CONTROL METHOD OF AN INTELLIGENT LOGISTICS VEHICLE AND CONTROL DEVICE

Abstract

A method and a device for controlling an intelligent logistics vehicle (110) are proposed, wherein the method comprises: identifying a task situation of an intelligent logistics vehicle (110); determining a route, based on map information in which at least some virtual paths within an operational boundary are assigned different route-selection-based scores for each task situation, such that the intelligent logistics vehicle (110) moves along a virtual path corresponding to the identified task situation based on a route-selection-based score corresponding to the identified task situation; and transmitting route information corresponding to the determined route to the intelligent logistics vehicle (110).

Description

Technical field

[0001] The present disclosure relates to a control method of an intelligent logistics vehicle and a control device for providing information about a driving route according to a task performance of an intelligent logistics vehicle. Background technology

[0002] In recent years, intelligent logistics vehicles have been introduced for the flexible and efficient delivery and transport of components and the like, not only in general logistics warehouses and factories, but also in operational boundaries (e.g., smart factories, etc.) where goods with different specifications are manufactured using various components.

[0003] The intelligent logistics vehicle is a concept that combines an autonomous mobile robot (AMR) and an automated guided vehicle (AGV), and such an intelligent logistics vehicle can perform movements and operations by being controlled by a control system.

[0004] A virtual path along which an intelligent logistics vehicle can move can be defined within a specific area or zone within the operational boundary. The control system can also generate a route for each intelligent logistics vehicle based on the virtual path defined within the operational boundary, according to the task at hand, and transmit this route to the vehicle. However, the virtual path defined within the operational boundary is not differentiated for each task of the intelligent logistics vehicle, so that when generating routes for the intelligent logistics vehicle, areas may arise where the routes intersect or overlap.

[0005] Furthermore, when performing tasks along the route provided by the control system, the intelligent logistics vehicles can perform evasive maneuvers, for example by reducing the speed of the intelligent logistics vehicle, to prevent collisions or downtime of intelligent logistics vehicles due to the occurrence of sections where the routes intersect or overlap.

[0006] This can lead to a problem of reduced mobility of intelligent logistics vehicles at the operational boundary, and to prevent this, it is necessary to propose a route generation plan in which the task-specific routes of intelligent logistics vehicles do not cross or overlap, and to make these available to the intelligent logistics vehicles.

[0007] The facts described above as background technology serve only to provide a better understanding of the background of the present disclosure and are not to be understood as an acknowledgment that they correspond to prior art already known to the person skilled in the art. RevelationTechnical Task

[0008] The present disclosure is proposed to solve such a problem and aims to provide a control method for an intelligent logistics vehicle and a control device to provide information about a driving route according to the task performance of an intelligent logistics vehicle, so that the driving routes of different intelligent logistics vehicles with different tasks to be performed do not cross or overlap.

[0009] The technical problems to be achieved with the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by the person skilled in the field of the present disclosure from the following description. Technical solution

[0010] A control method for an intelligent logistics vehicle according to the present disclosure for accomplishing the above-mentioned task comprises identifying a task situation of an intelligent logistics vehicle, determining a route based on map information in which different route-selection-based scores are assigned to at least some virtual paths within an operational boundary for each task situation, so that the intelligent logistics vehicle moves along the virtual path corresponding to the identified task situation based on the route-selection-based score corresponding to the identified task situation, and transmitting route information corresponding to the determined route to the intelligent logistics vehicle.

[0011] Furthermore, according to the present disclosure, a control device for accomplishing the above-mentioned task comprises a map management unit for providing map information in which different route-selection-based scores are assigned to at least some virtual paths within an operational boundary for each task situation, and a route selection unit for identifying the task situation of an intelligent logistics vehicle, for determining a route based on the map information provided by the map management unit, so that the intelligent logistics vehicle moves on the virtual path corresponding to the identified task situation based on the route-selection-based score corresponding to the identified task situation, and for transmitting route information corresponding to the determined route to the intelligent logistics vehicle. Beneficial effects

[0012] As described above, a control method of an intelligent logistics vehicle and a control device of the present disclosure can determine routes such that an intelligent logistics vehicle moves along a virtual path corresponding to the determined task situation based on a route selection-based score corresponding to the determined task situation, thereby preventing routes from crossing or overlapping and improving the mobility of the intelligent logistics vehicle.

[0013] Furthermore, it is possible to easily identify the logistics flow within an operational boundary by determining the route for each task situation of the intelligent logistics vehicle.

[0014] The effects achievable with the present disclosure are not limited to the effects mentioned above, and other, unmentioned effects will be clearly understandable to the person skilled in the art in the field of the present disclosure from the following description. Description of the drawings Fig. Figure 1 is a block diagram showing an example of an operating limit configuration applicable to exemplary embodiments of the present disclosure. Fig. Figure 2 is a block diagram showing an example of a control device configuration applicable to exemplary embodiments of the present disclosure. Fig. Figure 3 is a block diagram showing an example of a configuration of an intelligent logistics vehicle applicable to exemplary embodiments of the present disclosure. Fig. Figure 4 is a perspective view showing an example of the external appearance of an intelligent logistics vehicle applicable to exemplary embodiments of the present disclosure. Fig. Figure 5 is a flowchart showing an example of a driving process of an intelligent logistics vehicle applicable to exemplary embodiments of the present disclosure. Fig. Figure 6 is a view showing an operating boundary equipped with a control device according to an exemplary embodiment of the present disclosure. The Fig. 7, Fig. 8, Fig. 9 to Fig. Figure 10 shows a determination of a route for an intelligent logistics vehicle according to an exemplary embodiment of the present disclosure. Fig. Figure 11 is a view showing an intelligent logistics vehicle driving on the basis of route information according to an exemplary embodiment of the present disclosure. Fig. Figure 12 is a view showing a control method for an intelligent logistics vehicle according to an exemplary embodiment of the present disclosure. Mode of Revelation

[0015] In describing an exemplary embodiment disclosed in this description, a detailed description of that embodiment may be omitted if it is determined that a detailed description of the associated known technology could obscure the essence of the exemplary embodiment disclosed in this description. Furthermore, the accompanying drawings may only serve to facilitate understanding of the exemplary embodiment disclosed in this description, and the technical concept disclosed in this description need not be limited by the accompanying drawings and should be understood to include all modifications, equivalents, or substitutions contained within the meaning and technical scope of this disclosure.

[0016] Terms that include ordinal numbers, such as "first..." and "two...", can be used to describe different components, but the components are not limited by these terms. The terms can only be used to distinguish one component from another.

[0017] When it is mentioned that one component is "connected" or "linked" to another component, it can be assumed that the component may be directly connected or linked to the other component, but that another component may be in between. Conversely, when it is mentioned that one component is "directly connected" or "directly linked" to another component, it can be assumed that no other component is in between.

[0018] Singular expressions can include plural expressions unless the context clearly indicates otherwise.

[0019] In the present description, terms such as "exhibit" or "have" may be used to indicate the presence of features, numbers, steps, processes, components, parts or combinations thereof described in the description, and it should be understood that this does not exclude the presence or addition of one or more other features, numbers, steps, processes, components, parts or combinations thereof.

[0020] Exemplary embodiments disclosed in the present description are described in detail below with reference to the accompanying drawings, wherein identical or similar components are designated with the same reference numerals regardless of the drawing codes and redundant descriptions thereof are omitted.

[0021] Furthermore, terms such as "...unit" or "control unit" included in the designations of the internal configurations of an intelligent logistics vehicle or control unit are merely terms commonly used to describe control devices for specific functions and do not refer to a generic functional unit. For example, each control device may include a modem / transceiver for communicating with other control devices or sensors for controlling the assigned functions, memory for storing operating systems or logic instructions and input / output information, and one or more processors for performing the determinations, calculations, and decisions required to control the assigned functions. Depending on the implementation, a single processor may be responsible for the calculations for multiple control devices.

[0022] First, with reference to the Fig. 1 describes a configuration of an operating boundary in which an intelligent logistics vehicle is arranged and operated according to an exemplary embodiment.

[0023] Fig. Figure 1 is a block diagram showing an example of an operating limit configuration applicable to exemplary embodiments of the present disclosure.

[0024] With reference to the Fig. 1 can have an operating boundary 100, an intelligent logistics vehicle 110, a production device 120, a monitoring device 130 and a control device 140.

[0025] The operational boundary 100 can be equipped with multiple intelligent logistics vehicles 110, multiple production devices 120, and multiple monitoring devices 130, depending on the production process and the desired production speed of a product. The operational boundary 100 can be implemented as a smart factory, but is not limited to this. The individual components are described below.

[0026] Initially, the intelligent logistics vehicle 110 can include an autonomous mobile robot (hereinafter referred to as "AMR" for simplicity) and a driverless transport vehicle (hereinafter referred to as "AGV" for simplicity). According to an operating guideline of the intelligent logistics vehicle 110, either only one type of AGV and AMR can be operated within the operating boundary 100, or both the AGV and the AMR can be operated together within a single operating boundary 100.

[0027] The AGV can generally perform the required operations (movement, change of direction, stop, etc.) within the operating limit of 100 by detecting and following the guidance device installed on the floor. This guidance device may consist of visually detectable markers (dots, 2D codes, etc.), tags that can be detected contactlessly at close range (e.g., NFC tags, RFID tags, etc.), magnetic strips, wires, and the like, but this is only illustrative and not necessarily limited to these. The guidance device may be continuously arranged on the floor or at discontinuous intervals.Since the AGV essentially operates by detecting and following the guidance device, the guidance device must be installed before operation, and it is necessary to physically set up or modify the guidance device if the AGV is moved to a new route or the existing route is changed before operation. Furthermore, the AGV does not deviate from the route specified by the guidance device, so it is common practice to stop when an obstacle is detected on or near the route until the detected obstacle is removed or separate control is provided. During AGV operation, the control device 140 should control the AGV based on the guidance device, so that commands such as "Drive until the third marker is detected" and "Change direction by 90 degrees when the third marker is detected" can be issued either as a single command unit or as a task unit (e.g.,(Collecting, delivering, loading, patrolling, etc.) can be transmitted to the AGV with a multiple set of commands.

[0028] The AMR can determine its current location by sensing its surroundings (i.e., positioning), and the point at which route planning using positioning and a map becomes possible may be the key difference compared to an AGV. Therefore, if the AMR and the control device 140 share a map with compatible coordinates, the control device 140 can control the AMR by assigning it a route based on the coordinates. Furthermore, if the AMR detects an obstacle while driving, it can autonomously determine an alternative route, navigate around the obstacle, and return to its original route.The function of setting the AMR route to one or more waypoint coordinates by the control device 140 can be called global path planning, and the function of setting a movement route or an alternative route between the waypoint coordinates according to the global path planning can be called local path planning.

[0029] A more detailed configuration of the intelligent logistics vehicle 110 will be provided later with reference to the Fig. 3 and Fig. 4 described, and a driving control operation of the AMR will be described later with reference to the associated Fig. 5 described.

[0030] Furthermore, a production device 120 can be defined as a device (e.g., robot arm, conveyor belt, etc.) that carries out the production process of the product within the operating boundary 100. In a broader sense, it can also refer to a device that serves to support tasks such as the entry and exit of the intelligent logistics vehicle 110 when the production process is carried out by a person. The device, which is set up to support the execution of tasks, can be a device for detecting the state of a specific location where a pallet transported by the intelligent logistics vehicle 110 can be placed or picked up within a zone in which a specific production process is carried out, a device for determining the degree of process progress, and a means for blocking entry and exit within a zone, etc.being, however, is not necessarily limited to that.

[0031] For example, the production device 120 can be controlled via a programmable logic controller (PLC) and communicate with the control device 140 regarding the process progress.

[0032] The monitoring device 130 can perform a function for acquiring information to determine a situation within the operating limit 100 and transmitting it to the control device 140. For example, the monitoring device 130 can include a camera, a proximity sensor, and the like, but is not necessarily limited to these.

[0033] The control device 140 can communicate with the aforementioned components 110, 120, and 130 to obtain information necessary for operating the operating boundary 100 or for controlling each component. For example, the control device 140 can perform the scheduling, route setting, order assignment, process management per product, material management, and similar functions of the intelligent logistics vehicle 110.

[0034] In implementations, the control device 140 can include a local control device (ACS: AMR / AGV control system) for controlling the surrounding process equipment based on the locations of the AGVs / AMRs and for performing task-based control of the AGVs / AMRs, as well as an integrated control device (MoRIMS: Mobile Robot Integrated Monitoring System) for integrating and controlling two or more local control devices. The integrated control device can monitor the status and route, logistics flow settings, and traffic control of all intelligent logistics robots 110 within the operating boundary 100 from any of the multiple local control devices.For example, if the local control devices (ACS) are equipped as intelligent logistics robots of the same manufacturer or model, the integrated control device can perform integrated collision avoidance control, such as analyzing bottleneck levels in intersection / overlap areas, controlling driving acceleration / deceleration, and regenerating alternative routes, through heterogeneous traffic distribution control based on information obtained from the majority of local control devices (ACS).

[0035] Furthermore, the integrated control device can have a Manufacturing Execution System (MES) as a higher-level control device, and the Manufacturing Execution System (MES) can in turn be connected to Advanced Planning and Scheduling (APS).

[0036] In addition to configurations 110, 120, 130, 140 of the above-mentioned operating limit 100, it is obvious that a device for communication between components such as beacons, repeaters, APs (Access Points), a charger for charging the intelligent logistics vehicle 110, a cargo space for storing or loading parts, a space for storing finished products or intermediate products, traffic lights, interrupters and a waiting room for idle intelligent logistics vehicles 110 may be appropriately arranged within the operating limit 100.

[0037] The following refers to the Fig. 2 a configuration of the control device 140 is described which is applicable to exemplary embodiments of the present disclosure.

[0038] Fig. Figure 2 is a block diagram showing an example of a control device configuration applicable to exemplary embodiments of the present disclosure. Each in Fig. The component shown in 2 may mainly represent components relating to exemplary embodiments of the present disclosure, and the actual implementation of the control device 140 may contain more or fewer components.

[0039] With reference to the Fig. 2 The control device 140 can include a firmware management unit 141, a traffic control unit 142, a process management unit 143, a production / logistics management unit 144, an inventory management unit 145, a communication unit 146, a vehicle monitoring unit 147 and a map management unit 148.

[0040] The firmware management unit 141 can retrieve the latest firmware of the intelligent logistics vehicle 110 via the communication unit 146 and transfer it to the intelligent logistics vehicle 110 to perform a firmware update, thus keeping the firmware of the intelligent logistics vehicle 110 up to date.

[0041] The traffic control unit 142 can control the traffic light and the interrupter based on the route of the intelligent logistics vehicle 110 and recalculate the route of the intelligent logistics vehicle 110 according to the traffic.

[0042] The Process Management Unit 143 can define a process for each product and manage tasks such as the degree of process progress and a progress location.

[0043] The production / logistics management unit 144 can dispatch the intelligent logistics vehicle 110 on a task basis.

[0044] The inventory management unit 145 can manage the location and quantity for each material, and this information can be useful for a more efficient process, e.g. by allowing the intelligent logistics vehicle 110 to travel to a destination in advance to pick up or return pallets before the actual assembly or consumption of the materials is recorded.

[0045] The communication unit 146 can perform communication with internal components of the operating boundary 100, such as the intelligent logistics vehicle 110, the production device 120 and the monitoring device 130, as well as with external units, such as a firmware update server.

[0046] The vehicle monitoring unit 147 can monitor the location, route, battery status, communication status, powertrain status, and similar data of each intelligent logistics vehicle 110. The route can be a concept that includes a waypoint-based global route and a local real-time route. Furthermore, the battery status can include voltage, current, temperature, voltage and current peaks, state of charge (SOC), state of health (SOH), and similar data. The communication status can include information about the currently active communication protocol (such as Wi-Fi), a connected access point (AP), the distance to the AP, the channel in use, and similar data. Additionally, the powertrain status can include the load, temperature, rotational speed, and similar data of the drive system.

[0047] Furthermore, the vehicle monitoring unit 147 can identify the task, operating mode, firmware version, and the like that are currently assigned to the individual intelligent logistics vehicle 110.

[0048] The map management unit 148 can receive map data in the form of a grid map, captured by the AMR among the intelligent logistics vehicles 110 while driving within the operational boundary 100, and provide a factory manager with a tool for editing the captured map data. Editing the map data allows the definition of zones for performing one or more preset operations upon entry of the intelligent logistics vehicle 110, virtual lanes, intersections, and restricted access zones; however, this is only illustrative and not necessarily limited to these functions. Furthermore, the map management unit 148 can distribute the original grid map to the other intelligent logistics vehicles 110 via the communication unit 146, with the exception of the intelligent logistics vehicle 110 that receives the corresponding map through actual driving.

[0049] Next, an intelligent logistics vehicle will be developed, referencing the Fig. 3 and Fig. 4 described.

[0050] Fig. Figure 3 is a block diagram showing an example of a configuration of an intelligent logistics vehicle applicable to exemplary embodiments of the present disclosure.

[0051] With reference to the Fig. 3. The intelligent logistics vehicle 110 can have a driving unit 111, a sensor unit 112, a charging unit 113, a communication unit 114, and a control unit 115. The individual components are described below.

[0052] The drive unit 111 can include a drive source, a wheel, a suspension, and the like, which are involved in the movement, steering, and stopping of the intelligent logistics vehicle 110. The drive source can use an electric motor powered by an onboard battery (not shown). The wheel can have one or more drive wheels that receive the driving force from the drive source, and a non-drive wheel that rotates due to the movement of the vehicle body without receiving the driving force. Depending on the implementation, the rotation of each drive wheel can be controlled independently if multiple drive wheels are provided and the drive source is adapted for each drive wheel. In this case, the vehicle body can be turned by varying the differing directions of rotation of the drive wheels to enable steering without a separate steering device.At least some of the non-driven wheels can be set up as swivel casters, but this is just one example and not necessarily limited to this.

[0053] The sensor unit 112 can be used to detect the environment of the intelligent logistics vehicle 110 or a state of its own operation and may include at least one of the following elements: a 2D laser scanner (e.g. LiDAR), a 3D vision camera (stereo camera), a multi-axis gyroscope, an accelerometer, a wheel encoder and a proximity sensor.

[0054] The rotary encoder can output information to determine the wheel's angle of rotation by utilizing the light emitted by a light-emitting element (e.g., a photodiode). For example, the encoder can count the number of slots per unit of time arranged along the circumference of the wheel or the disk rotating with the wheel. The control unit 115 can perform odometry to estimate displacement by analyzing the magnitude of the change in position over time using data obtained from the rotary encoder and the gyroscope. However, a displacement estimated based on the rotary encoder data may exhibit an error compared to the actual displacement due to wheel slip or wear (a change in the dynamic wheel diameter).Therefore, when performing odometry, the control unit 115 can correct for noise and errors in the information collected by the wheel and gyro sensors using a predetermined algorithm (e.g., EKF: Extended Kalman Filter), thus outputting a result that tends to be close to the actual value. Such odometry can be particularly useful when it is impossible to determine the current location (localization) using a 2D laser scanner, which will be described later.

[0055] The 2D laser scanner can scan the environment by shining a laser beam into the surroundings via a rotating reflector and capturing the reflected signal. In this case, a detection result can be output in the form of a point cloud by analyzing the intensity of the reflected signal and the time difference between transmission and reception.

[0056] The 3D vision camera can calculate the distance to the object based on the parallax between two cameras that are separated by a predetermined distance, i.e., a pixel distance between the images captured by each camera. In this case, a texture projector can be used that projects infrared light with a predetermined pattern, allowing a flat object of the same color (e.g., a white wall) and the like to be detected.

[0057] In general, 2D laser scanners can be used for mapping, navigation, object recognition, etc., and 3D cameras can be used during navigation, especially to avoid obstacles, but this is only illustrative and is not necessarily limited.

[0058] The loading unit 113 can be a means of loading the object to be transported and can take the form of a top plate itself on the upper part of the vehicle body or a table arranged on the top plate, a lifting device, a turntable rotatable about a vertical axis, a forklift, a conveyor, or a combination thereof. In the case of a forklift, it can support telescopic and tilting functions similar to a forklift truck.

[0059] The communication unit 114 can communicate with other components within the operating boundary 100, such as the production device 120 and the control device 140, can support communication between the intelligent logistics vehicles 110 and can communicate with the charger when performing a charging task.

[0060] The control unit 115 can be the unit that takes over the overall control of each of the above-mentioned components 111, 112, 113, 114 and can execute a current task, current position, target determination, route planning, control of the loading unit and the like based on the information received from the control unit 140 via the communication unit 114.

[0061] Fig. Figure 4 is a perspective view showing an example of the external appearance of an intelligent logistics vehicle applicable to exemplary embodiments of the present disclosure.

[0062] With reference to the Fig. Figure 4 shows an example of an AMR as an intelligent logistics vehicle 110. The vehicle body can have a track-like planar shape with a longitudinal axis extending along the first axial direction. A drive wheel 111-1 can be arranged in the central section of the vehicle body in the first axial direction, can be arranged on one side in a second axial direction, and another drive wheel (not shown) can be arranged on the other side to be opposite a drive wheel 111-1 in the second axial direction. Such an arrangement of drive wheels can be called a differential drive (DD). Although in Fig. Not shown in Figure 4, two or more non-driven wheels can be arranged on a lower part of the vehicle body. In this case, it is possible to move forward or backward along the first axis direction if two drive wheels rotate at the same speed in the same direction, and it is possible to rotate on the basis of a rotation axis extending along a third axis direction and passing through a center point (C) of the vehicle body if these rotate at the same speed in opposite directions. Furthermore, the sensor unit 112 can be arranged on a front surface of the vehicle body, and the loading unit 113 can be arranged on an upper surface. The loading unit 113 can be configured to be lifted along the third axis direction, and a shelf, tray, or the like can be attached to the upper surface via a guide 113-1.

[0063] The AMR form described above of Fig. However, 4 is only illustrative, and it is obvious that the AGV may have a similar form to this or that the AMR may have a different form than this.

[0064] Next, a driving maneuver of the intelligent logistics vehicle 110 will be described with reference to the Fig. 5 described.

[0065] Fig. Figure 5 is a flowchart showing an example of a driving process of an intelligent logistics vehicle 110, applicable to exemplary embodiments of the present disclosure. Fig. For the sake of simplicity, it is assumed in paragraph 5 that the intelligent logistics vehicle 110 is an AMR capable of positioning itself and determining a local route.

[0066] With reference to the Fig. 5. The AMR can initially acquire a ground information grid map via a LiDAR or similar device while driving within the operating boundary 100 (S501).

[0067] When the AMR transmits the received grid map to the control device 140, a grid map processing and matching process can be performed in the map management unit 148 of the control device 140 (S502). This processing process can include a process for defining the aforementioned different zones on the grid map, a process for assigning costs to each grid cell, and so on. Here, the cost assignment can be such that the closer one gets to the obstacle or the entry exclusion zone, the higher the costs assigned, so that the AMR does not move around the obstacle or into the entry exclusion zone. The reason for this is that when determining the local route, the AMR selects the cells with the lowest costs among the waypoints as the route.

[0068] Furthermore, the map matching process can refer to a process for matching coordinates between the CAD map used in the design of the operational boundary 100, the ground information grid map (LiDAR map) and the topology map undergoing the processing process.

[0069] Afterwards, the control device 140 can share the topology map with all AMRs in the factory via the communication unit 146 (S503).

[0070] A subsequent step may be a process applied to a single AMR.

[0071] The AMR can determine its current location on the map using sensor data from sensor unit 112 and the map it has received (S504). For example, the AMR can determine its current location by comparing the surrounding terrain captured by the LiDAR with the map based on feature points.

[0072] The control device 140 can select a specific AMR to which a task can be assigned, and generally, one or more waypoints can be assigned to the task, determined by global path planning. The waypoint can be defined as coordinates on the map, and information about the direction (i.e., the course) in which the AMR is to be steered can be appended to the corresponding coordinates. According to the task assignment, a destination can be set in the AMR (Yes in S505), and the AMR can perform local path planning between waypoints based on the cost of the topology map (S506).

[0073] Once the route has been determined, the AMR can begin driving (S507), and if an obstacle is detected by the sensor unit 112 during the journey (Yes in S508), an evasive maneuver can be performed by conducting local pathfinding to bypass the detected obstacle (S509). In some cases, the control device 140 can update the task of the corresponding AMR according to the evasive maneuver or according to the failure of the evasive maneuver.

[0074] Furthermore, the AMR can correct the position error during the journey until the destination (S510) is reached using the odometry technique mentioned above.

[0075] Upon reaching the destination (S511), the AMR can then perform a task-based maneuver (S512). For example, the AMR can determine whether a condition for entering a specific process area is met, pick up an empty pallet from the destination, or unload a load mounted on the loading unit 113.

[0076] An exemplary embodiment of the present disclosure may aim to provide information about the route according to the task performance of the intelligent logistics vehicle, so that the routes of different intelligent logistics vehicles with different tasks to be performed do not cross or overlap.

[0077] In the following, a control device according to an exemplary embodiment of the present disclosure is described with reference to the Fig. 6 described.

[0078] Fig. Figure 6 is a view showing an operating boundary equipped with a control device according to an exemplary embodiment of the present disclosure.

[0079] With reference to the Fig. 6. According to an exemplary embodiment of the present disclosure, the control device 140 may include a map management unit 148 for providing map information in which at least some virtual paths within an operating boundary 100 are assigned different route-selection-based scores for each task situation, and a route selection unit 149 for identifying the task situation of the intelligent logistics vehicle 110, for determining the route based on the map information provided by the map management unit 148, so that the intelligent logistics vehicle 110 can move along the virtual path corresponding to the determined task situation based on the route-selection-based score corresponding to the determined task situation.and for transmitting the route information corresponding to the determined route to the intelligent logistics vehicle 110. In addition, the control device 140 may also have a communication unit 146 for collecting external information or for transmitting information generated within the control device 140 to the outside.

[0080] In the Fig. For the sake of simplicity, a control device 140 and an intelligent logistics vehicle 110 are described in section 6; however, it is understood that this applies equally if a plurality of intelligent logistics vehicles or a plurality of control units are provided.

[0081] The individual components contained in the control device 140 are described in detail below.

[0082] The virtual path along which an intelligent logistics vehicle 110 can move while performing a task can be defined within the operational boundary 100. Likewise, the map management unit 148 can assign the route selection-based score to at least some virtual paths among the majority of virtual paths defined within the operational boundary 100. The route selection-based score can refer to costs allocated to each unit area or unit cell within the operational boundary for each task situation.

[0083] As above in the Fig. As described in section 5, costs can conventionally be assigned to the virtual path, and the intelligent logistics vehicle 110 can move along a virtual path with a high cost and a virtual path with a low cost. In this case, if a route is formed based on such a virtual path, where only the possibility of movement or direction is determined by the assigned costs for the virtual path, there may be points or areas where the routes intersect or overlap. If the intelligent logistics vehicle 110 moves along such a route, a collision may occur between the intelligent logistics vehicles 110 at the intersecting or overlapping points or areas, or an evasive maneuver may be necessary to prevent the collision.

[0084] To solve this problem, the map management unit 148, according to an exemplary embodiment of the present disclosure, can provide map information in which at least some of the virtual lanes within the operating boundary 100 are assigned different route-selection-based scores for each task situation. In this case, the task situation of the intelligent logistics vehicle 110 can include at least one piece of information about a task to be performed by the intelligent logistics vehicle 110, information about a loading state of the intelligent logistics vehicle 110, and information about a type of intelligent logistics vehicle 110.The information about the task to be performed can include at least one supply task, one collection task, and one loading task to be performed by the intelligent logistics vehicle 110. The information about the loading status of the intelligent logistics vehicle 110 can include at least one piece of information about whether the intelligent logistics vehicle 110 is loaded with a cargo product and information about the product loaded onto the intelligent logistics vehicle 110. The information about the type of intelligent logistics vehicle 110 can include information about its intended use (e.g., performing a logistics task, performing a parking task, performing a cleaning task, etc.).

[0085] This means that the map management unit 148 can provide map information that differentiates virtual paths, which are configurable for each task situation of the intelligent logistics vehicle 110, with respect to at least some virtual paths. For example, the map management unit 148 can assign a low route selection-based score to a supply task and a high route selection-based score to a pickup task with respect to a virtual path, thereby providing map information so that the corresponding virtual path can only be used for one supply task. For this purpose, the route selection-based score can be assigned as a fixed value for each virtual path or it can be assigned as a value that changes periodically according to the task situation.

[0086] However, this is only illustrative and not necessarily limited to this. For example, the map management unit 148 can provide preset map information by presetting map information in which at least some virtual lanes within the operational boundary 100 are assigned different route-selection-based scores for each task situation, and the map management unit 148 can generate map information in which at least some virtual lanes within the operational boundary 100 are assigned different route-selection-based scores for each task situation, and provide the generated map information.

[0087] The route selection unit 149 can identify the task situation of the intelligent logistics vehicle 110 and determine the route of the intelligent logistics vehicle 110 based on the task situation, which was identified on the basis of the map information provided by the map management unit 148.

[0088] In particular, the route selection unit 149 can acquire process information from the operating boundary 100. This process information can include at least one of the process state information and logistics request information from the operating boundary 100, and the route selection unit 149 can acquire the process information provided by the production device 120 located at the operating boundary 100. In this case, the control device 140 can acquire the process messages provided by the production device 120 via the communication unit 146 and provide the process messages acquired by the communication unit 146 to the route selection unit 149, so that the route selection unit 149 can collect the provided process messages again. However, this is only illustrative and is not necessarily limited to this.

[0089] The route selection unit 149 can also determine a task situation for the intelligent logistics vehicle 110 based on the collected process information and identify the specific task situation. For example, the route selection unit 149 can determine a task situation such as a supply task and a collection task to be carried out by the intelligent logistics vehicle 110 based on the collected process information.

[0090] The route selection unit 149 can identify the task situation and determine the route so that the intelligent logistics vehicle 110 moves along the virtual path corresponding to the identified task situation, based on the route selection-based score corresponding to the identified task situation, which was determined based on the map information provided by the map management unit 148. The route determination is carried out with reference to the Fig. 7, Fig. 8, Fig. 9 to Fig. 10 described.

[0091] The Fig. 7, Fig. 8, Fig. 9 to Fig. Figure 10 shows the determination of a route for an intelligent logistics vehicle according to an exemplary embodiment of the present disclosure.

[0092] First, with reference to the Fig. 7. Assume that any two process zones, i.e., Process A and Process B, are formed within the operating boundary 100. Likewise, a plurality of virtual lanes (L1, L2) connecting the two processes can be formed between Process A and Process B. The map management unit 148 can provide map information, assigning each of the plurality of virtual lanes (L1, L2) a route-selection-based score for each task situation. For example, a first virtual lane (L1) can be assigned a route-selection-based score of 3 for a supply task, a route-selection-based score of 6 for a pickup task, and a route-selection-based score of 5 for a simple movement task.The second virtual lane (L2) can also be assigned the route selection-based score 5 for the supply task, the route selection-based score 2 for the pick-up task, and the route selection-based score 6 for the simple movement task.

[0093] If, based on the collected process information, a material supply from process A to process B is required, the route selection unit 149 can determine and identify the task situation so that the intelligent logistics vehicle 110 can perform the supply task from process A to process B. The route selection unit 149 can also determine the route to travel along the virtual path corresponding to the task situation identified based on the route selection-based score, which in turn was identified based on map information provided by the map management unit 148.If the task situation is, for example, a supply task from process A to process B, the route selection unit 149 can determine the route selection-based score assigned to the supply task for each of the multiple virtual lanes (L1, L2) based on map information. Previously, the route selection-based score for the supply task assigned to the first virtual lane (L1) might be 3, and the route selection-based score for the supply task assigned to the second virtual lane (L2) might be 5. The route selection unit 149 can then determine the route so that the intelligent logistics vehicle 110 travels along the first virtual lane (L1) with the lower score, based on the two route selection-based scores.

[0094] The route selection unit 149 can also select a starting point and a destination according to the identified task situation within the operating boundary 100 and determine the route connecting the selected starting point and the selected destination based on the map information.

[0095] As in Fig. As shown in Figure 8, a plurality of virtual paths can be formed within the operational boundary 100, and the route selection-based score for each task situation, in particular a supply task and a recovery task, can be assigned by the map management unit 148 to each of the plurality of virtual paths. For example, a supply virtual path may refer to a virtual path to which the route selection-based score is assigned a low value according to a supply task, and a recovery virtual path may refer to a virtual path to which the route selection-based score is assigned a low value according to a recovery task. However, this is merely illustrative and is not necessarily limited to such examples. The map management unit 148 can provide the map information thus determined to the route selection unit 149.

[0096] Also with reference to the Fig. 8. The route selection unit 149 can select a starting point and a destination according to the identified task situation within the operating boundary 100. For example, a plurality of processes (process A, process B, and process C) can be configured within the operating boundary 100, and if the identified task situation is a parts supply order based on a parts order from process C, the route selection unit 149 can select one of the plurality of supply interfaces (SP1, SP2, SP3) provided within the operating boundary 100 as the starting point (e.g., SP2) and select process C as the destination.

[0097] Once the origin and destination are selected, the route selection unit 149 can determine the route connecting the origin and destination along the virtual path corresponding to the task situation identified based on map information, based on the route selection-based score associated with that situation. For example, the route selection unit 149 can determine the route connecting the supply interface (SP2) as the origin and process C as the destination along a virtual path for supply purposes, which has a low route selection-based score assigned to the parts delivery task of a first intelligent logistics vehicle (110-1) performing the parts delivery task.

[0098] If, however, the identified task situation is a pickup task resulting from a request to collect empty pallets from process C, the route selection unit 149 can select process C as the starting point and a pickup interface (e.g., CP3) from a plurality of pickup interfaces (CP1, CP2, CP3) provided in the operational boundary 100 as the destination. Once a starting point and a destination are selected, the route selection unit 149 can determine the route connecting process C as the starting point and the pickup interface (CP3) as the destination for a second intelligent logistics vehicle 110-2 to perform the pickup task by moving along a virtual pickup path that is assigned a low route selection-based score for the pickup task, which is a task situation identified based on map information.

[0099] Furthermore, the route selection unit 149 can determine the route connecting the origin and destination by additionally considering the status information of the selected origin and destination. For example, the route selection unit 149 can determine the route connecting the origin and destination by additionally considering the status information of each of the multiple supply interfaces (SP1, SP2, SP3), the status information of each of the multiple pickup interfaces (CP1, CP2, CP3), and the status information of each of the multiple processes (Process A, Process B, and Process C).

[0100] In this way, it is possible to prevent the route of the first intelligent logistics vehicle 110-1, which performs the supply task, and the route of the second intelligent logistics vehicle 110-2, which performs the pickup task, from overlapping or crossing when the first intelligent logistics vehicle 110-1, which performs the supply task, and the second intelligent logistics vehicle 110-2, which performs the pickup task, exist side by side, by determining the route according to the task situation to be performed by the intelligent logistics vehicle 110.

[0101] In addition, the route selection unit 149 can determine the route based on map information, which further takes into account information about whether at least some virtual paths are activated for each task situation.

[0102] With reference to the Fig. 9. The plurality of virtual paths can be formed around any process zone (e.g., process B) that exists within the operational boundary 100, and each of the plurality of virtual paths can be assigned by the map management unit 148 the route selection-based score according to a task situation, in particular a supply task and a pickup task. Fig. 9 It is assumed that the majority of virtual pathways formed around process B according to an exemplary embodiment are virtual pathways for pickup purposes, to which a route selection-based score corresponding to a pickup task is assigned a lower priority than a route selection-based score corresponding to a supply task.

[0103] As described above, the map management unit 148 can provide map information in which at least some virtual lanes within the operational boundary 100 are assigned different route selection-based scores according to the task situations. In this case, it can be specified for at least some virtual lanes whether they should be activated for each task situation to shorten a travel route or travel time. The map management unit 148 can update the map information so that information about whether to activate for each task situation is still reflected in the existing map information for at least some virtual lanes, and can provide the updated map information. Likewise, the route selection unit 149 can determine the travel route for each task situation based on the updated map information.

[0104] For example, if information about the activation of a virtual lane (L3) is included in the initial map information provided by the map management unit 148, the route selection unit 149 can determine an initial route that has the activated virtual lane (L3) in order to move on the virtual lane corresponding to the task situation based on the route selection-based score corresponding to the task situation, where this is a pickup task of the intelligent logistics vehicle 110 from process B to the pickup interface (CP2) based on the initial map information.The intelligent logistics vehicle 110 can be driven based on the initial route determined by the route selection unit 149, but to reduce task completion time, the map management unit 148 can further gather information on whether another unused virtual lane corresponding to the identified task situation is activated. For example, the map management unit 148 can update the map information so that information about the virtual lane (L4) that can be temporarily activated corresponding to the pickup task identified by the route selection unit 149 can be further considered, and can provide the updated map information to the route selection unit 149.

[0105] Route Selection Unit 149 can determine the route, which differs from the initial route from Process B to the Pickup Interface (CP2), based on map information updated with details of the temporary activation of virtual lane (L4). Route Selection Unit 149 can also determine a route that is shorter than the initial route, based on map information that further considers whether at least some virtual lanes should be activated. However, as described above, the activation of virtual lanes is intended to shorten the route or travel time, but this is illustrative and not necessarily limited to this.For example, it can be determined whether the virtual path should be activated to avoid overlaps or crossings when determining the route, taking into account the task situation, or whether activation is determined according to the question of whether driving is possible by determining the current state of the virtual path based on the process information provided by the production device 120.

[0106] However, with reference to the Fig. 10, if two adjacent virtual paths (L5, L6) exist that connect process A and process B, which are defined in the operating boundary 100, the route selection unit 149 can be applied based on the route selection-based score that corresponds to the task situation for the two adjacent virtual paths. For example, for the task situation of traveling back and forth between process A and process B, the route selection unit 149 can preferably determine the route that includes the virtual path (L5) while corresponding to the task situation in which process A is the starting point and process B is the destination. Likewise, the route selection unit 149 can determine the route including the virtual path (L5) that corresponds to the task situation in which process B is the starting point and process A is the destination.In this case, however, it can be cumbersome to change the route selection-based score assigned to virtual path (L5) in order to include virtual path (L5) in the route again. Therefore, if adjacent virtual paths (L5, L6) exist that connect the two processes (Process A and Process B), the map management unit 148 can apply the route selection-based score corresponding to the task situation to the two adjacent virtual paths. For this reason, when determining the route, the route selection unit 149 can only include virtual path (L5) as the route when moving from Process A to Process B, and can only include virtual path (L6) as the route when moving from Process B to Process A.This means that the map management unit 148 can assign the route selection-based score at least for some virtual paths for each task situation of the intelligent logistics vehicle 110, on the basis of which the route selection unit 149 can determine the route in such a way that the virtual paths are distinguished for each task situation of the intelligent logistics vehicle 110.

[0107] Back on Fig. 6. Referring to section 6, when the route is determined by the route selection unit 149, it can transmit the route information corresponding to the determined route to the intelligent logistics vehicle 110, which is executing the identified task. Specifically, based on the determined route, the route selection unit 149 can select a plurality of waypoints corresponding to the route and transmit the route information, which includes information about the plurality of selected waypoints, to the intelligent logistics vehicle 110. In this case, the route selection unit 149 can transmit the route information to the communication unit 146 and, via the communication unit 146, transmit it to the intelligent logistics vehicle 110.The intelligent logistics vehicle 110, which executes the task situation identified by the route selection unit 149, can receive the route information corresponding to the route determined by the route selection unit 149 via the communication unit 114.

[0108] By transmitting the route information, which includes information about the majority of waypoints, to the intelligent logistics vehicle 110, the route selection unit 149 can determine the route selection-based score assigned to at least one virtual path formed between the waypoints based on the route information and can move between the waypoints. This is done with reference to the Fig. 11 described in detail.

[0109] Fig. Figure 11 is a view showing an intelligent logistics vehicle driving on the basis of route information according to an exemplary embodiment of the present disclosure.

[0110] With reference to the Fig. The route selection unit 149 can select the plurality of waypoints (WP1, WP2, WP3, WP4) that correspond to the determined route, and the plurality of waypoints (WP1, WP2, WP3, WP4) can be located on any virtual path. However, this is only illustrative, and while it may be shown that waypoints lie on a virtual path, it is not necessarily limited to doing so. For example, the plurality of waypoints (WP1, WP2, WP3, WP4) can be located on the plurality of virtual paths.

[0111] The route selection unit 149 can transmit route information, including information about the majority of selected waypoints, to the intelligent logistics vehicle 110. The intelligent logistics vehicle 110 can then navigate according to a given task based on this route information. While navigating, the intelligent logistics vehicle 110 can simultaneously update the status of the currently traversed virtual path in real time via the separately equipped sensor unit 112.Accordingly, even if the route selection unit 149 provides the intelligent logistics vehicle 110 with a global route connecting multiple waypoints (WP1, WP2, WP3, WP4) as a driving route, the intelligent logistics vehicle 110 can establish a local route in the area between the waypoints instead of the global route by real-time data acquisition by the acquisition unit 112. If at least one virtual path is established between the multiple waypoints (WP1, WP2, WP3, WP4), the intelligent logistics vehicle 110 can then directly determine the route selection-based score assigned to at least one virtual path and move between the waypoints along the virtual path with the route selection-based score that corresponds to the current task situation.

[0112] The following describes a control method for an intelligent logistics vehicle according to an exemplary embodiment of the present disclosure with reference to the Fig. 12 based on the configuration of the in Fig. Control device 140 described in section 6.

[0113] Fig. Figure 12 is a view showing a control method of an intelligent logistics vehicle according to an exemplary embodiment of the present disclosure.

[0114] With reference to the Fig. 12. The map management unit 148 can generate map information, assigning different route selection-based scores to at least some virtual paths within the operating boundary 100 for each task situation (S1210). Likewise, the map management unit 148 can provide the generated map information to the route selection unit 149 (S1220). However, step S1210 can be omitted according to exemplary embodiments, and if omitted, the map management unit 148 can provide the route selection unit 149 with the preset map information.

[0115] Similarly, the route selection unit 149 can receive and collect process information about the operating boundary 100 from the production device 120 (S1230). Based on the collected process information, the route selection unit 149 can identify the task situation of the intelligent logistics vehicle 110 (S1240) and can determine the route to move along the virtual path corresponding to the identified task situation, based on the route selection-based score corresponding to the identified task situation, which was identified based on the map information provided by the map management unit 148 (S1250). The route determination process is carried out with reference to the Fig. Sections 6 and 7 to 10 are described in detail, so further explanation is unnecessary.

[0116] The route selection unit 149 can generate route information corresponding to the determined route and transmit the generated route information to the communication unit 114 of the intelligent logistics vehicle 110 (S1260-1). The communication unit 114 of the intelligent logistics vehicle 110 can transmit the route information received from the route selection unit 149 to the control unit 115 (S1260-2), and the control unit 115 can control the driving of the intelligent logistics vehicle 110 according to the task situation based on the received route information (S1270).

[0117] As described above, a control method of an intelligent logistics vehicle and a control device of the present disclosure can determine routes such that an intelligent logistics vehicle moves along a virtual path corresponding to the determined task situation based on a route selection-based score corresponding to the determined task situation, thereby preventing routes from crossing or overlapping and improving the mobility of the intelligent logistics vehicle.

[0118] Furthermore, it is possible to easily identify the logistics flow within an operational boundary by determining the route for each task situation of the intelligent logistics vehicle.

[0119] Although this has been shown and described with reference to a specific exemplary embodiment of the present disclosure, it will be obvious to the person skilled in the art that the present disclosure can be modified and amended in various ways without deviating from the technical idea of ​​the present disclosure provided by the following claims.

[0120] The disclosure described above can be implemented as computer-readable code on a medium on which a program is recorded. The computer-readable medium can comprise any type of recording device on which data readable by a computer system is stored. Examples of computer-readable media include hard disk drives (HDDs), solid-state drives (SSDs), silicon hard disk drives (SDDs), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, and the like. Therefore, the foregoing detailed description should not be interpreted in every respect but should be considered illustrative. The scope of this disclosure should be determined by a proper interpretation of the accompanying claims, and any modifications within the equivalent scope of this disclosure may be included within its scope. Description of the reference symbols 100 operating limit 110 intelligent logistics vehicle 120 production equipment 130 Monitoring device 140 Control device

Claims

A control method for an intelligent logistics vehicle (110; 110-1, 110-2), wherein the method comprises: identifying (S1240) a task situation of an intelligent logistics vehicle (110; 110-1, 110-2); determining (S1250) a route based on map information, wherein at least some virtual paths (L1, L2, L3, L4, L5, L6) within an operating boundary (100) are assigned different route selection-based scores for each task situation, such that the intelligent logistics vehicle (110; 110-1, 110-2) moves along the virtual path (L1, L2, L3, L4, L5, L6) corresponding to the determined task situation based on the route selection-based score corresponding to the determined task situation; and transfer (S1260-1) route information corresponding to the determined route to the intelligent logistics vehicle (110; 110-1, 110-2). The method according to claim 1, wherein the task situation comprises at least one piece of information about a task to be performed by the intelligent logistics vehicle (110; 110-1, 110-2), about a loading state of the intelligent logistics vehicle (110; 110-1, 110-2) and about a type of intelligent logistics vehicle (110; 110-1, 110-2). The method according to claim 2, wherein the information on the loading status includes at least information on whether the intelligent logistics vehicle (110; 110-1, 110-2) is loaded and on the goods loaded on the intelligent logistics vehicle (110; 110-1, 110-2). The method according to claim 1, wherein the route selection-based score relates to costs allocated to each unit region or unit cell within the operating boundary (100) for each task situation. The method according to claim 1, wherein the identification comprises: collecting process information of the operating boundary (100) and identifying the task situation of the intelligent logistics vehicle (110; 110-1, 110-2), which is determined on the basis of the collected process information. The method according to claim 5, wherein the process information includes at least one of process state information and logistics request information of the operating boundary (100). The method according to claim 1, wherein the determining (S1250) comprises: selecting a departure point and a destination within the operating boundary (100) corresponding to the identified task situation and determining the route connecting the selected starting point and the destination, based on the map information. The method according to claim 7, wherein the determination (S1250) of the route connecting the starting point and the destination is determined taking into account state information of each of the selected starting points and destinations. The method according to claim 1, wherein the determining (S1250) comprises determining the route based on the map information, which furthermore reflects information on whether at least some of the virtual paths (L1, L2, L3, L4, L5, L6) are activated in the map information for each task situation. The method according to claim 1, wherein the transmission (S1260-1) comprises: selecting a plurality of waypoints (WP1, WP2, WP3, WP4) corresponding to the route, based on the determined route; and transmitting the route information, which includes information about the plurality of selected waypoints (WP1, WP2, WP3, WP4), to the intelligent logistics vehicle (110; 110-1, 110-2). The method according to claim 10, wherein the transmission (S1260-1) comprises the transmission of the route information, which includes information about the plurality of waypoints (WP1, WP2, WP3, WP4), to the intelligent logistics vehicle (110; 110-1, 110-2), such that the intelligent logistics vehicle (110; 110-1, 110-2) determines the route selection-based score, which is assigned to at least one virtual path (L1, L2, L3, L4, L5, L6) formed between the waypoints (WP1, WP2, WP3, WP4) on the basis of the route information, and moves between the waypoints (WP1, WP2, WP3, WP4). A control device (140), the device comprising: a map management unit (148) for providing map information in which different route selection-based scores are assigned to at least some virtual lanes (L1, L2, L3, L4, L5, L6) within an operating boundary (100) for each task situation; and a route selection unit (149) for identifying the task situation of an intelligent logistics vehicle (110; 110-1, 110-2), for determining a route based on the map information provided by the map management unit (148), so that the intelligent logistics vehicle (110;110-1, 110-2) along the virtual path (L1, L2, L3, L4, L5, L6) corresponding to the identified task situation, based on the route selection-based score corresponding to the identified task situation, and to transmit route information corresponding to the determined route to the intelligent logistics vehicle (110; 110-1, 110-2).; The device according to claim 12, wherein the route selection unit (149) collects process information of the operating boundary (100) and identifies the task situation of the intelligent logistics vehicle (110; 110-1, 110-2) which was determined on the basis of the collected process information. The device according to claim 12, wherein the route selection unit (149) selects a starting point and a destination within the operating boundary (100) corresponding to the identified task situation and determines the route connecting the selected starting point and the destination on the basis of the map information. The device according to claim 14, wherein the route selection unit (149) determines the route connecting the starting point and the destination, taking into further consideration state information of each selected starting point and destination. The device according to claim 12, wherein the route selection unit (149) determines the driving route on the basis of the map information, which additionally reflects information on whether at least some of the virtual paths (L1, L2, L3, L4, L5, L6) are activated in the map information for each task situation. The device according to claim 12, wherein the route selection unit (149) selects a plurality of waypoints (WP1, WP2, WP3, WP4) corresponding to the route based on the determined route, and transmits the route information, which includes information about the plurality of selected waypoints (WP1, WP2, WP3, WP4), to the intelligent logistics vehicle (110; 110-1, 110-2). The device according to claim 17, wherein the route selection unit (149) transmits the route information, which includes information about the plurality of waypoints (WP1, WP2, WP3, WP4), to the intelligent logistics vehicle (110; 110-1, 110-2), so that the intelligent logistics vehicle (110; 110-1, 110-2) determines the route selection-based score that is assigned to at least one virtual path (L1, L2, L3, L4, L5, L6) formed between the waypoints (WP1, WP2, WP3, WP4), based on the route information, and moves between the waypoints (WP1, WP2, WP3, WP4).

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