System and method for controlling logistics robot

Abstract

Disclosed are a system and method for controlling a logistics robot, the system including: a route management device for predicting expected travel routes of a plurality of workers or a plurality of nearby logistics robots located within an operation boundary and, when a target logistics robot located within the operation boundary is assigned a new task, determining a travelable route, along which the target logistics robot can travel at the operation boundary, on the basis of the predicted expected travel routes; and a control device for controlling the target logistics robot to travel along the travelable route determined by the route management device.

Description

System and method for controlling a logistics robot

[0001] The present invention relates to a system and method for controlling a logistics robot for efficiently operating the logistics robot in a factory using the logistics robot.

[0002] Recently, logistics robots are being introduced not only in general logistics warehouses and factories, but also in smart factories that manufacture products with different specifications using various parts, for the flexible and efficient supply and transport of parts.

[0003] Logistics robots are a general term for autonomous mobile robots (AMRs) and automated guided vehicles (AGVs). These logistics robots can move and perform tasks under the control of a control system.

[0004] However, in factories, workers are present to handle processes that logistics robots cannot perform. Consequently, workers and logistics robots coexist within the factory. While logistics robots can be controlled through the control system, as described above, workers cannot. Therefore, unexpected situations can arise within the factory due to workers.

[0005] Logistics robots lack the ability to predict, detect, and respond to these unexpected situations. Therefore, their speed is limited to prevent accidents when they occur. However, limiting their speed can lead to decreased factory productivity. This, in turn, can lead to increased costs and congestion within the factory, as more logistics robots are deployed to prevent this decline.

[0006] This increased congestion could lead to unexpected situations not only between workers but also between logistics robots, and measures need to be put in place to prevent such situations.

[0007]

[0008] The matters described as background technology above are only intended to enhance understanding of the background of the present invention, and should not be taken as an admission that they correspond to prior art already known to those skilled in the art.

[0009]

[0010] The present invention has been proposed to solve such problems, and aims to provide a system and method for controlling a logistics robot capable of predicting the movement of a worker or other logistics robot in a factory using logistics robots and determining the movement of the logistics robot to be controlled by taking this into consideration.

[0011]

[0012] The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0013]

[0014] In order to achieve the above object, a system for controlling a logistics robot according to the present invention may include a path management device that predicts expected movement paths of a plurality of workers or a plurality of surrounding logistics robots located within an operation boundary, and determines a movable path along which the target logistics robot can move within the operation boundary based on the predicted expected movement path when assigning a new task to a target logistics robot located within the operation boundary; and a control device that controls the target logistics robot to move along the movable path determined by the path management device.

[0015]

[0016] In addition, a method for controlling a logistics robot according to the present invention for achieving the above object may include: a step of predicting an expected movement path of a plurality of workers or a plurality of surrounding logistics robots located within an operation boundary; a step of determining a possible movement path along which the target logistics robot can move within the operation boundary based on the predicted possible movement path when assigning a new task to a target logistics robot located within the operation boundary; and a step of controlling the target logistics robot to move along the determined possible movement path.

[0017]

[0018] According to the above, the system and method for controlling the logistics robot of the present invention can prevent accidents between workers and logistics robots or between logistics robots due to unexpected situations by predicting the movement path of a worker or another logistics robot and determining the movement path of the logistics robot to be controlled by considering the predicted movement path.

[0019] In addition, by determining the movement speed of the logistics robot to be controlled when determining the movement path of the logistics robot, the movement speed of the logistics robot can be freely controlled, thereby improving the productivity of the factory even with a small number of logistics robots.

[0020]

[0021] The effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present invention belongs from the description below.

[0022]

[0023] FIG. 1 is a block diagram showing an example of an operational boundary configuration that can be applied to embodiments of the present invention.

[0024] FIG. 2 is a block diagram showing an example of the configuration of a control device that can be applied to embodiments of the present invention.

[0025] FIG. 3 is a block diagram showing an example of the configuration of a logistics robot that can be applied to embodiments of the present invention.

[0026] FIG. 4 is a perspective view showing an example of the appearance of a logistics robot that can be applied to embodiments of the present invention.

[0027] Figure 5 is a flowchart showing an example of a driving process of a logistics robot that can be applied to embodiments of the present invention.

[0028] Figure 6 is a block diagram showing the configuration of a system for controlling a logistics robot according to one embodiment of the present invention.

[0029] FIG. 7 is a drawing for explaining a process for determining a movable path of a target logistics robot according to one embodiment of the present invention.

[0030] FIG. 8 is a drawing for explaining a method for controlling a logistics robot according to one embodiment of the present invention.

[0031]

[0032] It is possible to communicate with internal components of the operational boundary (100), such as a logistics robot (110), a production device (120), and a monitoring device (130), as well as with external entities, such as a firmware update server.

[0033] The vehicle monitoring unit (147) can monitor the location, route, battery status, communication status, power train status, etc. of individual logistics robots (110). Here, the route is a concept that includes a waypoint-based global route and a real-time local route. In addition, the battery status may include voltage, current, temperature, peak voltage and current, state of charge (SOC: State of Charge), state of health (SOH: State of Health), etc. The communication status may include information on the currently activated communication protocol (such as Wi-Fi), connected AP, distance to the AP, channel in use, etc. In addition, the power train status may include the load, temperature, RPM, etc. of the drivetrain.

[0034] In addition, the vehicle monitoring unit (147) can also check the mission, operation mode, firmware version, etc. currently assigned to each logistics robot (110).

[0035]

[0036] *The map management unit (148) may obtain map data in the form of a grid map obtained when an AMR among logistics robots (110) drives within the operation boundary (100), and may provide a tool that allows a factory manager to edit the obtained map data. By editing the map data, a zone, a virtual lane, an intersection, a no-entry zone, etc., in which one or more preset actions are performed when the logistics robot (110) enters, may be set, but this is merely an example and is not necessarily limited thereto. In addition, the map management unit (148) may distribute the corresponding map to the remaining logistics robots (110) other than the logistics robot (110) that obtained the initial grid map through actual driving, through the communication unit (146).

[0037] Next, the logistics robot (110) will be described with reference to FIGS. 3 and 4.

[0038] FIG. 3 is a block diagram showing an example of a logistics robot configuration that can be applied to embodiments of the present invention.

[0039] Referring to FIG. 3, the logistics robot (110) may include a driving unit (111), a sensing unit (112), a loading unit (113), a communication unit (114), and a control unit (115). Each component is described below.

[0040] The driving unit (111) may include a driving source, wheels, suspension, etc. involved in the movement, steering, and stopping of the logistics robot (110). The driving source may be an electric motor supplied with power from a built-in battery (not shown). The wheels may include one or more driving wheels that receive driving force from the driving source, and non-driving wheels that rotate by the movement of the vehicle body without receiving driving force. Depending on the implementation, when multiple driving wheels are provided, the driving source may be matched to each driving wheel so that the rotation of each driving wheel can be independently controlled. In this case, by making the rotation directions of different driving wheels different, the vehicle body can be rotated and steering can be performed without a separate steering means. At least some of the non-driving wheels may be configured as caster-type wheels, but this is exemplary and is not necessarily limited thereto.

[0041] The sensing unit (112) is for detecting the surrounding environment or its own operating status of the logistics robot (110), and may include at least one of a 2D laser scanner (e.g., LiDAR), a 3D vision (stereo) camera, a multi-axis gyro sensor, an acceleration sensor, a wheel encoder, and a proximity sensor.

[0042] An encoder can output information that can determine how much the wheel has rotated by using light emitted from a light-emitting element (e.g., a photodiode). For example, the encoder can count the number of slits arranged along the circumference of the wheel or a disk rotating with the wheel per unit time. The control unit (115) can perform odometry, which estimates displacement by analyzing the amount of position change over time using data acquired through the encoder and gyro sensor. However, the displacement estimated based on the encoder data may have an error from the actual displacement due to wheel slip or wear (change in diameter along with the wheel). Therefore, when performing odometry, the control unit (115) can perform noise and error correction on the information collected from the wheel and gyro sensor using a predetermined algorithm (e.g., EKF: Extended Kalman Filter) to output a result that tends to be close to the actual value. This odometry can be particularly useful when localization using a 2D laser scanner, as described later, is not possible.

[0043] 2D laser scanners scan their surroundings by projecting laser light onto a rotating reflector and detecting the reflected signal. By analyzing the intensity of the reflected signal and the time difference between the projection and reception, they can output detection results in the form of a point cloud.

[0044] A 3D vision camera can calculate the distance to an object based on the parallax between two cameras spaced a certain distance apart, i.e., the pixel distance between the images captured by each camera. A texture projector that projects infrared light in a predetermined pattern may also be included to enable detection of objects of the same color, such as flat surfaces (e.g., white walls).

[0045] Typically, 2D laser scanners are used for mapping, navigation, object recognition, etc., and 3D cameras can be used for navigation, especially for obstacle avoidance, but these are examples and are not necessarily limited to this.

[0046] The loading section (113) is a means for loading items to be transported, and may be a top plate on the upper part of the vehicle body itself, a table placed on the top plate, a lift, a turntable rotating along a vertical axis, a forklift, a conveyor, or a combination thereof. Similar to a forklift, a forklift may also support telescopic and tilting functions.

[0047] The communication unit (114) can communicate with other components within the operation boundary (100), such as the production device (120) and the control device (140), and can also support communication between logistics robots (110), and can also communicate with the charger when performing a charging mission.

[0048] The control unit (115) is a subject that performs overall control of each of the aforementioned components (111, 112, 113, 114), and can perform current mission, current location, destination determination, route planning, load control, etc. based on information obtained from the control device (140) through the communication unit (114).

[0049] FIG. 4 is a perspective view showing an example of the exterior of a logistics robot that can be applied to embodiments of the present invention.

[0050] Referring to FIG. 4, an example of an AMR is illustrated as a logistics robot (110). The body may have a track-shaped planar shape having a long axis extending along a single axis direction as a whole. One drive wheel (111-1) may be arranged in the center of the body in the single-axis direction, may be arranged on one side in the double-axis direction, and another drive wheel (not shown) may be arranged on the other side to face one drive wheel (111-1) in the double-axis direction. This arrangement of the drive wheels may be referred to as a 'differential drive (DD)'. Although not illustrated in FIG. 4, two or more non-drive wheels may be arranged on the lower part of the body. In this case, if two drive wheels rotate in the same direction at the same speed, forward or backward movement is possible along a single axis direction, and if they rotate in opposite directions at the same speed, they may extend along a three-axis direction and rotate around a rotation axis passing through the plane center (C) of the body. In addition, a sensor unit (112) may be placed on the front of the body, and a loading unit (113) may be placed on the upper surface. The loading unit (113) may be configured to be able to be raised and lowered along three axes, and a rack or tray may be fixed to the upper surface through a guide (113-1).

[0051] However, the AMR shape of the above-described Fig. 4 is exemplary, and it is obvious that the AGV may have a similar shape or the AMR may have a different shape.

[0052] Next, the driving process of the logistics robot (110) will be described with reference to Fig. 5.

[0053] Fig. 5 is a flowchart illustrating an example of a driving process of a logistics robot (110) applicable to embodiments of the present invention. In Fig. 5, for convenience, it is assumed that the logistics robot (110) is an AMR capable of positioning and local path setting.

[0054] Referring to Fig. 5, first, while the AMR drives within the operating boundary (100), it can obtain a real-world grid map through lidar, etc. (S501).

[0055] When the AMR transmits the acquired grid map to the control device (140), a grid map editing and matching process can be performed in the map management unit (148) of the control device (140) (S502). Here, the editing process can include a process of setting the aforementioned various zones in the aforementioned grid map, a process of assigning a cost to each grid, etc. Here, the cost assignment can be performed in a direction in which a higher cost is assigned the closer the AMR is to an obstacle or a no-entry area so that the AMR does not move around an obstacle or into an area that it should not enter. This is because, when the AMR sets a local route, it selects a set of cells with the lowest cost among waypoints as the route.

[0056] Additionally, the map matching process may mean a process of matching coordinates between a CAD map used in the design of the operational boundary (100), a real-world grid map (lidar map), and a topology map that has undergone an editing process.

[0057] Afterwards, the control device (140) can share the topology map with all AMRs in the factory through the communication unit (146) (S503).

[0058] Subsequent steps may be applied to individual AMRs.

[0059] The AMR can determine (localize) its current location on the map using sensor data from the sensing unit (112) and the acquired map (S504). For example, the AMR can determine its current location by comparing the surrounding terrain acquired via lidar with the map based on feature points.

[0060] The control device (140) can select a specific AMR and assign a mission, and the mission can be assigned one or more waypoints, which are generally determined through global path planning. The waypoints can be defined as coordinates on a map and can be accompanied by information about the direction (i.e., heading) that the AMR should head in from the coordinates. Based on this mission assignment, a destination can be set for the AMR (Yes in S505), and the AMR can perform local path planning between waypoints based on the cost of the topology map (S506).

[0061] Once the path is determined, the AMR begins driving (S507). If an obstacle is detected by the sensing unit (112) during driving (Yes in S508), the AMR may perform an evasive maneuver by performing a local route search to bypass the detected obstacle (S509). In some cases, depending on the evasive maneuver or the failure of the evasive maneuver, the control device (140) may update the mission of the AMR.

[0062] Additionally, the AMR can also compensate for position errors during movement using the aforementioned odometry technique until it reaches its destination (S510).

[0063] After reaching the destination (S511), the AMR can perform mission-based maneuvers (S512). For example, the AMR can determine whether conditions for entering a specific process area are cleared, retrieve empty pallets at the destination, or drop off loads loaded on the loading section (113).

[0064]

[0065] In one embodiment of the present invention described below, the purpose is to provide a system and method for controlling a logistics robot that can predict the movement of a worker or another logistics robot in an operation boundary (100) using a logistics robot (110) and determine the movement of the logistics robot to be controlled by taking this into consideration.

[0066] Hereinafter, a system for controlling a logistics robot according to one embodiment of the present invention will be described with reference to FIG. 6.

[0067] Figure 6 is a block diagram showing the configuration of a system for controlling a logistics robot according to one embodiment of the present invention.

[0068] Referring to FIG. 6, a system for controlling a logistics robot according to one embodiment of the present invention may include a plurality of logistics robots (110-1, 110-2, 110-3), a control device (140), and a route management device (150). FIG. 6 mainly illustrates components related to an embodiment of the present invention, and it is obvious that a system for controlling an actual logistics robot may include fewer or more components than this.

[0069] Each component is described below.

[0070] A plurality of logistics robots (110-1, 110-2, 110-3) may be provided within an operation boundary (100) according to an embodiment of the present invention. At this time, the plurality of logistics robots (110-1, 110-2, 110-3) may be divided into a target logistics robot (110-1) and peripheral logistics robots (110-2, 110-3). For example, the target logistics robot (110-1) may refer to a logistics robot that is a control target of the present invention, which will be described below, and the peripheral logistics robots (110-2, 110-3) may refer to the remaining logistics robots among the plurality of logistics robots (110-1, 110-2, 110-3) provided within the operation boundary (100), excluding the target logistics robot (110-1). In Fig. 6, for convenience of explanation, it is assumed that there is one target logistics robot (110-1) and two peripheral logistics robots (110-2, 110-3), but this is an example and a larger number of robots can be provided.

[0071] And, the control device (140) according to the above-described method performs control on a plurality of logistics robots (110-1, 110-2, 110-3), but in the embodiment of the present invention described below, the focus is on performing control on the target logistics robot (110-1) among the plurality of logistics robots (110-1, 110-2, 110-3).

[0072]

[0073] Meanwhile, as described above, when multiple logistics robots (110-1, 110-2, 110-3) are provided within the operation boundary (100), the congestion of the operation boundary (100) may increase due to the multiple logistics robots (110-1, 110-2, 110-3). In an environment with increased congestion, it may be difficult to simply control the movement of multiple logistics robots (110-1, 110-2, 110-3) to increase the productivity of the operation boundary (100). In addition, in the operation boundary (100), there may be multiple logistics robots (110-1, 110-2, 110-3) as well as multiple workers (P1, P2), and it may also be difficult to control the movement and operation of the multiple logistics robots (110-1, 110-2, 110-3) due to the multiple workers (P1, P2) whose movements are unpredictable.

[0074] Accordingly, a system for controlling a logistics robot according to an embodiment of the present invention may be provided with a path management device (150) that can communicate with a control device (140) and enable safe and efficient movement of the logistics robot (110) within an operation boundary (100).

[0075] A path management device (150) according to an embodiment of the present invention can predict expected movement paths of a plurality of workers (P1, P2) or a plurality of surrounding logistics robots (110-2, 110-3) located within an operation boundary (100), and determine a possible movement path along which a target logistics robot (110-1) can move based on the predicted expected movement path. To this end, the path management device (150) may include an information collection unit (151), a path prediction unit (152), and a path determination unit (153). In the following, for the convenience of explanation, it is assumed that there are two workers (P1, P2), but this is merely exemplary and the present invention is not limited thereto.

[0076] Below, each component of the path management device (150) is described.

[0077] The information collection unit (151) can collect information necessary to predict the expected movement paths of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) or to determine the possible movement paths of the target logistics robot (110-1). For example, the information collection unit (151) can collect at least one of map information of the operation boundary (100), work history information for multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3), and location information for multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3).

[0078] At this time, the map information of the operation boundary (100) can be collected by receiving it from the control device (140), and the work history information for multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) can be collected by receiving it from the production management system (160). In addition, the location information of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) can be collected by receiving it directly from each of them. For example, in the case of multiple workers (P1, P2), GPS information can be received from the portable terminals each of the multiple workers (P1, P2). However, this is exemplary, and it goes without saying that various devices capable of providing GPS information other than portable terminals can be used when collecting the location information of multiple workers (P1, P2).

[0079] The work history information for multiple workers (P1, P2) or multiple peripheral logistics robots (110-2, 110-3) may be information about the work performed in each process provided in the operation boundary (100), and may include, for example, information about the work start time, work end time, and work performer. However, this is merely exemplary and is not necessarily limited thereto.

[0080]

[0081] Next, the path prediction unit (152) can predict the expected movement paths of the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) based on at least one piece of information collected by the information collection unit (151). For example, the path prediction unit (152) can predict the expected movement paths of the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) based on a pre-learned path prediction model. At this time, the path prediction model may be a model derived by learning information about the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) in the past as learning data. In addition, the pre-learned path prediction model can take at least one piece of information collected by the information collection unit (151) as an input value, and produce movement paths that represent the location information of the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) in a time series manner as an output value.

[0082] For example, such a path prediction model could be a Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), which are network-based models suitable for predicting distant paths using contextual information as network input. However, this is merely an example and should not be construed as a limitation.

[0083]

[0084] The path determination unit (153) can determine a possible movement path for the target logistics robot (110-1) to move within the operation boundary (100) based on the expected movement path predicted by the path prediction model in the path prediction unit (152). Prior to this, the path determination unit (153) can determine a possible movement path for the target logistics robot (110-1) when a new task is assigned to the target logistics robot (110-1) from the control device (140).

[0085] For example, the above-described production management system (160) can transmit map information of the operation boundary (100) and work history information of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) to the control device (140), and the control device (140) can assign a new task to be performed by the target logistics robot (110-1) based on the information collected from the production management system (160). At this time, the control device (140) can assign the new task by including information on the starting point and destination of the target logistics robot (110-1).

[0086] When a new task is assigned to the target logistics robot (110-1), the path determination unit (153) can determine the possible movement path of the target logistics robot (110-1) based on the expected movement paths of the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) predicted by the path prediction unit (152). However, since the expected movement path is one-dimensional point-type data, if the possible movement path of the target logistics robot (110-1) is determined based on this, there is a concern that a safety accident may occur due to interference between the target logistics robot (110-1) and the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3). To prevent this in advance, the path determination unit (153) can determine the possible movement path of the target logistics robot (110-1) by further considering the size information of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) in the expected movement path predicted by the path prediction unit (152).

[0087] To this end, the information collection unit (151) can further collect size information depending on whether or not multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) are transporting materials. At this time, the information collection unit (151) can collect size information depending on whether or not the material is transported based on the size information of the material provided from the production management system (160) and the size information of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) previously stored within the operation boundary (100).

[0088] For example, when multiple workers (P1, P2) or multiple peripheral logistics robots (110-2, 110-3) are transporting materials, the size information of the multiple workers (P1, P2) or multiple peripheral logistics robots (110-2, 110-3) may be size information about the larger object among the multiple workers (P1, P2) or the multiple peripheral logistics robots (110-2, 110-3) and the materials they transport. For example, when multiple workers (P1, P2) are transporting materials that are larger than their own sizes, the size of the materials may become the sizes of the multiple workers (P1, P2).

[0089] Meanwhile, when multiple workers (P1, P2) or multiple peripheral logistics robots (110-2, 110-3) are not transporting materials, the size information of the multiple workers (P1, P2) or multiple peripheral logistics robots (110-2, 110-3) may be size information for each of the multiple workers (P1, P2) or multiple peripheral logistics robots (110-2, 110-3).

[0090] The size information described above may be, for example, information on the external dimensions of multiple workers (P1, P2), multiple surrounding logistics robots (110-2, 110-3), and materials, and may include information including width, height, and depth. However, this is merely exemplary and should not be construed as being limited thereto.

[0091] As described above, when size information of a plurality of workers (P1, P2) or a plurality of surrounding logistics robots (110-2, 110-3) is collected from the information collection unit (151), the path determination unit (153) can determine a possible movement path of the target logistics robot (110-1) based on the size information of a plurality of workers (P1, P2) or a plurality of surrounding logistics robots (110-2, 110-3) collected from the information collection unit (151) and the predicted movement paths of a plurality of workers (P1, P2) or a plurality of surrounding logistics robots (110-2, 110-3) provided from the path prediction unit (152). This will be described with reference to FIG. 7.

[0092] FIG. 7 is a drawing for explaining a process for determining a movable path of a target logistics robot according to one embodiment of the present invention.

[0093] Referring to FIG. 7, the path determination unit (153) can determine a possible movement path of the target logistics robot (110-1) by reflecting the information collected from the information collection unit (151) and the path prediction unit (152) on the map information of the operation boundary (100). For example, the path determination unit (153) can determine the effective occupied area of ​​the multiple workers (P1, P2) or the multiple surrounding logistics robots (110-2, 110-3) by combining the size information of the multiple workers (P1, P2) or the multiple surrounding logistics robots (110-2, 110-3) collected by the information collection unit (151) with the expected movement path predicted by the path prediction unit (152). The path determination unit (153) can reflect information on the determined effective occupied area on the map information of the operation boundary (100).

[0094] As shown in Fig. 7, the map information of the operation boundary (100) includes the expected movement path of multiple workers (P1, P2). ) according to the effective occupancy area ( ) and the expected movement path of multiple surrounding logistics robots (110-2, 110-3) ) according to the effective occupancy area ( ) can be reflected. In Fig. 7, for convenience of explanation, the effective occupied area of ​​multiple workers (P1, P2) ) and the effective occupied area of ​​multiple surrounding logistics robots (110-2, 110-3) ) is reflected one by one, but this is an example, and it is of course possible for the effective occupied area to be reflected according to the number of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3).

[0095] In addition, the map information of the operation boundary (100) may show the starting point (S) and the destination (D) for the new task assigned to the target logistics robot (110-1) through the control device (140). The target logistics robot (110-1) must move from the starting point (S) to the destination (D) according to the assigned new task and perform the task. To this end, the path determination unit (153) may determine the movement path of the target logistics robot (110-1). For example, the path determination unit (153) may determine the effective occupied area (of a plurality of workers (P1, P2) in the map area corresponding to the operation boundary (100). ) and the effective occupied area of ​​multiple surrounding logistics robots (110-2, 110-3) ) can be used to determine the excluded area.

[0096] And, the path determination unit (153) can derive the movable path (R) of the target logistics robot (110-1) based on the determined area. In other words, the path determination unit (153) can derive the effective occupied area (R) of multiple workers (P1, P2). ) and the effective occupied area of ​​multiple surrounding logistics robots (110-2, 110-3) ) can determine a possible movement path (R) of the target logistics robot (110-1) that can connect the starting point (S) and the destination (D) in the area excluding the area excluded.

[0097]

[0098] Returning to FIG. 6, if there are multiple possible movement paths of the target logistics robot (110-1), the path determination unit (153) can determine the required time for each of the multiple possible movement paths and ultimately derive the possible movement path with the shortest required time among the determined required times.

[0099] Meanwhile, the movable path (R) determined by the path determination unit (153) may be composed of multiple movable sections. In this case, the path determination unit (153) may determine the movable path (R) and at the same time determine the moving speed of the target logistics robot (110-1) for each of the multiple movable sections constituting the movable path (R). For example, the path determination unit (153) may determine the moving speed for each of the multiple movable sections so that the target logistics robot (110-1) moves at a constant speed. In other words, when the multiple movable sections are composed of a first section and a second section, the path determination unit (153) may determine the moving speed for the target logistics robot (110-1) to move at a constant speed in each of the first section and the second section, but may determine that the moving speed in the first section and the moving speed in the second section are different from each other.

[0100] However, if the movement speed of the target logistics robot (110-1) is determined as a fixed value, it may not be able to respond to sudden external factors occurring within the operation boundary (100). Therefore, in order to flexibly respond to external factors, the path determination unit (153) may determine the movement speed of the target logistics robot (110-1) to be variable for each of a plurality of possible movement sections. In this case, the path determination unit (153) may determine the expected time required according to the possible movement path of the target logistics robot (110-1) and obtain information on the actual time required as the target logistics robot (110-1) moves. In addition, if a delay occurs in the actual time required compared to the expected time required, the speed may be variable for the section in which the target logistics robot (110-1) moves in order to compensate for the time delay.

[0101]

[0102] As described above, when a movable path for the target logistics robot (110-1) is determined in the path determination unit (153), the path management device (150) can transmit information about the determined movable path to the control device (140). The control device (140) can control the target logistics robot (110-1) based on the movable path provided by the path management device (150). Accordingly, the target logistics robot (110-1) can perform a new task assigned by the control device (140) along the movable path.

[0103] As previously described with reference to FIGS. 3 and 5, the target logistics robot (110-1) not only follows a movable path but can also autonomously perform avoidance measures to address issues such as deadlocks that may arise during movement. Accordingly, the movable path transmitted to the target logistics robot (110-1) may differ from the actual movement path. Accordingly, the control device (140) may collect information on the actual movement path of the target logistics robot (110-1) and transmit this to the path management device (150).

[0104] The path management device (150) can compare information about the actual movement path provided from the control device (140) with a predetermined movable path to determine an error value between the two paths. In addition, the path management device (150) can compare the determined error value with a preset reference value, and if the error value is greater than the reference value, can redetermine the movable path for the target logistics robot (110-1). In addition, the path management device (150) can transmit the redetermined movable path back to the control device (140) so that the target logistics robot (110-1) is controlled based on the movable path redetermined by the control device (140), thereby allowing the movement path of the target logistics robot (110-1) to be adjusted in real time.

[0105]

[0106] Hereinafter, a method for controlling a logistics robot based on the system for controlling the logistics robot described above through FIGS. 6 to 7 will be described with reference to FIG. 8. In FIG. 6, each component of the path management device (150) is described, but in FIG. 8, for convenience of explanation, it is assumed that all functions performed by each component are performed by the path management device (150).

[0107] In addition, since each step is described in detail through Figures 6 and 7, it will be briefly explained below.

[0108] FIG. 8 is a drawing for explaining a method for controlling a logistics robot according to one embodiment of the present invention.

[0109] Referring to FIG. 8, the path management device (150) can collect map information about the operation boundary (100) from the control device (140) (S801), and can collect work history information about a plurality of workers (P1, P2) or a plurality of surrounding logistics robots (110-2, 110-3) and information about the size of materials transported by the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) from the production management system (160) (S802). In addition, the path management device (150) can receive location information from each of the plurality of workers (P1, P2) or the plurality of surrounding logistics robots (110-2, 110-3) (S803-1, S803-2). The path management device (150) can predict the expected movement path of multiple workers (P1, P2) or multiple surrounding logistics robots (110-2, 110-3) based on the collected information (S804).

[0110] And, the path management device (150) can predict the expected movement paths of a plurality of workers (P1, P2) or a plurality of surrounding logistics robots (110-2, 110-3), and collect information about a new task assigned to the target logistics robot (110-1) from the control device (140) (S805) to determine the possible movement path of the target logistics robot (110-1) (S806).

[0111] When the movable path of the target logistics robot (110-1) is determined, the path management device (150) can transmit the determined movable path to the control device (140) (S807-1), and the control device (140) can transmit the provided movable path to the target logistics robot (110-1) (S807-2) to control the target logistics robot (110-1) to move along the movable path.

[0112] Thereafter, the control device (140) can collect information on the actual movement path of the target logistics robot (110-1) from the target logistics robot (110-1) (S808-1) and provide the collected information on the actual movement path to the path management device (150) (S808-2).

[0113] The path management device (150) can compare the actual movement path of the target logistics robot (110-1) provided from the control device (140) with a pre-determined possible movement path to determine an error value between the two paths (S809). In addition, the path management device (150) can compare the determined error value with a preset reference value, and if the error value is greater than the reference value, can re-determine the possible movement path of the target logistics robot (110-1) (S810).

[0114] When the movable path of the target logistics robot (110-1) is re-determined, the path management device (150) can transmit the re-determined movable path to the control device (140) (S811-1), and the control device (140) can transmit this back to the target logistics robot (110-1) (S811-2) to control the target logistics robot (110-1) to move along the re-determined movable path.

[0115]

[0116] According to the above, the system and method for controlling the logistics robot of the present invention can prevent accidents between workers and logistics robots or between logistics robots due to unexpected situations by predicting the movement path of a worker or another logistics robot and determining the movement path of the logistics robot to be controlled by considering the predicted movement path.

[0117] In addition, by determining the movement speed of the logistics robot to be controlled when determining the movement path of the logistics robot, the movement speed of the logistics robot can be freely controlled, thereby improving the productivity of the factory even with a small number of logistics robots.

[0118]

[0119] While the present invention has been illustrated and described with respect to specific embodiments thereof, it will be apparent to those skilled in the art that the present invention may be variously improved and modified without departing from the technical spirit of the invention as defined by the claims below.

[0120] The present invention described above can be implemented as computer-readable code on a medium having a program recorded thereon. Computer-readable media include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include hard disk drives (HDDs), solid-state disks (SSDs), silicon disk drives (SDDs), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, and optical data storage devices. Therefore, the above detailed description should not be construed as limiting in any respect, but rather as illustrative. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are intended to be included within the scope of the present invention.

Claims

1. A path management device that predicts the expected movement paths of multiple workers or multiple surrounding logistics robots located within an operation boundary, and determines a possible movement path for the target logistics robot to move within the operation boundary based on the predicted expected movement path when assigning a new task to a target logistics robot located within the operation boundary; and A system for controlling a logistics robot, comprising: a control device for controlling the target logistics robot to move along a movable path determined by the path management device; 2. In claim 1, The above path management device A system for controlling a logistics robot, characterized in that it collects at least one piece of information from among map information of the above-mentioned operation boundary, work history information for the plurality of workers or the plurality of surrounding logistics robots, and location information of the plurality of workers or the plurality of surrounding logistics robots, and predicts the expected movement path of the plurality of workers or the plurality of surrounding logistics robots based on the at least one piece of information collected.

3. In claim 2, The above path management device A system for controlling a logistics robot, characterized in that the expected movement path is predicted based on a pre-learned path prediction model that uses at least one of the collected pieces of information as an input value and a movement path representing the location information of a plurality of workers or a plurality of surrounding logistics robots in a time series manner as an output value.

4. In claim 1, The above path management device A system for controlling a logistics robot, characterized in that the possible movement path of the target logistics robot is determined by further considering size information according to whether the plurality of workers or the plurality of surrounding logistics robots transport materials in the predicted expected movement path.

5. In claim 4, A system for controlling a logistics robot, characterized in that when the plurality of workers or the plurality of surrounding logistics robots are transporting materials, the size information is size information about an object with a larger size among the plurality of workers or the plurality of surrounding logistics robots and the material.

6. In claim 4, The above path management device A system for controlling a logistics robot, characterized in that the effective occupancy area of ​​the plurality of workers or the plurality of surrounding logistics robots is determined by combining the size information with the predicted expected movement path, and the possible movement path is determined based on the determined effective occupancy area.

7. In claim 6, The above path management device A system for controlling a logistics robot, characterized in that the determined effective occupied area is reflected in the map information of the operation boundary, an area from which the effective occupied area is excluded is determined from the map area corresponding to the operation boundary based on the map information in which the effective occupied area is reflected, and the possible movement path is determined based on the determined area.

8. In claim 1, A system for controlling a logistics robot, wherein the above-mentioned movable path is composed of a plurality of movable sections, and when the above-mentioned movable path is determined, the movement speed for each of the plurality of movable sections is determined together.

9. In claim 1, The above path management device A system for controlling a logistics robot, characterized in that when there are multiple possible movement paths along which the target logistics robot can move, the time required for each of the multiple possible movement paths is determined, and the movement path with the shortest time among the determined times is finally determined.

10. In claim 1, The above path management device A system for controlling a logistics robot, characterized in that it receives information on the actual movement path of the target logistics robot from the control device, determines an error value between the actual movement path and the possible movement path, and re-determines the possible movement path if the determined error value is greater than a preset reference value.

11. A step of predicting the expected movement path of multiple workers or multiple surrounding logistics robots located within the operational boundary; When assigning a new task to a target logistics robot located within the above-mentioned operation boundary, a step of determining a possible movement path that the target logistics robot can move within the above-mentioned operation boundary based on the predicted expected movement path; and A method for controlling a logistics robot, comprising: a step of controlling the target logistics robot to move along the determined movable path; 12. In claim 11, The above predicting steps are A step of collecting at least one of the following information: map information of the above-mentioned operation boundary, work history information for the plurality of workers or the plurality of surrounding logistics robots, and location information for the plurality of workers or the plurality of surrounding logistics robots; and A method for controlling a logistics robot, characterized in that it includes a step of predicting an expected movement path of a plurality of workers or a plurality of surrounding logistics robots based on at least one piece of information collected above.

13. In claim 12, The above predicting steps are A method for controlling a logistics robot, characterized by comprising: a step of predicting the expected movement path based on a pre-learned path prediction model that uses at least one of the collected pieces of information as an input value and a movement path representing the location information of a plurality of workers or a plurality of surrounding logistics robots in a time series as an output value.

14. In claim 11, The above decision steps are A method for controlling a logistics robot, characterized in that it comprises a step of determining the possible movement path of the target logistics robot by further considering size information according to whether the plurality of workers or the plurality of surrounding logistics robots transport materials in the predicted expected movement path.

15. In claim 14, A method for controlling a logistics robot, characterized in that when the plurality of workers or the plurality of surrounding logistics robots are transporting materials, the size information is size information about an object with a larger size among the plurality of workers or the plurality of surrounding logistics robots and the material.

16. In claim 14, The above decision steps are A step of determining the occupied area occupied by the plurality of workers or the plurality of surrounding logistics robots within the operation boundary by combining the size information with the predicted expected movement path; and A method for controlling a logistics robot, characterized by including a step of determining the movable path based on the determined occupied area.

17. In claim 16, The above decision steps are A step of reflecting the determined valid occupied area in the map information of the operating boundary; A step of determining an area from which the effective occupied area is excluded from the map area corresponding to the operational boundary based on map information reflecting the effective occupied area; and A method for controlling a logistics robot, characterized by including a step of determining the movable path based on the determined area.

18. In claim 11, A method for controlling a logistics robot, wherein the above-mentioned movable path is composed of a plurality of movable sections, and when the above-mentioned movable path is determined, the movement speed for each of the plurality of movable sections is determined together.

19. In claim 11, The above decision steps are When the target logistics robot has multiple movable paths, a step of determining the time required for each of the multiple movable paths; and A method for controlling a logistics robot, characterized in that it comprises a step of finally determining a possible movement path having the shortest required time among the above-determined required times.

20. In claim 11, After the above controlling step, A step of receiving information on the actual movement path of the target logistics robot and determining an error value between the actual movement path and the possible movement path; and A method for controlling a logistics robot, characterized in that it further includes a step of re-determining the movable path when the determined error value is greater than a preset reference value.

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