g., loading / unloading time). In addition, the movement path-based work assignment unit (200) can determine the shortest estimated travel time using the first movement path. The movement path-based work assignment unit (200) can determine the estimated time of arrival at the work location of the first movement path by using the estimated time of work completion and the shortest estimated time of travel of the first movement path. For example, the movement path-based work assignment unit (200) can determine the estimated time of arrival at the work location by adding the shortest estimated time of travel of the first movement path to the estimated time of work completion. However, it is not limited thereto, and the movement path-based work assignment unit (200) can determine the estimated time of arrival at the work location in various ways.

[0057] In the case of the second movement path, since there is no need to consider the estimated time of work completion, the movement path-based work assignment unit (200) can determine the estimated time of arrival at the work location of the second movement path using the shortest estimated travel time of the second movement path. When the estimated times of arrival at the work location of the first movement path and the second movement path are determined, the movement path-based work assignment unit (200) can determine the movement path of the unmanned transport robot having the earliest estimated time of arrival at the work location as the optimal movement path. For example, the movement path-based work assignment unit (200) can determine the movement path of the unmanned transport robot performing the work as the optimal movement path when the estimated time of arrival at the work location of the first work is the earliest after the unmanned transport robot performing the work completes the work. As another example, the movement path-based work assignment unit (200) can determine the movement path of the unmanned transport robot waiting for work as the optimal movement path when the estimated time of arrival at the work location of the first work is the earliest. However, it is not limited to this, and the movement path-based work assignment unit (200) can determine the optimal movement path in various ways.

[0058] When an optimal movement path is determined, the movement path-based task assignment unit (200) can assign a newly requested first task to an unmanned transport robot having the optimal movement path. Since the optimal movement path is determined among the first movement path and the second movement path, the unmanned transport robot assigned the first task may be an unmanned transport robot that is currently working or an unmanned transport robot that has the optimal movement path among unmanned transport robots that are waiting. In other words, the movement path-based task assignment unit (200) can assign a newly requested task to an unmanned transport robot having an efficient movement path, regardless of whether the unmanned transport robot is waiting or working.

[0059] According to some embodiments of the present disclosure, the movement path-based work assignment unit (200) may use either an estimated path distance or an estimated travel time as a criterion for determining the optimal movement path based on the work environment of the unmanned transport robot.

[0060] Specifically, while the estimated path distance of an unmanned transport robot can be predicted with a relatively small amount of computation, the estimated travel time of an unmanned transport robot requires a large amount of computation for accurate prediction due to various variables such as real-time changes in obstacles and traffic conditions, work environment variables like spatial structure or floor conditions, dynamic performance differences between unmanned transport robots, and driving algorithms. The estimated path distance can predict the work environment relatively accurately when the work environment of the unmanned transport robot is simple. However, the estimated path distance may be difficult to use as a factor for predicting work time as the work environment of the unmanned transport robot becomes more complex. Since the estimated travel time can be used directly to predict work time, the estimated travel time may be more useful than the estimated path distance in efficiently allocating work to the unmanned transport robot. In some embodiments of the present disclosure, the movement path-based work allocation unit (200) can efficiently perform dynamic work allocation to the unmanned transport robot by dynamically changing the criteria for determining the optimal movement path according to the work environment of the unmanned transport robot to the estimated path distance or the estimated travel time.

[0061] In some examples, the working environment of an unmanned transport robot may include the number of unmanned transport robots operating in the workspace, the size of the workspace, the ratio of the number of unmanned transport robots to the size of the workspace, the number of types of unmanned transport robots, the dynamic performance difference among the types of unmanned transport robots, and the ratio of the remaining workload to the remaining work period. However, the working environment of an unmanned transport robot may include various elements, not limited thereto.

[0062] In some examples, the movement path-based task assignment unit (200) can determine the optimal movement path based on the estimated path distance when the work environment of the unmanned transport robot is not complex. Additionally, the movement path-based task assignment unit (200) can determine the optimal movement path based on the estimated travel time when the work environment of the unmanned transport robot is complex. For example, the movement path-based task assignment unit (200) can use the estimated path distance as a criterion for determining the optimal movement path when the number of unmanned transport robots operating in the workspace is less than the predetermined appropriate number of operations. As another example, the movement path-based task assignment unit (200) can use the estimated travel time as a criterion for determining the optimal movement path when the number of unmanned transport robots operating in the workspace is more than the predetermined appropriate number of operations.

[0063] Additionally, the movement path-based work assignment unit (200) may use the estimated path distance as a criterion for determining the optimal movement path when the workspace size is smaller than the predetermined appropriate workspace size. As another example, the movement path-based work assignment unit (200) may use the estimated travel time as a criterion for determining the optimal movement path when the workspace size is larger than the predetermined appropriate workspace size.

[0064] Additionally, the movement path-based work assignment unit (200) may use the estimated path distance as a criterion for determining the optimal movement path when the ratio of the number of unmanned transport robots to the size of the workspace is smaller than the predetermined appropriate robot placement density. As another example, the movement path-based work assignment unit (200) may use the estimated travel time as a criterion for determining the optimal movement path when the ratio of the number of unmanned transport robots to the size of the workspace is larger than the predetermined appropriate robot placement density.

[0065] Additionally, the movement path-based task assignment unit (200) may use the estimated path distance as a criterion for determining the optimal movement path when the number of unmanned transport robot types is less than the predetermined appropriate number of robot types. As another example, the movement path-based task assignment unit (200) may use the estimated travel time as a criterion for determining the optimal movement path when the number of unmanned transport robot types is more than the predetermined appropriate number of robot types.

[0066] Additionally, the movement path-based task assignment unit (200) may use the estimated path distance as a criterion for determining the optimal movement path when the dynamic performance difference between types of unmanned transport robots is smaller than the predetermined appropriate performance difference criterion. As another example, the movement path-based task assignment unit (200) may use the estimated travel time as a criterion for determining the optimal movement path when the dynamic performance difference between types of unmanned transport robots is larger than the predetermined appropriate performance difference criterion.

[0067] Additionally, the movement path-based work assignment unit (200) may use the estimated route distance as a criterion for determining the optimal movement path when the ratio of the remaining workload to the remaining work period is smaller than the predetermined work urgency rate. As another example, the movement path-based work assignment unit (200) may use the estimated travel time as a criterion for determining the optimal movement path when the ratio of the remaining workload to the remaining work period is larger than the work urgency rate. However, it is not limited to this, and the movement path-based work assignment unit (200) may dynamically change the criterion for determining the optimal movement path to the estimated route distance or the estimated travel time in various ways.

[0068] According to some embodiments of the present disclosure, the motion connectivity-based task assignment unit (300) may determine whether there is a second task among the previously assigned tasks to an unmanned transport robot assigned a first task that has motion connectivity to the first task. In response to the determination that there is a second task, the motion connectivity-based task assignment unit (300) may adjust the task order of the first task so that the first task and the second task are performed consecutively.

[0069] Specifically, to process tasks more efficiently than the method in which unmanned transport robots process tasks in the order they are requested, the motion connectivity-based task assignment unit (300) can prioritize processing tasks that have motion connectivity between tasks. Referring to FIG. 3, an exemplary task request list of an unmanned transport robot assigned a first task is shown. In the task request list, the newly assigned task may have the lowest task rank. As shown in FIG. 3, the newly requested first task 'D' may have a lower fourth task rank compared to the previously assigned tasks 'A', 'B', and 'C'. The motion connectivity-based task assignment unit (300) can check whether there is a second task among the previously assigned tasks that has motion connectivity with the newly requested first task.

[0070] Motion connectivity may refer to cases where the unmanned transport robot performs the first task immediately following the second task, instead of performing the first task and the second task separately, which is efficient in terms of travel distance, work and travel time, and workload. In some examples, the first task and the second task may have a motion connectivity relationship with receiving and receiving tasks. When the unmanned transport robot performs receiving and receiving tasks alternately and continuously, work efficiency may increase. Therefore, the motion connectivity-based task assignment unit (300) can adjust the task priority of the first task so that receiving and receiving tasks can be performed continuously. For example, when performing tasks according to the task request list shown in FIG. 3, the unmanned transport robot performs the newly assigned receiving task after performing receiving tasks three times, resulting in inefficient work. In particular, when the unmanned transport robot is additionally assigned other receiving tasks, the inefficiency increases further. To prevent this, the operation connectivity-based work assignment unit (300) can check whether there is a second work among the previously assigned tasks that has operation connectivity with the newly requested first work. For example, referring to FIG. 3, the operation connectivity-based work assignment unit (300) can check whether there is a second work among the previously assigned tasks that has a work type corresponding to 'receiving work' which has operation connectivity with the newly requested first work 'D'. The operation connectivity-based work assignment unit (300) can check whether there are tasks 'A', 'B', and 'C' among the previously assigned tasks that have a work type corresponding to 'receiving work'. In this case, the operation connectivity-based work assignment unit (300) can adjust the work order of the first work so that the first work and the second work are performed consecutively.As illustrated in FIG. 3, when it is confirmed that there are multiple second tasks, the motion connectivity-based task assignment unit (300) can adjust the task ranking of the first task so that the first task is performed continuously for the second task with the highest task ranking. For example, as illustrated in FIG. 4, the motion connectivity-based task assignment unit (300) can adjust the task ranking of the first task 'D' from '4' to '2' so that the first task 'D' and the second task 'A' are performed continuously. In this case, the unmanned transport robot can prioritize processing tasks with task connectivity by performing the first task 'D' continuously after performing the second task 'A'. However, it is not limited thereto, and the motion connectivity-based task assignment unit (300) can adjust the task ranking of the first task in various ways to prioritize processing tasks with motion connectivity between tasks.

[0071] According to some embodiments of the present disclosure, the battery status-based work assignment unit (400) may determine whether to reassign the first work by using the result of comparing the remaining battery level of the unmanned transport robot assigned the first work with a predetermined work limit battery level.

[0072] Specifically, the battery status-based task assignment unit (400) can reassign a newly requested task by considering the battery status of the unmanned transport robot. In order to prevent situations where the task is delayed or interrupted more than expected due to battery charging or discharging, the battery status-based task assignment unit (400) can compare the remaining battery level of the unmanned transport robot assigned the first task with a predetermined remaining battery level. For example, if the remaining battery level of the unmanned transport robot assigned the first task is 0.8% and the remaining battery level for the task limit is 10%, the battery status-based task assignment unit (400) can decide to reassign the first task to another unmanned transport robot. As another example, if the remaining battery level of the unmanned transport robot assigned the first task is 12% and the remaining battery level for the task limit is 10%, the battery status-based task assignment unit (400) can decide not to reassign the first task.

[0073] According to some embodiments of the present disclosure, the battery status-based work assignment unit (400) may determine whether to reassign the first work by using the result of comparing the remaining battery level of the unmanned transport robot assigned the first work with a predetermined work limit battery level.

[0074] Specifically, the battery status-based task assignment unit (400) can determine individually for each task whether the battery status of the unmanned transport robot can stably perform the newly assigned task. For example, the battery status-based task assignment unit (400) can predict the expected battery consumption due to the newly assigned first task. In some examples, the battery status-based task assignment unit (400) can predict the expected battery consumption due to the first task by utilizing a power consumption measurement-based prediction technique using a power consumption pattern, a software usage pattern analysis technique, a battery discharge curve model utilization technique, or a prediction using machine learning. The battery status-based task assignment unit (400) can compare the remaining battery capacity of the unmanned transport robot assigned the first task with the expected battery consumption due to the first task. For example, if the remaining battery of the unmanned transport robot assigned the first task is 12% and the estimated battery consumption due to the first task is 10%, the battery status-based task assignment unit (400) may decide to reassign the first task to another unmanned transport robot. As another example, if the remaining battery of the unmanned transport robot assigned the first task is 12% and the estimated battery consumption due to the first task is 10%, the battery status-based task assignment unit (400) may decide not to reassign the first task. However, it is not limited thereto, and the battery status-based task assignment unit (400) may assign the requested task according to the battery status of the unmanned transport robot in various ways.

[0075] According to some embodiments of the present disclosure, the work performance-based work assignment unit (500) may reassign the first work when the work performance of the unmanned transport robot assigned the first work exceeds a predetermined work assignment limit.

[0076] Specifically, to promote operational efficiency by maintaining the durability of the unmanned transport robot, the work performance-based work assignment unit (500) may reassign a newly requested task by considering the work performance of the unmanned transport robot. For example, the work performance-based work assignment unit (500) may compare the work performance of the unmanned transport robot assigned the first task with a predetermined work assignment limit. The work assignment limit may refer to the maximum amount of work that the unmanned transport robot can perform during a specific work period (e.g., daily, weekly, monthly, etc.). For example, if the work performance of the unmanned transport robot assigned the first task is smaller than the work assignment limit, the work performance-based work assignment unit (500) may decide not to reassign the first task to another unmanned transport robot. As another example, if the amount of work performed by the unmanned transport robot assigned the first task reaches the task assignment limit, the battery state-based task assignment unit (400) may decide not to reassign the first task.

[0077] According to some embodiments of the present disclosure, the work performance-based work assignment unit (500) may determine whether to reassign the first work by using the result of comparing the optimal movement path with the predetermined movement path limit criteria when the work performance of the unmanned transport robot assigned the first work exceeds the predetermined movement path limit.

[0078] Specifically, the work performance-based work assignment unit (500) can individually determine whether to reassign tasks for each task so that the unmanned transport robot can perform as many tasks as possible within the limits of maintaining durability. For example, the work performance-based work assignment unit (500) can compare the work performance of the unmanned transport robot assigned the first task with a predetermined movement path limit. In some examples, the movement path limit may have a smaller value than the work assignment limit. For example, if the work assignment limit is 100 tasks, the movement path limit may be 90 tasks. If the work performance exceeds the movement path limit, the work performance-based work assignment unit (500) can control the unmanned transport robot to perform only tasks with a restricted movement path. For example, if the work performance exceeds the movement path limit, the work performance-based work assignment unit (500) can compare the optimal movement path with a predetermined movement path limit. The movement path restriction criteria may have a value that limits the distance or travel time of the optimal movement path. For example, if the estimated path distance of the optimal movement path is shorter than the restricted path distance included in the movement path restriction criteria, the work performance-based work assignment unit (500) may decide not to reassign the first task to another unmanned transport robot. As another example, if the estimated path distance of the optimal movement path is longer than the restricted path distance included in the movement path restriction criteria, the work performance-based work assignment unit (500) may decide to reassign the first task to another unmanned transport robot. As yet another example, if the estimated travel time of the optimal movement path is shorter than the restricted travel time included in the movement path restriction criteria, the work performance-based work assignment unit (500) may decide not to reassign the first task to another unmanned transport robot.As another example, if the estimated travel time of the optimal travel path is longer than the limited travel time included in the movement path limit criteria, the work performance-based work assignment unit (500) may decide to reassign the first task to another unmanned transport robot. However, it is not limited thereto, and the work performance-based work assignment unit (500) may assign the requested task according to the work performance amount in various ways.

[0079] FIG. 5 is a flowchart of a dynamic task assignment method for an unmanned transport robot according to some embodiments of the present disclosure.

[0080] According to some embodiments of the present disclosure, a dynamic task assignment method for the unmanned transport robot may include the step (S100) of searching for a first movement path by setting the task completion position of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and setting the task start position of the first task as an arrival point in response to a task request for a first task.

[0081] According to some embodiments of the present disclosure, a dynamic task assignment method for the unmanned transport robot may include the step (S200) of searching for a second movement path by setting the waiting position of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and setting the task start position of the first task as a destination point in response to a task request for the first task.

[0082] According to some embodiments of the present disclosure, a dynamic task assignment method for the unmanned transport robot may include the step (S300) of determining an optimal movement path among the first movement path and the second movement path.

[0083] According to some embodiments of the present disclosure, a dynamic task assignment method for the unmanned transport robot may include the step (S400) of assigning the first task to the unmanned transport robot having the optimal movement path.

[0084] Alternatively, the step (S300) of determining the optimal movement path among the first movement path and the second movement path may include determining the movement path having the shortest expected path distance among the first movement path and the second movement path as the optimal movement path.

[0085] Alternatively, the step (S300) of determining the optimal movement path among the first movement path and the second movement path may include: determining the estimated time of arrival at the work location of the first movement path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated travel time of the first movement path; determining the estimated time of arrival at the work location of the second movement path using the shortest estimated travel time of the second movement path; and determining the movement path of the unmanned transport robot having the fastest estimated time of arrival at the work location as the optimal movement path.

[0086] Alternatively, the step of determining the estimated time of arrival at the work location of the first movement path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated time of travel of the first movement path may include: determining the estimated time of completion of the work using the travel time from the current work location to the work completion location, the work preparation time, and the work execution time of the at least one unmanned transport robot performing the work; determining the shortest estimated time of travel using the first movement path; and determining the estimated time of arrival at the work location of the first movement path by adding the shortest estimated time of travel of the first movement path to the estimated time of completion of the work.

[0087] Alternatively, the dynamic task assignment method for the unmanned transport robot may further include the step of checking whether there is a second task among previously assigned tasks to the unmanned transport robot assigned the first task that has motion connectivity with the first task, and the step of adjusting the task order of the first task so that the first task and the second task are performed sequentially in response to the confirmation that the second task exists.

[0088] Alternatively, the first operation and the second operation may have an inbound and outbound operation relationship through the operation connectivity.

[0089] Alternatively, the dynamic task assignment method for the unmanned transport robot may further include the step of determining whether to reassign the first task using the result of comparing the remaining battery level of the unmanned transport robot assigned the first task with a predetermined task limit battery level.

[0090] Alternatively, the dynamic task assignment method for the unmanned transport robot may further include a step of determining whether to reassign the first task using the result of comparing the remaining battery level of the unmanned transport robot assigned the first task with the estimated battery consumption due to the first task.

[0091] Alternatively, the dynamic task assignment method for the unmanned transport robot may further include the step of reassigning the first task when the amount of work performed by the unmanned transport robot assigned the first task exceeds a predetermined task assignment limit.

[0092] Alternatively, the dynamic task assignment method for the unmanned transport robot may further include a step of determining whether to reassign the first task using the result of comparing the optimal movement path with the predetermined movement limit criteria when the amount of work performed by the unmanned transport robot assigned the first task exceeds the predetermined movement limit.

[0093] The steps of the dynamic task assignment method for the aforementioned unmanned transport robot are presented merely for illustrative purposes, and some steps may be omitted or additional steps may be added. Additionally, the aforementioned steps may be performed in any order.

[0094] FIG. 6 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0095] Although the present disclosure has been described as generally being implementable by a computing device, a person skilled in the art will be well aware that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers and / or as a combination of hardware and software.

[0096] Generally, a program module includes routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, a person skilled in the art will be well aware that the method of the present disclosure can be implemented in other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which may be connected to and operated with one or more associated devices).

[0097] The embodiments described in this disclosure may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0098] Computers typically include various computer-readable media. Any medium accessible by a computer may be a computer-readable medium, and such computer-readable media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media. By example, but not limiting, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be accessed by a computer and used to store desired information.

[0099] Computer-readable transmission media typically include all information transmission media that implement computer-readable instructions, data structures, program modules, or other data, etc., on a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal in which one or more of the characteristics of the signal are set or modified to encode information within the signal. By example, not limiting, computer-readable transmission media include wired media, such as wired networks or direct-wired connections, and wireless media, such as acoustic, RF, infrared, and other wireless media. Any combination of the media described above is also considered to be within the scope of computer-readable transmission media.

[0100] An exemplary environment for implementing various aspects of the present disclosure, including a computer (1102), is shown, wherein the computer (1102) includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including system memory (1106) (but not limited thereto), to the processing unit (1104). The processing unit (1104) may be any processor among various commercial processors. Dual processors and other multiprocessor architectures may also be used as the processing unit (1104).

[0101] The system bus (1108) may be any of several types of bus structures that can be additionally interconnected to a local bus using any of the memory bus, peripheral bus, and various commercial bus architectures. System memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). The basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, EEPROM, etc., and this BIOS includes basic routines that help transfer information between components within the computer (1102) at times such as during startup. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0102] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA)—this internal hard disk drive (1114) may also be configured for external use within a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from a CD-ROM disk (1122) or reading from or writing to other high-capacity optical media such as a DVD). The computer (1102) may also include an external hard disk drive (HDD) (1114) connected to the computer (1102) via ports such as USB, Thunderbolt, and eSATA. A hard disk drive (1114), a magnetic disk drive (1116), and an optical disk drive (1120) can each be connected to a system bus (1108) via a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128). An interface (1124) for implementing an external drive includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

[0103] These drives and associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of a computer (1102), the drives and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, a person skilled in the art will know that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., may also be used in exemplary operating environments and that any of these media may contain computer-executable instructions for performing the methods of the present disclosure.

[0104] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or part of the operating system, application, module and / or data may also be cached in RAM (1112). It will be well known that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0105] The user can input commands and information into the computer (1102) through one or more wired / wireless input devices, such as a pointing device like a keyboard (1138) and a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, etc. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) connected to the system bus (1108), but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

[0106] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface such as a video adapter (1146). In addition to the monitor (1144), the computer generally includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0107] The computer (1102) may operate in a networked environment using a logical connection to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communication. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), but for brevity, only the memory storage device (1150) is illustrated. The illustrated logical connection includes a wired / wireless connection to a local area network (LAN) (1152) and / or a larger network, e.g., a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global computer network, e.g., the Internet.

[0108] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communication to the LAN (1152), and the LAN (1152) may also include a wireless access point installed therein to communicate with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communication computing device on the WAN (1154), or have other means to establish communication through the WAN (1154), such as through the Internet. The modem (1158), which may be an internal or external and a wired or wireless device, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, the program modules described for the computer (1102) or parts thereof may be stored in a remote memory / storage device (1150). It will be well known that the illustrated network connection is exemplary and that other means of establishing a communication link between computers may be used.

[0109] The computer (1102) operates to communicate with any wireless device or object that is deployed and operated via wireless communication, for example, a printer, scanner, desktop and / or portable computer, PDA (portable data assistant), communication satellite, any equipment or place associated with a wireless detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or simply ad hoc communication between at least two devices.

[0110] Wi-Fi (Wireless Fidelity) enables connectivity to the Internet and other sources without wires. Wi-Fi is a wireless technology, similar to a cell phone, that allows devices, such as computers, to transmit and receive data indoors and outdoors—that is, anywhere within the coverage area of ​​a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band).

[0111] Those skilled in the art of the present disclosure will understand that information and signals may be represented using any various different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0112] Those skilled in the art will understand that the various exemplary logic blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented by electronic hardware, various forms of programs or design code (referred to herein as software for convenience), or a combination of all such. To clearly illustrate this interoperability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in relation to their functions. Whether such functions are implemented as hardware or software depends on the design constraints imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementation decisions should not be interpreted as being outside the scope of this disclosure.

[0113] The various embodiments presented herein may be implemented as methods, devices, or articles manufactured using standard programming and / or engineering techniques. The term "article manufactured" includes a computer program, a carrier, or a medium accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical discs (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, the various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0114] It should be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but do not imply being limited to the specific order or hierarchy presented.

[0115] Description of the presented embodiments is provided so that a person skilled in the art may use or practice the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments presented herein, but should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

Claims

Claim 1 A method for dynamic task assignment for an unmanned transport robot performed by a computing device, comprising: a step of searching for a first movement path by responding to a task request for a first task, setting the scheduled completion position of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and setting the task start position of the first task as an arrival point; a step of searching for a second movement path by responding to a task request for the first task, setting the waiting position of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and setting the task start position of the first task as an arrival point; and a step of determining an optimal movement path among the first movement path and the second movement path. The method comprises the step of assigning the first task to an unmanned transport robot having the optimal movement path; and the step of determining the optimal movement path among the first movement path and the second movement path comprises: the step of determining the estimated time of arrival at the work position of the first movement path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated travel time of the first movement path; the step of determining the estimated time of arrival at the work position of the second movement path using the shortest estimated travel time of the second movement path; and the step of determining the movement path of the unmanned transport robot having the fastest estimated time of arrival at the work position as the optimal movement path; and the step of determining one of the estimated path distance or the estimated travel time as a criterion for determining the optimal movement path based on the work environment of the unmanned transport robot.The method further comprises the step of determining one of an estimated path distance or an estimated travel time as a criterion for determining an optimal travel path based on the working environment of the unmanned transport robot, wherein the estimated path distance is used as a criterion for determining the optimal travel path when the number of unmanned transport robots operating in the workspace is less than a predetermined optimal number of operations, and the estimated travel time is used as a criterion for determining the optimal travel path when the number of unmanned transport robots operating in the workspace is greater than a predetermined optimal number of operations; and the estimated path distance is used as a criterion for determining the optimal travel path when the size of the workspace is smaller than a predetermined optimal workspace size, and the estimated travel time is used as a criterion for determining the optimal travel path when the size of the workspace is larger than a predetermined optimal workspace size. A step of using the estimated path distance as a criterion for determining the optimal movement path when the ratio of the number of unmanned transport robots to the workspace size is smaller than the predetermined optimal robot placement density, and using the estimated travel time as a criterion for determining the optimal movement path when the ratio of the number of unmanned transport robots to the workspace size is larger than the predetermined optimal robot placement density; a step of using the estimated path distance as a criterion for determining the optimal movement path when the number of unmanned transport robot types is less than the predetermined optimal number of robot types, and using the estimated travel time as a criterion for determining the optimal movement path when the number of unmanned transport robot types is larger than the predetermined optimal number of robot types.A method comprising: a step of using the estimated path distance as a criterion for determining the optimal movement path when the dynamic performance difference between types of unmanned transport robots is smaller than a predetermined appropriate performance difference criterion, and using the estimated travel time as a criterion for determining the optimal movement path when the dynamic performance difference between types of unmanned transport robots is larger than a predetermined appropriate performance difference criterion; and a step of using the estimated path distance as a criterion for determining the optimal movement path when the ratio of the remaining workload to the remaining work period is smaller than a predetermined work urgency rate, and using the estimated travel time as a criterion for determining the optimal movement path when the ratio of the remaining workload to the remaining work period is larger than the work urgency rate; Claim 2 delete Claim 3 delete Claim 4 In claim 1, the step of determining the estimated time of arrival at the work position of the first movement path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated travel time of the first movement path comprises: the step of determining the estimated time of completion of the work using the travel time from the current work position to the estimated time of completion of the work, the work preparation time, and the work execution time of the at least one unmanned transport robot performing the work; the step of determining the shortest estimated travel time using the first movement path; and the step of determining the estimated time of arrival at the work position of the first movement path by adding the shortest estimated travel time of the first movement path to the estimated time of completion of the work. Claim 5 A method according to claim 1, further comprising: a step of checking whether there is a second task among previously assigned tasks to an unmanned transport robot assigned to a first task that has operational connectivity with said first task; and a step of adjusting the task order of said first task so that said first task and said second task are performed sequentially in response to the confirmation that said second task exists. Claim 6 In paragraph 5, the above first operation and the above second operation have an receiving and shipping operation relationship as the above operation connectivity. Claim 7 A method according to claim 1, further comprising the step of determining whether to reassign the first task using the result of comparing the remaining battery of the unmanned transport robot assigned the first task with a predetermined task limit battery remaining. Claim 8 A method comprising, in addition to claim 1, a step of determining whether to reassign the first task using the result of comparing the remaining battery of the unmanned transport robot assigned the first task with the estimated battery consumption due to the first task. Claim 9 A method according to claim 1, further comprising the step of reassigning the first task when the amount of work performed by the unmanned transport robot assigned the first task exceeds a predetermined task assignment limit. Claim 10 A method according to claim 1, further comprising the step of determining whether to reassign the first task by using the result of comparing the optimal movement path with the predetermined movement path limit criteria when the amount of work performed by the unmanned transport robot assigned the first task exceeds the predetermined movement path limit. Claim 11 A computer program stored on a computer-readable storage medium, wherein the computer program includes instructions for one or more processors to perform a dynamic task assignment method for an unmanned transport robot, the method comprising: a step of searching for a first movement path by responding to a task request for a first task, setting the scheduled completion position of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and setting the task start position of the first task as an arrival point; a step of searching for a second movement path by responding to a task request for the first task, setting the waiting position of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and setting the task start position of the first task as an arrival point; and a step of determining an optimal movement path among the first movement path and the second movement path. The method comprises the step of assigning the first task to an unmanned transport robot having the optimal movement path; and the step of determining the optimal movement path among the first movement path and the second movement path comprises: the step of determining the estimated time of arrival at the work position of the first movement path using the estimated time of completion of the work of at least one unmanned transport robot performing the work among the plurality of unmanned transport robots and the shortest estimated travel time of the first movement path; the step of determining the estimated time of arrival at the work position of the second movement path using the shortest estimated travel time of the second movement path; and the step of determining the movement path of the unmanned transport robot having the fastest estimated time of arrival at the work position as the optimal movement path; and the step of determining one of the estimated path distance or the estimated travel time as a criterion for determining the optimal movement path based on the work environment of the unmanned transport robot.The method further comprises the step of determining one of an estimated path distance or an estimated travel time as a criterion for determining an optimal travel path based on the working environment of the unmanned transport robot, wherein the estimated path distance is used as a criterion for determining the optimal travel path when the number of unmanned transport robots operating in the workspace is less than a predetermined optimal number of operations, and the estimated travel time is used as a criterion for determining the optimal travel path when the number of unmanned transport robots operating in the workspace is greater than a predetermined optimal number of operations; and the estimated path distance is used as a criterion for determining the optimal travel path when the size of the workspace is smaller than a predetermined optimal workspace size, and the estimated travel time is used as a criterion for determining the optimal travel path when the size of the workspace is larger than a predetermined optimal workspace size. A step of using the estimated path distance as a criterion for determining the optimal movement path when the ratio of the number of unmanned transport robots to the workspace size is smaller than the predetermined optimal robot placement density, and using the estimated travel time as a criterion for determining the optimal movement path when the ratio of the number of unmanned transport robots to the workspace size is larger than the predetermined optimal robot placement density; a step of using the estimated path distance as a criterion for determining the optimal movement path when the number of unmanned transport robot types is less than the predetermined optimal number of robot types, and using the estimated travel time as a criterion for determining the optimal movement path when the number of unmanned transport robot types is larger than the predetermined optimal number of robot types.A computer program stored on a computer-readable storage medium, comprising: a step of using the estimated path distance as a criterion for determining the optimal movement path when the dynamic performance difference between types of unmanned transport robots is smaller than a predetermined appropriate performance difference criterion, and using the estimated travel time as a criterion for determining the optimal movement path when the dynamic performance difference between types of unmanned transport robots is larger than a predetermined appropriate performance difference criterion; and a step of using the estimated path distance as a criterion for determining the optimal movement path when the ratio of the remaining workload to the remaining work period is smaller than a predetermined work urgency rate, and using the estimated travel time as a criterion for determining the optimal movement path when the ratio of the remaining workload to the remaining work period is larger than the work urgency rate. Claim 12 A computing device for performing a dynamic task assignment method for an unmanned transport robot, wherein the computing device comprises: a processor; wherein the processor searches for a first movement path by responding to a task request for a first task, setting the task completion location of at least one unmanned transport robot performing a task among a plurality of unmanned transport robots as a starting point and the task start location of the first task as a destination point; wherein the processor searches for a second movement path by responding to a task request for the first task, setting the waiting location of at least one unmanned transport robot waiting for a task among the plurality of unmanned transport robots as a starting point and the task start location of the first task as a destination point; determines an optimal movement path among the first movement path and the second movement path; and assigns the first task to an unmanned transport robot having the optimal movement path; wherein, when determining the optimal movement path among the first movement path and the second movement path, the processor uses the task completion time of at least one unmanned transport robot performing a task among the plurality of unmanned transport robots and the shortest expected travel time of the first movement path to Determining the estimated time of arrival at the work location of the first movement path, determining the estimated time of arrival at the work location of the second movement path using the shortest estimated travel time of the second movement path, and determining the movement path of the unmanned transport robot having the fastest estimated time of arrival at the work location as the optimal movement path, and the processor performs an operation of determining one of the estimated path distance or the estimated travel time as a criterion for determining the optimal movement path based on the work environment of the unmanned transport robot, and the operation of determining one of the estimated path distance or the estimated travel time as a criterion for determining the optimal movement path based on the work environment of the unmanned transport robot is: when the number of unmanned transport robots operating in the work space is less than the predetermined optimal number of operations,An operation in which the above-mentioned estimated path distance is used as a criterion for determining the optimal movement path, and the above-mentioned estimated travel time is used as a criterion for determining the optimal movement path when the number of unmanned transport robots operating in the above-mentioned workspace is greater than the predetermined appropriate number of operations; an operation in which the above-mentioned estimated path distance is used as a criterion for determining the optimal movement path when the size of the workspace is smaller than the predetermined appropriate workspace size, and the above-mentioned estimated travel time is used as a criterion for determining the optimal movement path when the size of the workspace is larger than the predetermined appropriate workspace size; An operation in which, when the ratio of the number of unmanned transport robots to the workspace size is smaller than the predetermined optimal robot placement density, the estimated path distance is used as a criterion for determining the optimal movement path, and when the ratio of the number of unmanned transport robots to the workspace size is larger than the predetermined optimal robot placement density, the estimated travel time is used as a criterion for determining the optimal movement path; an operation in which, when the number of unmanned transport robot types is less than the predetermined optimal number of robot types, the estimated path distance is used as a criterion for determining the optimal movement path, and when the number of unmanned transport robot types is larger than the predetermined optimal number of robot types, the estimated travel time is used as a criterion for determining the optimal movement path; an operation in which, when the dynamic performance difference between types of unmanned transport robots is smaller than the predetermined optimal performance difference criterion, the estimated path distance is used as a criterion for determining the optimal movement path, and when the dynamic performance difference between types of unmanned transport robots is larger than the predetermined optimal performance difference criterion, the estimated travel time is used as a criterion for determining the optimal movement path. and when the ratio of the remaining workload and the remaining work period is smaller than the predetermined work urgency rate, the above-mentioned estimated path distance is used as a criterion for determining the above-mentioned optimal travel path, andA computing device that performs the operation of using the estimated travel time as a criterion for determining the optimal travel path when the ratio of the remaining workload to the remaining work period is greater than the work urgency rate.

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