LATENCY-BASED ROBOT MAP GENERATION DEVICE AND METHOD

Abstract

A latency-based robot map generation device includes a network map generator configured to divide a space into grids and measure the network latency for each grid to generate a network map with assigned latency levels. The device further includes a success rate calculator configured to calculate success rates for repeated driving operations on paths within grids to which specific latency levels are assigned in the network map. Additionally, a path determiner selects a driving path on the network map based on the assigned latency levels and the calculated success rates.

Description

BACKGROUND OF THE INVENTION(a) Field of the invention

[0001] The present disclosure relates to a latency-based robot ticket generation device and a method, and in particular to a latency-based robot ticket generation device and a method that generate a ticket for a mobile robot by mirroring a network situation. (b) Description of the related situation

[0002] Currently, when creating a map for an autonomous robot, elements that could affect its movement, such as obstacles, glass walls, and areas with many reflective materials, are scanned. Accordingly, if it is determined that stable driving is impossible, a robot driving ban is placed on the map to ensure stability.

[0003] Network-related problems that could affect robot driving and service operations are typically identified during field tests after configuring a service scenario.

[0004] In wireless networks, latency can occur depending on various external factors, such as the number of users connected to the network, the weather, and the modem / router status, and such latency can cause problems with robot driving and service. SUMMARY OF THE INVENTION

[0005] The present disclosure aims to provide a latency-based robot ticket generation device and a latency-based robot ticket generation method that additionally manage network status variables and service success rate indicators for an existing map, so that robot driving and service are not performed in an area where the latency is always high, and path priorities are set based on success rate data in an area where the latency occurs intermittently.

[0006] The present disclosure aims to provide a latency-based robot ticket generation device and a latency-based robot ticket generation method that creates a space in grids, measures a network latency for each grid, generates a network map to which degrees are assigned, calculates a success rate according to statistical numbers by path repetition, updates the network map based on the success rate, and determines a route selection priority according to the latency degree and the success rate.

[0007] According to an exemplary embodiment, a latency-based robot map generation device may include: a network map generator that divides a space into grids and measures a network latency for each grid to generate a network map with assigned latency grades; a success rate calculator that computes success rates by repeatedly driving on a path within a grid to which a specific latency grade is assigned; and a path determiner that selects paths on the network map based on latency grades and success rates.

[0008] The network card generator can determine the latency level as a first level if the network latency is equal to or greater than a first criterion, a second level if the network latency is less than the first criterion and equal to or greater than a second criterion, and a third level if the network latency is less than the second criterion and equal to or greater than a third criterion, and the first to third criteria can vary based on a service operating status of a mobile robot.

[0009] The path determiner can exclude a grid whose latency is the first degree from the path.

[0010] The success rate calculator can accumulate statistical values ​​by repeatedly driving grids assigned a second or third latency level and calculate the success rates for each grid based on these accumulated values. The route determiner can set a route selection priority proportional to the success rates for each grid on the network map and determine the route based on this priority.

[0011] The route determiner can update the network map and the route preset on the network map by integrating the route selection priorities calculated for each grid.

[0012] The route determiner can exclude a specific grid with a success rate of 50% or less from the route.

[0013] If an anomaly is detected in a specific router, the network card generator can reduce the operating speed of the robot that measures the network latency for grids within a certain radius of a location of the specific router.

[0014] The route determiner can reduce the route selection priority for grids within a specified radius of the location of the specific router to a predefined level. If a specific router shuts down, the network map generator can set the latency levels for grids within a specified radius of the shut-down router to the first level.

[0015] According to another exemplary embodiment, a latency-based robot route map generation method may involve creating a space in grids and measuring a network latency for each grid to generate a network map with assigned latency grades, calculating a success rate by repeatedly driving on a path on a grid to which a specific latency grade is assigned in the network map, setting route selection priorities for each grid based on the latency grade and success rate, and determining a route on the network map based on the route selection priority.

[0016] Generating the network map may involve receiving an initial network map for the room by a control center and measuring the network latency for each grid on the initial network map.

[0017] Generating the network map can include determining the latency level as a first level if the network latency is equal to or greater than a first criterion, a second level if the network latency is less than the first criterion and equal to or greater than a second criterion, and a third level if the network latency is less than the second criterion and equal to or greater than a third criterion, and the first to third criteria can vary depending on a service operating status of a mobile robot.

[0018] Calculating the success rate can involve accumulating statistical values ​​by repeatedly driving grids assigned a second or third latency level, and calculating the success rates for each grid based on these accumulated values. Determining the driving path can involve excluding a grid with a first-level latency from the driving path.

[0019] Determining the route can involve updating the network map and the route preset on the network map in real time by reflecting the route selection priorities for each grid proportionally to the success rate.

[0020] Determining the route may also involve excluding a specific grid with a success rate of 50% or less from the route.

[0021] If an anomaly is detected in a specific router, generating the network map may involve reducing the operating speed of the robot that measures network latency for grids within a certain radius of the router's location. Determining the path may involve reducing the path selection priority set for grids within that radius of the router to a certain degree.

[0022] When a specific router shuts down, generating the network map may also involve determining the latency levels for the grids within a certain radius of a location of the shut-down specific router as the first level.

[0023] According to the latency-based robot ticket generation device and the method according to an exemplary embodiment of the present disclosure, it is possible to reduce the frequency of service failure in the network problem situation by automatically updating and managing the latest map that reflects the network status and managing the detailed tasks of the robot in a form that enables unmanned operation.

[0024] According to the latency-based robot ticket generation device and the method according to an exemplary embodiment of the present disclosure, it is possible to drive the robot stably and improve the completeness of robot products and services by managing the network status as the variable in addition to the physical obstacles. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a schematic diagram representing a latency-based robot ticket generation system according to an exemplary embodiment of the present disclosure. Fig. Figure 2 is a block diagram of a latency-based robot ticket generation device according to an exemplary embodiment of the present disclosure. Fig. Figure 3 is a diagram representing a network map according to an exemplary embodiment of the present disclosure. Fig. Figure 4 is a flowchart illustrating a latency-based robot ticket generation method according to an exemplary embodiment of the present disclosure. Fig. Figure 5 is a flowchart illustrating the latency-based robot ticket generation method according to an exemplary embodiment of the present disclosure. Fig. Figure 6 is a diagram describing the latency-based robot ticket generation method according to an exemplary embodiment of the present disclosure. Fig. Figure 7 is a flowchart illustrating the operation of a robot in a network problem situation according to an exemplary embodiment of the present disclosure. Fig. Figure 8 is a diagram describing a latency-based robot ticket generation method according to another exemplary embodiment of the present disclosure. Fig. Figure 9 is a diagram describing the latency-based robot ticket generation method according to another exemplary embodiment of the present disclosure. Fig. Figure 10 is a diagram describing a computing device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EXECUTION FORMS

[0025] The present invention is described in more detail below with reference to the accompanying drawings, which illustrate embodiments of the invention. As a person skilled in the art would recognize, the described embodiments can be modified in various ways without deviating from the basic concept or scope of the present invention. Accordingly, the drawings and the description are to be regarded as illustrative and not as limiting. The same reference numerals denote the same elements throughout the entire description.

[0026] Throughout this description and in the claims, unless expressly stated otherwise, the word "comprise" and variations such as "includes" or "comprehensive" are understood to imply the inclusion of the specified elements but not the exclusion of other elements. Terms containing an ordinal number, such as first, second, etc., may be used to describe different components, but the components are not limited to these terms. The foregoing terms are used solely for the purpose of distinguishing one component from another.

[0027] Terms such as "... unit", "... and / or", and "module", as used in this description, refer to components capable of performing at least one function or operation described herein. These may be implemented as hardware, circuits, software, or a combination of hardware and software. Exemplary embodiments of this disclosure are described below with reference to the drawings.

[0028] Fig. Figure 1 is a schematic diagram representing a latency-based robot ticket generation system according to an exemplary embodiment of the present disclosure.

[0029] A latency-based robot ticket generation system 1000 can be attached to a mobile robot. That is, the latency-based robot ticket generation system 1000 can be implemented as a mobile robot.

[0030] With reference to Fig. 1 The latency-based robot ticket generation system 1000 comprises a latency-based robot ticket generation device 100, a communication module 20, a sensor module 30, a memory 40 and a drive module 50.

[0031] The latency-based robot ticket generation device 100, the communication module 20, the sensor module 30, the memory 40 and the drive module 50 can be connected via a network.

[0032] The latency-based robot ticket generation device 100 can be implemented as a processor. It receives data from the communication module 20 and the sensor module 30 and processes the data to generate a robot ticket. That is, the latency-based robot ticket generation device 100 can receive the strength of a communication signal from the communication module 20 and receive and compare points of interest (POs) from the sensor module 30.

[0033] The communication module 20 transmits and receives data with other modules via a communication network. The communication module 20 can measure the strength of the communication signal and the network latency for each POI during data transmission and reception.

[0034] Sensor module 30 can include a lidar sensor for creating an indoor map. Sensor module 30 can also include various positioning sensors for measuring locations indoors for autonomous driving.

[0035] Memory 40 can contain various types of volatile or non-volatile storage media and store the generated network map, the robot map, and various data. Memory 40 stores data generated by the latency-based robot map generation device 100.

[0036] The drive module 50 propels the mobile robot and can receive driving-related data from the sensor module 30. The latency-based robot ticket generation system 1000 can be connected via a network to a robot task manager 60 and a database (DB).

[0037] The Robot Task Manager 60 can manage unit tasks of robot services, such as a robot's movement, object recognition, and speech. In one exemplary embodiment, the Robot Task Manager 60 can measure the success rate of the unit task. The Robot Task Manager 60 can store the measured success rate in the database (DB).

[0038] The database (DB) can be a server connected to storage, used to periodically store and manage generated map data. Fig. Figure 2 is a block diagram of a latency-based robot ticket generation device according to an exemplary embodiment of the present disclosure.

[0039] With reference to Fig. 2 The latency-based robot ticket generation device 100 can include a network card generator 110, a success rate calculator 120 and a route determiner 130.

[0040] The Network Map Generator 110 can divide a room into grids. That is, the Network Map Generator 110 can divide the entire map of a room to be measured into N × N grids. The room to be measured could be a room in which a robot is moving.

[0041] The Network Card Generator 110 can measure network latency for each grid.

[0042] The Network Card Generator 110 can assign any latency level based on the network latency measured for each grid.

[0043] The Network Map Generator 110 creates a network map with latency levels assigned to each grid. The Network Map Generator 110 can assign a latency level of a first level if the network latency is equal to or greater than a first criterion. For example, the Network Map Generator 110 can assign a high latency level to the corresponding grid if the grid's latency is 1000 m / s or higher.

[0044] The Network Card Generator 110 can determine the latency level as a second grade if the network latency is less than the first criterion and equal to or greater than a second criterion. For example, the Network Card Generator 110 can determine the latency level of the corresponding grid as a medium grade if the latency is less than 1000 m / s and 500 m / s or more.

[0045] The Network Card Generator 110 can determine the latency level as a third grade if the network latency is less than the second criterion and equal to or greater than a third criterion. For example, the Network Card Generator 110 can determine the latency level of the corresponding grid as a low grade if the latency is less than 500 m / s and 100 m / s or more.

[0046] The first three criteria can change dynamically based on the service status of the robot. If the network map generator 110 detects an anomaly in a specific router, it is possible to reduce the operating speed of the robot, which measures the network latency for grids within a certain radius of a specific router.

[0047] This means that the network map generator 110 can refine latency-based mapping by a slowly moving robot.

[0048] When a shutdown of a specific router occurs, the network card generator 110 can determine the latency levels for the grids within a certain radius from a location of the shut-down specific router as the first level or the high level.

[0049] The network map generator 110 can assign the highest latency level to unmetered grids to ensure they are excluded from the driving path. The success rate calculator 120 can calculate a success rate by repeatedly driving a path in a grid to which a specific latency level has been assigned in the network map.

[0050] The Success Rate Calculator 120 can accumulate a statistical value by repeatedly driving for grids assigned the second degree or the third degree and calculate the success rates for each grid based on the accumulated statistical value.

[0051] The route determiner 130 can generate the route on the network map based on the latency level and the success rate.

[0052] The path selector 130 excludes grids with a latency of the first or higher degree from the path. In other words, it excludes grids with a latency of 1000 ms or more. The path selector 130 can set each path selection priority proportionally to the success rates for each grid on the network map.

[0053] The route determiner 130 can determine the route based on the route selection priorities.

[0054] The route determiner 130 can update the network map and the route preset on the network map by reflecting the route selection priorities calculated in real time for each grid.

[0055] The route selector 130 excludes grids with a success rate of 50% or less from the route. The route selector 130 can reduce the route selection priority set for grids within a certain radius of the specific router where the anomaly is detected to a certain degree.

[0056] Fig. Figure 3 is a diagram representing a network map according to an exemplary embodiment of the present disclosure.

[0057] The latency-based robot ticket generation device 100 can generate a network map using information provided by the communication module 20 (see Fig. 1) and the sensor module 30 (see Fig. 1) be measured.

[0058] The latency-based robot map generation device 100 can convert the existing map into grids and measure a latency for each grid to generate the network map to which the latency level is assigned.

[0059] In Fig. Section 3 of the network map contains a variety of grids. These grids can be displayed differently based on latency levels. Each grid distinguishes between cases of normal and abnormal latency. For example, grids with normal latency and grids with abnormal latency can be displayed in different colors.

[0060] Some grids may be displayed as physical obstacles that are detected by sensor module 30.

[0061] Some grids with abnormal latency may exhibit high latency, as measured by Communication Module 20. Other grids with abnormal latency may exhibit medium or low latency. On the network map, some of the high-, medium-, and low-latency grids can be distinguished by different colors. Alternatively, the network level can be displayed on the grids of the network map. The network level can be represented by a number. For example, high latency can be represented by 3, medium latency by 2, and low latency by 1 on the network map.

[0062] Fig. Figure 4 is a flowchart of the latency-based robot ticket generation process according to an exemplary embodiment of the present disclosure. The latency-based robot ticket generation process of Fig. 4 can be achieved using the latency-based robot ticket generation device 100 (see Fig. 2) be carried out.

[0063] In Fig. 4. The latency-based robot map generation device 100 can convert a room into grids and measure a network latency for each grid to create a network map to which each latency level is assigned (step S410).

[0064] The latency-based robot ticket generation device 100 can calculate a success rate by repeatedly driving on a path in a grid to which a specific latency level is assigned in the network map (step S420).

[0065] Here, the success rate can refer to a work success rate or a task success rate. The success rate can also refer to a predefined frequency of robot task success. In other words, the success rate can be a statistic about whether the task or the robot's movement within a grid is ultimately successful with a specific latency level.

[0066] For example, if a target movement from point A to point B on the network map is successful, the success rate can be determined based on the number of successes for multiple movement requests.

[0067] Alternatively, in the case of a goods delivery, if a specific task, such as receiving / loading / placing goods in an elevator in a specific grid, is attempted and is successful according to a predefined operation, the success rate can be determined based on the number of successes compared to the total number of requests.

[0068] In an exemplary embodiment, the latency-based robot ticket generation device 100 measures the success rate directly using the success rate calculator 120 (see Fig. 2) In this case, it receives task execution information for the robot from the robot task manager 60 (see Fig. 1) Alternatively, the latency-based robot ticket generation device 100 can receive the task success rate measured by the robot task manager 60.

[0069] The latency-based robot map generation device 100 can set path selection priorities for each grid based on the latency level and success rate (step S430).

[0070] The latency-based robot ticket generation device 100 determines the route on the network map based on the assigned route selection priorities (step S440). Fig. Figure 5 is a flowchart illustrating the latency-based robot ticket generation method according to an exemplary embodiment of the present disclosure. Fig. 5 is a diagram that specifically illustrates a latency-based robot ticket generation method of Fig. 4 describes.

[0071] In Fig. 5. The latency-based robot ticket generation device 100 can generate a network map (or network latency map) to which a latency grade with respect to a network latency is assigned for each grid (step S510).

[0072] The latency-based robot ticket generation device 100 can determine whether the latency anomaly occurred in a grid on a path while a robot is moving (step S520).

[0073] If there is no grid on the travel path where the latency anomaly occurred, the latency-based robot ticket generation device 100 can perform the travel operation on the travel path (step S521). That is, the latency-based robot ticket generation device 100 can issue a command to the travel module 50 to perform a path operation (see Fig. 1).

[0074] If the latency anomaly exists on the path, the latency-based robot ticket generation device 100 can determine the driving operations for each latency level (step S530).

[0075] The latency-based robot ticket generation device 100 can first determine whether the latency of the grid on the network map path is high (step S540). For example, if the latency is 1000 m / s or more, it can be classified as high. If the latency is less than 1000 m / s and 500 m / s or more, it can be classified as medium. If the latency is less than 1000 m / s and 100 m / s or more, it can be classified as low.

[0076] The criteria for determining latency levels can vary based on the service environment or the robot's operating status. If the latency level is classified as high, the latency-based robot map generation device 100 can be configured to avoid the corresponding grid (step S541). That is, the latency-based robot map generation device 100 can exclude a grid with a high latency level from the travel path.

[0077] If the latency level of the grid on the path is not high, the latency-based robot ticket generation device 100 can set the corresponding grid to a path caution, since it still has the medium or low degree (step S542).

[0078] The latency-based robot ticket generation device 100 can operate a robot along a path with path avoidance settings or path caution settings for some of the anomaly grids (step S550).

[0079] The latency-based robot ticket generation device 100 can check in real time whether there is a change in the latency information of each grid of the network map during the path operation (step S560).

[0080] If there is a change in the latency information, the latency-based robot ticket generation device 100 can re-receive the network card with the changed latency information (step S561).

[0081] The latency-based robot ticket generation device 100 can repeat steps S520 to S550 with the newly received network card.

[0082] If there is no change in the latency information, the latency-based robot map generation device 100 can repeatedly perform the robot operation along the set path and collect the success rate statistics for a specific number of repetitions or more (step S570).

[0083] This means that the latency-based robot ticket generation device 100 can accumulate statistical values ​​by repeatedly driving for grids assigned the second or third degree and calculate the success rates for each grid based on the accumulated statistical values.

[0084] The success rate can be determined by comparing the frequency of successes to the total requirements for predefined robot tasks in each grid.

[0085] The latency-based robot ticket generation device 100 determines whether the success rate is greater than 90% (step S571), and if so, it can set a path with the highest priority for the corresponding grid (step S581). That is, the latency-based robot ticket generation device 100 can determine the path selection priority as a first priority for a grid with a success rate greater than 90%.

[0086] The latency-based robot ticket generation device 100 determines whether the success rate is 80% or higher when the success rate is 90% or lower (step S572), and if so, it can set a track path for the corresponding grid (step S582). That is, the latency-based robot ticket generation device 100 can determine the path selection priority as the second priority for a grid with a success rate of 80% or higher and 90% or lower.

[0087] The latency-based robot ticket generation device 100 determines whether the success rate is between 50% and 80% (step S573). The latency-based robot ticket generation device 100 can set a third-priority path for a grid with a success rate of 50% or more (step S583). That is, the latency-based robot ticket generation device 100 can assign a third-priority path selection priority for a grid with a success rate of 80% or less and 50% or more.

[0088] The latency-based robot ticket generation device 100 can set a path avoidance for a grid with a success rate of 50% or less (step S574). That is, the latency-based robot ticket generation device 100 can exclude a specific grid from the travel path with a success rate of 50% or less.

[0089] In other words, the success rate and the path selection priority are proportional. The latency-based robot map generation device 100 can update the network map and the path preset on the network map in real time by reflecting the path selection priorities for each grid, which is proportional to the success rate (step S590).

[0090] The latency-based robot ticket generation device 100 can repeat steps S520 to S590 with the updated route.

[0091] Fig. Figure 6 is a diagram describing the latency-based robot ticket generation method according to an exemplary embodiment of the present disclosure.

[0092] In Fig. 6 The latency-based robot ticket generation device 100 receives an initial network map for a measurement room from a control center and measures the network latency for each grid on the initial network map (step S610).

[0093] The latency-based robot ticket generation device 100 can determine the latency level based on the measured network latency and operate a robot (BOT) along a path that is set based on the latency level (step S620).

[0094] In particular, the latency-based robot map generation device can determine 100 caution / avoidance for a grid on a path of a network map (MAP) based on the latency and perform the robot's driving or task operation along the determined path.

[0095] The latency-based robot map generation device 100 repeats the robot's path operation a specific number of times, calculates the success rate, and then re-determines the path based on the calculated success rate (step S630). The latency-based robot map generation device 100 can update the network map based on the calculated success rate and update the path on the network map (step S640).

[0096] This means that the latency-based robot ticket generation device 100 can update the route determined on the network map based on the latency and success rate in order to generate the robot ticket.

[0097] The latency-based robot ticket generation device 100 can remeasure the latency through the robot on the network latency map, whose path is updated, and assign the degree (step S640).

[0098] The latency-based robot ticket generation device 100 repeats steps S610 to S640 using the network card with reassigned latency levels. Fig. Figure 7 is a flowchart illustrating the operation of a robot in a network problem situation according to an exemplary embodiment of the present disclosure. Here, the robot can be a robot equipped with a latency-based robot ticket generation system 1000 (see Figure 7). Fig. 1).

[0099] In Fig. 7 The robot moves to location A to perform the assigned task (step S710).

[0100] If a network disconnection occurs during movement after the robot task has been received normally, it is generally handled by the control center. If the robot's operating status information changes, this information is transmitted to the control center, and the robot can also determine the network disconnection if the information is not transmitted.

[0101] The robot can move to a destination it wants to go to when the network is restored and can perform the task.

[0102] The robot arrives at location A and can complete the detection / delivery task (step S720).

[0103] The robot can then check the network status (step S730). If the network disconnection occurs at the time of detection completion after reaching the POI, the robot determines that the network is disconnected if the execution result is not transmitted from the maintenance location.

[0104] The robot moves to the next location if the task is determined to be successful during the network status check (step S741). If the network is determined to be disconnected, the robot can move to a waiting location and continue with the recovery process (step S742). The robot can determine that the network is restored when the result is transmitted normally.

[0105] The robot attempts network recovery three or more times and, if recovery is unsuccessful, transmits a network error notification to the control center (step S751). Afterwards, the robot moves to a charging location and can terminate the service (step S760).

[0106] If network recovery is successful after two attempts, the robot moves back to location A, its previous location (step S752), and performs the task (step S770). Fig. Figure 8 is a diagram to describe a latency-based robot ticket generation method according to another exemplary embodiment of the present disclosure.

[0107] In Fig. 8. The latency-based robot ticket generation device 100 can change the latency measurement method if a specific router RT1 exhibits an anomaly among a plurality of routers (step S810).

[0108] For example, the latency-based robot ticket generation device 100 can retry a measured amount of allocation from another router RT2 if latency increases significantly or packet loss occurs at a specific location due to the anomaly in the specific router RT1.

[0109] The latency-based robot ticket generation device 100 additionally assigns a latency measurement time near the location of another router (RT2) being measured (step S820). For example, the latency-based robot ticket generation device 100 can identify an approximate location of the specific router RT1 where the anomaly occurred using a received signal strength indicator (RSSI).

[0110] If the latency-based robot map generation device 100 detects the anomaly in the specific router RT1, it can reduce the robot's operating speed by measuring the network latency for grids within a certain radius AR1 from the location of the specific router RT1 and can refine the latency mapping.

[0111] The latency-based robot ticket generation device 100 performs the robot operation based on the completed latency map (step S830).

[0112] The latency-based robot map generation device 100 can set the robot path and perform the operation execution through the completed network latency map.

[0113] The latency-based robot map generation device 100 lowers the priority of the corresponding area when mapping robot operations (step S840). For example, the latency-based robot map generation device 100 can determine the path selection priority on the motion path and reduce the grids contained within the specified radius AR1 near the anomaly router RT1 by one step from the preset priority.

[0114] Fig. Figure 9 is a diagram describing the latency-based robot ticket generation method according to another exemplary embodiment of the present disclosure.

[0115] In Fig. 9. The latency-based robot ticket generation device 100 can change the measurement method for the nearby latency when a specific router RT3 shuts down (step S910).

[0116] For example, the latency-based robot ticket generation device 100 can only perform a measurement of another router RT2 if the specific router RT3 shuts down. Whether the specific router RT3 should be shut down can be determined based on information received via the router or multiple communication failures.

[0117] The latency-based robot ticket generation device 100 can assign the latency to high-degree grids within the specified radius AR2 near the location of the shut-down router RT3 (step S920).

[0118] The latency-based robot map generator 100 operates the robot based on the completed network latency map (step S930). The latency-based robot map generator 100 can avoid the downed router area AR2 during mapping by the robot operation. Alternatively, the latency-based robot map generator 100 can allow the robot to operate in an autonomous mode (step S940).

[0119] For example, the latency-based robot map generation device 100 can configure all grids that are distinguished as being of a high degree (e.g., red color) in the network map on the robot's (BOT) operational path to be avoided.

[0120] In other words, the robot (BOT) performs an avoidance maneuver if the area near the target is marked as high grade, or drives to the target in autonomous mode. Fig. Figure 10 is a diagram describing a computing device according to an exemplary embodiment of the present disclosure.

[0121] With reference to Fig. 10. The latency-based robot ticket generation device and the method described in the exemplary embodiments can be implemented using a computing unit 900. The computing unit 900 can include at least one processor 910, a memory 930, a user interface input device 940, a user interface output device 950, and a storage device 960, which communicate via a bus 920. The computing unit 900 can also include a network interface 970, which is electrically connected to a network 90. ​​The network interface 970 can transmit or receive signals to and from other entities via the network 90.

[0122] The Processor 910 can be implemented in various ways, such as a microcontroller unit (MCU), an application processor (AP), a central processing unit (CPU), a graphics processing unit (GPU), a neural processing unit (NPU), and can be any semiconductor device that executes instructions stored in the Memory 930 or Memory Device 960. The Processor 910 can be configured to perform the tasks described above. Fig. 1 to Fig. to implement the 9 described functions and procedures.

[0123] The memory 930 and the memory device 960 can include various types of volatile or non-volatile storage media. For example, the memory can include a read-only memory (ROM) 931 and a random-access memory (RAM) 932. In an exemplary embodiment of the present disclosure, the memory 930 can be positioned inside or outside the processor 910, and the memory 930 can be connected to the processor 910 by various means that are well known.

[0124] In some embodiments, components or functions of the latency-based robot ticket generation device and the latency-based robot ticket generation method can be implemented as software or a program that runs on the computing device 900, and this software or program can be stored on a computer-readable medium. In some exemplary embodiments, at least some components or functions of the latency-based robot ticket generation device and the latency-based robot ticket generation method according to the exemplary embodiments are implemented using hardware or circuitry of the computing device 900 or can be implemented as separate hardware or circuitry that can be electrically connected to the computing device 900.

[0125] While the embodiments of the present disclosure have been described in detail, the scope of the present disclosure is not limited to these descriptions. It includes modifications and alterations made by the person skilled in the art based on the fundamental concepts of the present disclosure as defined in the claims. <bezugszeichenliste> 20 Communication module 30 Sensor module 40 storage 50 Driving module 60 robot task managers 100 Latency-Based Robot Ticket Generation Device 110 Network Card Generator 120 success rate calculators 130 route planners< / bezugszeichenliste>

Claims

[1] Latency-based robot ticket generation device, comprising: a network map generator configured to divide a room into grids and measure network latency for each grid in order to generate a network map with an assigned latency level for each grid, a success rate calculator configured to calculate a success rate by repeatedly driving a path in a grid to which a specific latency level is assigned in the network card, and a route determiner that is configured to determine a route on the network map based on the assigned latency level and the calculated success rate. [2] Latency-based robot ticket generation device according to claim 1, wherein: The network card generator is configured to determine the latency level. as a first degree if the network latency is equal to or greater than a first criterion, a second degree if the network latency is smaller than the first criterion and equal to or greater than a second criterion, and a third degree if the network latency is smaller than the second criterion and equal to or greater than a third criterion, and where the first to third criteria vary depending on the service operational status of a driving robot. [3] Latency-based robot ticket generation device according to claim 2, wherein: the route determiner excludes any grid to which the first latency level is assigned from the route. [4] Latency-based robot ticket generation device according to claim 3, wherein: the success rate calculator a statistical value is accumulated through repeated driving operations for grids assigned the second degree or the third degree, and the success rates for each grid are calculated based on the accumulated statistical value. [5] Latency-based robot ticket generation device according to claim 1, wherein: the route determiner sets a route selection priority proportional to the calculated success rates for each grid on the network map and determines the route based on the route selection priority. [6] Latency-based robot ticket generation device according to claim 5, wherein: the route determiner The network map and a route preset on the network map are updated by reflecting the route selection priorities calculated in real time for each grid. [7] Latency-based robot ticket generation device according to claim 6, wherein: the route determiner excludes any grid with a success rate of 50% or less from the route. [8] Latency-based robot ticket generation device according to claim 5, wherein: when an anomaly is detected in a specific router, the network card generator a robot operating speed that measures network latency for grids within a certain radius of a location of the specific router. [9] Latency-based robot ticket generation device according to claim 8, wherein: the route determiner the route selection priority set for grids within a specific radius of the location of the specific router is reduced to a certain degree. [10] Latency-based robot ticket generation device according to claim 3, wherein: when a specific router shuts down, the network card generator assigns the first latency level to the grids within a certain radius of the location of the shut-down router. [11] Latency-based robot ticket generation method, comprising: Dividing a space into grids and measuring network latency for each grid to generate a network map with assigned latency levels using a network map generator, Calculating a success rate by repeatedly traversing a path within a grid, to which a specific latency level is assigned in the network map, using a success rate calculator, and Setting route selection priorities for each grid based on the latency level and the calculated success rate, and determining a route on the network map based on the route selection priority using a route determiner. [12] Latency-based robot ticket generation method according to claim 11, wherein: Creating the network card involves the following: Receiving an initial network map for the room by a control center and measuring the network latency for each grid on the initial network map using the network map generator. [13] Latency-based robot ticket generation method according to claim 11, wherein: Creating the network card involves using the network card generator to: Determining the latency level as a first degree if the network latency is equal to or greater than a first criterion, a second degree if the network latency is smaller than the first criterion and equal to or greater than a second criterion, and a third degree if the network latency is smaller than the second criterion and equal to or greater than a third criterion, and where the first to third criteria vary depending on the service operational status of a driving robot. [14] Latency-based robot ticket generation method according to claim 13, wherein: Calculating the success rate using the success rate calculator includes: Accumulating a statistical value through repeated driving operations for grids assigned the second or third latency grade, and calculating the success rates for each grid based on the accumulated statistical value. [15] Latency-based robot ticket generation method according to claim 14, wherein: Determining the route involves using the route determiner to: Exclude any grid assigned the first latency level from the route. [16] Latency-based robot ticket generation method according to claim 11, wherein: Determining the route involves using the route determiner to: Updating the network map and a preset route on the network map in real time by reflecting the route selection priorities for each grid proportionally to the success rate. [17] Latency-based robot ticket generation method according to claim 16, wherein: Determining the route also includes using the route determiner: Exclude any specific grids with a success rate of 50% or less from the route. [18] Latency-based robot ticket generation method according to claim 11, wherein: If an anomaly is detected in a specific router, generating the network map involves using the path determiner to: Reducing the operating speed of the robot that measures network latency for grids within a certain radius of a location of the specific router. [19] Latency-based robot ticket generation method according to claim 18, wherein: Determining the route and using the route determiner includes: Reducing the path selection priority set for grids within a specified radius from the location of a specific router to a certain degree. [20] Latency-based robot ticket generation method according to claim 15, wherein: When a specific router shuts down, creating the network card and using the network card generator also involves: Determining the first latency level for the grids within a specific radius of the location of the shut-down router.

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