Semi-centralized robotic frit management system and management method

Abstract

The present invention discloses a semi-centralized robot fritt management system and management method. A semi-centralized robot fritt management system according to one embodiment of the present invention includes a task assignment module that assigns tasks to each of a plurality of robots, a path planning module that calculates a travel path that allows each of the robots to move to a destination in order to complete the assigned task without colliding with each other, a traffic control module that generates traffic signals to control the entry order and direction of movement of at least one of the robots at a waypoint node at an intersection, and a communication interface to the robots for the travel path and the traffic signals. The semi-centralized robot fritt management system and management method of the present invention can optimize the control of multiple robots by utilizing the advantages of both centralized and distributed control systems and minimizing their disadvantages.

Description

Technical Field

[0001] The present invention relates to robot control, and more particularly, to a semi-centralized robot fleet management system and a management method capable of efficiently managing a plurality of autonomous mobile robots (AMRs).

Background Art

[0002] In recent years, the logistics industry has seen an increasing demand for logistics automation technologies that can quickly and efficiently process a large amount of goods, particularly the use of logistics transfer robots. Logistics sites are trying to solve the problems of high labor costs and labor shortages by introducing logistics robots, and accordingly, the requirements for logistics robots that enable automated logistics operations have increased rapidly. Most sites using logistics robots in South Korea operate a small number of mobile robots (less than 10), but recently, large logistics warehouses and manufacturing sites are trying to operate many robots (more than 50). For such large-scale robot operations, a fleet management system (FMS) must provide a multi-robot control function so that robots can move smoothly without colliding with each other. The centralized control and decentralized control methods in a multi-robot control system provide different accesses in the operation and adjustment methods of robots. Centralized control is performed by a single central control device or system for all decisions and controls, and is mainly used when moving along a specified path, such as an automated guided vehicle (AGV). In this method, the central control system collects the status information of all robots, makes an optimal decision for achieving the overall system goal, and gives specific instructions to each robot. The advantage of centralized control is that the entire system can be managed and optimized in an integrated manner. However, because all information and control commands are processed centrally, as the complexity of the system increases, processing delays and communication bottlenecks can occur. In the case of dynamic obstacles, the system must either wait until the obstacle is removed or specify an alternative path centrally. On the other hand, while autonomous mobile robots (AMRs) are capable of autonomous avoidance and can easily evade obstacles, centralized control systems make it difficult to utilize these functions and respond immediately to dynamic obstacles. The distributed control system is a structure in which each robot makes its own decisions and acts independently, reducing its dependence on a central control unit. In this system, each robot understands its surrounding environment and the state of other robots, makes independent judgments, and acts autonomously. The advantages of distributed control are its superior system flexibility and scalability, and its ability to avoid bottlenecks and single-point failures in centralized systems. It is particularly suitable for autonomous mobile robots (AMRs) with autonomous avoidance capabilities and can be used effectively even in environments with people and other dynamic obstacles. However, because each robot makes independent decisions and acts accordingly, overall optimization for the goals of the entire system becomes difficult, and there is a possibility of collisions or stalemates between robots. Stalemates are particularly likely to occur frequently in spaces with many narrow passages, such as logistics and manufacturing sites, and there are limitations to the use of distributed control systems. Therefore, there is a need for a system that leverages the advantages of both centralized and distributed control systems while minimizing their disadvantages to achieve optimal multi-robot control performance. [Overview of the project] [Problems that the invention aims to solve]

[0003] The present invention provides a hybrid, semi-centralized robot fleet management system and management method that appropriately combines a centralized system and a distributed control system for the efficient operation of multiple autonomous mobile robots (AMRs). The problems that the present invention addresses are not limited to those mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0004] A semi-centralized robotic frit management system according to one embodiment of the present invention includes: a task assignment module that assigns tasks to each of a plurality of robots; a route planning module that calculates a travel path that allows each of the robots to move to a destination in order to complete the assigned task without colliding with each other; a traffic control module that generates traffic signals to control the entry order and direction of movement of at least one of the robots at a waypoint node at an intersection; and an interface for communicating the travel path and the traffic signals to the robots. Preferably, each of the robots that receives the movement path can determine its position via its attached sensors, recognize obstacles, and move autonomously. Preferably, the area along the travel path where the robot can move autonomously is an autonomous driving area with two or more lanes, and the autonomous driving area does not include the waypoint nodes. Preferably, within the autonomous driving area, the robot is capable of autonomous movement in accordance with a pre-set right-hand traffic rule. Preferably, the traffic signal includes information regarding the entry sequence and direction of movement of at least one of the robots in a single-lane passage. Preferably, the traffic control module stores the movement sequence of at least one robot at each waypoint node in a dependency graph, where each of the multiple waypoint nodes that the at least one robot must traverse over time is represented as a vertex, and the movement conditions between robots for the at least one waypoint node represented by the vertex are represented as edges. Preferably, the calculation of the travel path by the path planning module is performed using multi-agent pathfinding (MAPF), and the objective function of the multi-agent pathfinding (MAPF) is the sum of the travel distance / time for each robot along the path (sum of costs) or the maximum required time / distance for each robot (makespan).

[0005] A semi-centralized robot frit management method according to one embodiment of the present invention, comprising: a task assignment step of assigning tasks to each of a plurality of robots; a path planning step of calculating a travel path that allows each of the robots to move to a destination in order to complete the assigned task without colliding with each other; a traffic control step of generating traffic signals to control the entry order and direction of movement of at least one of the robots at a waypoint node of an intersection; and This includes a communication step in which the aforementioned travel route and traffic signals are communicated to the robot. Preferably, each of the robots that receives the movement path can determine its position via its attached sensors, recognize obstacles, and move autonomously. Preferably, the area along the travel path where the robot can move autonomously is an autonomous driving area with two or more lanes, and the autonomous driving area does not include the waypoint nodes. Preferably, within the autonomous driving area, the robot is capable of autonomous movement in accordance with a pre-set right-hand traffic rule. Preferably, the traffic signal includes information regarding the entry sequence and direction of movement of at least one of the robots in a single-lane passage. Preferably, the traffic control step includes generating a dependency graph in which the movement sequence of at least one robot at each waypoint node is represented, wherein each of the plurality of waypoint nodes that the at least one robot must traverse over time is represented as a vertex, and the movement conditions between robots for the at least one waypoint node represented by the vertex are represented as edges. Preferably, the path planning step includes a step of calculating a travel path using multi-agent pathfinding (MAPF), wherein the objective function of the multi-agent pathfinding (MAPF) is the sum of the travel distance / time for each robot along the path (sum of costs) or the maximum required time / distance for each robot. Specific details of other embodiments are included in the detailed description and drawings. [Effects of the Invention]

[0006] The semi-centralized robot frit management system and management method of the present invention can optimize the control of multiple robots by utilizing the advantages of both centralized and distributed control systems while minimizing their disadvantages. The semi-centralized robot frit management system and management method of the present invention prevent collisions and stalemates by ensuring that the robot moves along the optimal path while keeping the system complexity low. The semi-centralized robot Flit management system and management method of the present invention are applicable to a wide variety of robots, including automated guided vehicles (AGVs) and autonomous mobile robots (AMRs), and can also be applied to third-party robots with restricted access to their internal application programming interface (API). Therefore, operation is possible simply by specifying the next waypoint node. The semi-centralized robotic frit management system and management method of the present invention assists robots in effectively avoiding obstacles even in environments with people or other dynamic obstacles, enabling robots to immediately avoid obstacles and perform tasks. However, the effects of the present invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description. [Brief explanation of the drawing]

[0007] [Figure 1]Figure 1 is a schematic diagram of a semi-centralized robot frit management system according to one embodiment of the present invention. [Figure 2] Figure 2 shows an exemplary map used for operating a robot in a semi-centralized robot frit management system according to one embodiment of the present invention. [Figure 3] Figure 3 shows an example of a dependency graph that represents the prerequisites that a robot must check in order to move to each waypoint node, using a trunk line. [Figure 4] Figure 4 shows the input and output of the multiple agent pathfinding (MAPF) of the path planning module of the present invention. [Figure 5] Figure 5 is a table summarizing the features of the semi-centralized control system of the present invention in comparison with conventional centralized and distributed control systems. [Figure 6] Figure 6 is a flowchart showing a semi-centralized robot frit management method according to one embodiment of the present invention. [Figure 7] Figure 7 shows an exemplary computing device that can embody various embodiments of the present invention, including devices and / or systems. [Modes for carrying out the invention]

[0008] The advantages and features of the present invention, and methods for achieving them, will become clearer with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and can be embodied in a variety of different forms, and these embodiments are provided merely to complete the disclosure of the present invention and to fully inform those who are ordinary skill in the art to which the invention pertains, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. The embodiments described herein are described with reference to cross-sectional views and / or plan views that are ideal exemplary diagrams of the present invention. In the drawings, the thickness of the configuration is exaggerated for an effective explanation of the technical content. Therefore, the configurations illustrated in the drawings have schematic attributes, and the forms of the configurations illustrated in the drawings are for exemplifying specific forms of the configurations and are not for limiting the scope of the invention. In various embodiments of the present specification, terms such as first, second, third, etc. are used to describe various components, but these components should not be limited by such terms. These terms are merely used to distinguish one component from another. The embodiments described and exemplified herein also include complementary embodiments thereof. The terms used herein are for explaining the embodiments and are not intended to limit the present invention. In this specification, the singular form includes the plural form as well, unless otherwise specified in the context. The "comprises" and / or "comprising" used in the specification do not exclude the presence or addition of one or more other components, steps, operations, and / or elements to the recited components, steps, operations, and / or elements. Unless otherwise defined, all terms (including technical and scientific terms) used herein can be used in a meaning commonly understood by those having ordinary knowledge in the technical field to which the present invention pertains. Also, terms defined in commonly used dictionaries are not ideally or excessively interpreted unless specifically defined otherwise.

[0009] Hereinafter, with reference to the drawings, the concept of the present invention and embodiments thereof will be described in detail. FIG. 1 is a diagram showing a schematic view of a semi-centralized robot frit management system according to an embodiment of the present invention. The semi-centralized robot frit management system 100 of the present invention presents a semi-centralized control method in a hybrid form that combines a central centralized system and a distributed control method for the efficient operation of multiple robots. The semi-centralized robot fleet management system 100 of the present invention makes use of the advantages of both the central centralized and distributed control systems, minimizes the disadvantages, and achieves optimal multi-robot control performance. The semi-centralized robot fleet management system 100 of the present invention provides an optimal movement path while reducing the complexity of the system, preventing collisions and jams between robots. In particular, it is compatible not only with automated guided vehicles (AGVs) and autonomous mobile robots (AMRs), but also with various types of robots, and can be effectively used in environments where people and other dynamic obstacles exist. The semi-centralized robot fleet management system 100 of the present invention presents a new form of robot control method that provides an optimal path for robots and enables autonomous avoidance activation of robots in order to efficiently manage multiple autonomous mobile robots AMRs. The semi-centralized robot fleet management system 100 according to an embodiment of the present invention includes a work assignment module 110 that assigns tasks to each of a plurality of robots, a path planning module 120 that calculates a movement path that each robot can move to a destination in order to complete the assigned tasks without colliding with each other, a traffic control module 130 that generates a traffic signal for controlling the entry order and movement direction of at least one robot at a waypoint node of an intersection, and a traffic signal for the movement path and the traffic signal includes a robot.

[0010] The work assignment module 110 of the semi-centralized robot fleet management system 100 of the present invention calculates and stores the optimal work execution order from the work list given to the robot 150. When a new job is added, the job assignment module 110 batches the added job and recalculates the optimal order. When the robot 150 completes the assigned task, it requests the next new task from the semi-centralized robot fleet management system 100, and the semi-centralized robot fleet management system 100 assigns the next task to the robot 150. The path planning module 120 of the semi-centralized robot frit management system 100 of the present invention uses a multi-agent pathfinding (MAPF) algorithm to calculate the optimal path that allows all robots to move to their destination without colliding with each other. When the route planning module 120 receives the next work request from the robot 150, it specifies the next destination for the robot 150, calculates a new route for all robots, and stores it in the route table. The traffic control module 130 of the semi-centralized robot traffic management system 100 of the present invention stores the robot's movement sequence in a dependency graph format when it passes through specific waypoint nodes based on a route table. As shown in Figure 3, the traffic control module 130 extracts the necessary preconditions for passing through each waypoint node on the planned route, displays them in a dependency graph, and controls traffic using the dependency graph. In the present invention, the robot, as a mobile robot 150, includes automated guided vehicles (AGVs), autonomous mobile robots (AMRs), and automated guided forklifts (AGFs). The mobile robot 150 of the present invention includes a communication interface 160 that communicates with a communication interface 140 of a semi-centralized robot frit management system 100, a robot manager 170 that controls mode switching (for example, from centralized mode to distributed mode or from distributed mode to centralized mode) and manages robot operation, and a robot autonomous driving module 180 that determines its position through sensors such as LiDAR, recognizes obstacles and performs autonomous driving.

[0011] In one embodiment, each robot 150 receives a movement path, determines its position via attached sensors, recognizes obstacles, and is capable of autonomous movement. In one embodiment, the robot 150 is capable of autonomous movement in an autonomous driving area in accordance with a pre-set right-hand traffic rule. In one embodiment, the area where the robot can autonomously move along the travel path is an autonomous driving area with two or more lanes, and the autonomous driving area does not include waypoint nodes (220 in Figure 2). In one embodiment, the traffic signal includes information regarding the entry order and direction of movement of at least one robot in the primary pathway. The robots move using a distributed control system in the space excluding designated nodes. Spaces operated using a distributed control system are controlled differently, with secondary or more passages (where two or more robots can move) and primary passages separated. In existing centralized systems, robots continuously transmit position and status information to the Flit Management System, which in turn transmits movement paths and control signals to the robots. However, in this invention, the robot 150 requests the semi-centralized robot frit management system 100 to perform tasks such as assigning tasks, setting destinations, and approving passage through specific waypoints, and the semi-centralized robot frit management system 100 provides a response to these requests. The semi-centralized robot frit management system 100 of the present invention only needs to respond to requests from the robot 150, thus eliminating the need for a continuous network connection and solving network latency and bandwidth problems. Furthermore, the semi-centralized robot frit management system 100 of the present invention only requires the addition of a communication interface 160 and a robot manager module 170 to a robot with built-in autonomous driving capabilities in order to work in conjunction with a robot 150. Therefore, the semi-centralized robot frit management system 100 of the present invention can work in conjunction with various types of robots. Figure 2 shows an exemplary map used for operating a robot in a semi-centralized robot frit management system according to one embodiment of the present invention. This invention provides a robot frit management system (FMS) that can efficiently manage multiple autonomous mobile robots (AMRs) when humans and robots collaborate in logistics and manufacturing sites. The present invention presents a hybrid semi-centralized robot frit management system 100 that combines centralized and distributed control methods to optimize robot operation and increase efficiency. The present invention presents a semi-centralized robot frit management system 100 that determines the optimal path for the robot and controls it to work smoothly without collisions or stalemates, while keeping the system complexity low. Furthermore, the automatic avoidance function of the autonomous mobile robot (AMR) supports the effective operation of robots even in dynamic environments where cooperation with workers is necessary. In this invention, the robot 150 can autonomously move around the general node 210 on the map in Figure 2. Adjacent nodes are connected by edges to form a graph, and task assignment and path planning are carried out based on this graph. On the map, specific intersection nodes are designated as waypoint nodes 220, and traffic control is performed at waypoint nodes 220. The semi-centralized robot traffic management system 100 of the present invention centrally manages the robots 150 at waypoint nodes 220.

[0012] In the semi-centralized robot frit management system 100 of the present invention, the general node 210 operates the robots 150 independently using a distributed control method. In the map in Figure 2, the area where general node 210 is located is called the autonomous driving area, and the robots move autonomously within this area, avoiding each other. The intersection area where the intermediate node 220 is located is called the centralized control area. When robot 150 arrives at intermediate node 220, it checks whether it is possible to move to the next intermediate node and then moves to the next intermediate node. Traffic control at the intermediate node 220 functions as a traffic light, and the traffic control module 130 generates traffic signals to optimize the robot's movement sequence. Figure 3 shows an example of a dependency graph that represents the prerequisites that a robot must check in order to move to each waypoint node, using a trunk line. The traffic control module 130 stores the movement sequence of at least one robot at each waypoint node in the form of a dependency graph. The dependency graph displays each of the multiple waypoint nodes that at least one robot must traverse over time as a vertex, and represents the movement conditions between robots for at least one waypoint node displayed at the vertex as an edge. As shown in Figure 3, the traffic control module 130 extracts the necessary preconditions for passing through each waypoint node on the planned route and displays them in a dependency graph. The traffic control module 130 controls traffic using a dependency graph. To create a dependency graph based on the planned path, the vertices are set to indicate the positions the robot should pass through at each time stage. If another robot must visit a specific node before moving to that node, that relationship is linked to the main line (Edge) 340. Traffic control checks the preceding conditions in real time using a dependency graph and classifies each node as (1) completed movement, (2) movable, or (3) immovable based on the robot's current position information. If the robot's location information is continuously received, the system updates the information on the waypoint nodes that the robot can reach. When the robot reaches a waypoint node, it sends a transit request regarding the possibility of moving to the next waypoint node, updates the dependency graph, and communicates to the robot whether transit is permitted or not. Along with the transit permission, the robot's travel path is also communicated.

[0013] In one embodiment, if a secondary abnormal passage occurs and the robots move in different directions from each other, collisions can be prevented by setting and operating according to standards such as a right-hand traffic rule. For example, a robot can move according to right-hand traffic rules and avoid dynamic obstacles using avoidance techniques such as velocity obstacles (VO). The robot may arrive at waypoint nodes in a different order than planned, but in this case, the dependencies must be readjusted and the dependencies reconfigured. If a cycle occurs in the reset graph, a deadlock may occur, in which case the original movement order will be followed. If robots do not arrive in order near a waypoint node, the robots will wait near that node, and according to priority, they can move first by overtaking the waiting robots. In one embodiment, in the case of a primary pathway, robots cannot move in both directions, and all robots must move in only one direction. The optimized path planning predetermines the robot entry order and direction of movement, and the traffic control module 130 adjusts the robots to match this order, so there is no possibility of robots coming from opposite directions or getting stuck. Therefore, if one robot is waiting at a waypoint node, subsequent robots will wait in accordance with that order and pass through the pathway in sequence. Figure 4 shows the input and output of the multiple agent pathfinding (MAPF) of the path planning module of the present invention. In one embodiment of the present invention, the calculation of the travel path by the path planning module 120 is performed using multi-agent pathfinding (MAPF). The objective function of multi-agent pathfinding (MAPF) is the sum of the travel distance / time for each robot along the path (sum of costs) or the maximum required time / distance for each robot (makespan).

[0014] Multiple-agent pathfinding (MAPF) is the problem of finding a collision-free path in space and time for multiple robots with a starting point and a destination. Traditionally, MAPF defines the problem in a graph-based search format, and the input / output can be represented as shown in Figure 4. Based on this definition, there are various methods for solving the MAPF problem. Representative methods for solving the Multi-Agent Pathfinding (MAPF) problem include the Priority Planning (PP) method, which sequentially searches for paths using previously explored robot paths as obstacles based on the priority of the robots; the Independent Detection (ID) method, which groups robots that will collide with each other in their paths and performs an A* search for each individual group; and the Conflict Based Search (CBS) method, which expands the search based on each robot's path until an optimal solution is derived that avoids the sections in between where collisions will occur. Although there are differences depending on the multi-agent pathfinding (MAPF) method, it is possible to find the optimal or near-optimal path depending on the objective function to be achieved. Typically, the objective function used is either the sum of the distance traveled by all robots along the path (sum of costs) or the maximum time taken per robot (makespan). Figure 5 is a table summarizing the features of the semi-centralized control system of the present invention in comparison with conventional centralized and distributed control systems. As shown in Figure 5, the semi-centralized robot frit management system of the present invention enables optimization, prevention of stalemate, large-scale robot expansion, and adaptation to dynamic environments, eliminates communication bottleneck phenomena, is compatible with different models, and is applicable to automated guided vehicles (AGVs) and autonomous mobile robots (AMRs). Figure 6 is a flowchart showing a semi-centralized robot frit management method according to one embodiment of the present invention.

[0015] A semi-centralized robot frit management method according to one embodiment of the present invention includes a task assignment step (S610) in which tasks are assigned to each of a plurality of robots, a path planning step (S620) in which a travel path is calculated in which each robot can move to a destination in order to complete the assigned task without colliding with each other, and a traffic control step (S630) in which traffic signals are generated to control the entry order and direction of movement of at least one robot at a waypoint node of an intersection. In one embodiment, each robot that receives the movement path can determine its position via its attached sensors, recognize obstacles, and move autonomously. In one embodiment, the areas where the robot can autonomously move along the travel path are autonomous driving areas with two or more lanes, and the autonomous driving areas do not include waypoint nodes. In one embodiment, the robot is capable of autonomous movement within an autonomous driving area in accordance with pre-set right-hand traffic rules. In one embodiment, the traffic signal includes information regarding the entry order and direction of movement of at least one robot in the primary pathway. In one embodiment, the traffic control stage includes a step of generating a dependency graph in which the movement order of at least one robot at each waypoint node is represented, the dependency graph displays each of the multiple waypoint nodes that the at least one robot must traverse over time as a vertex, and the movement conditions between robots for the at least one waypoint node displayed at the vertex are represented as edges. In one embodiment, the path planning stage includes a step of calculating a travel path using multi-agent pathfinding (MAPF), wherein the objective function of multi-agent pathfinding (MAPF) is the sum of the travel distance / time for each robot along the path (sum of costs) or the maximum required time / distance for each robot (makespan). Figure 7 shows an exemplary computing device that can embody various embodiments of the present invention, including devices and / or systems. Referring to Figure 7, an exemplary computing device 700 that can realize an apparatus according to several embodiments of the present disclosure will be described in more detail. The computing device 700 may include one or more processors 710, a bus 750, a communication interface 770, a memory 730 for loading computer programs 791 performed by the processors 710, and a storage 790 for storing the computer programs 791.

[0016] However, Figure 7 shows only the components relevant to the embodiments of this disclosure. Therefore, a person of ordinary skill in the art to which this disclosure belongs will see that, in addition to the components shown in Figure 7, other general-purpose components are also included. The processor 710 controls the overall operation of each component of the computing device 700. The processor 710 comprises a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphic Processing Unit), or any form of processor 710 well known in the art of this disclosure. The processor 710 may also perform calculations for at least one application or program to carry out the method according to the embodiments of this disclosure. The computing device 700 may comprise one or more processors 710. The computing device 700 may refer to artificial intelligence (AI). Memory 730 stores various data, instructions, and / or information. Memory 730 can load one or more programs 791 from storage 790 to perform the method according to embodiments of the present disclosure. Memory 730 is embodied in volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto. Bus 750 provides communication functions between the components of the computing device 700. Bus 750 can be implemented in various forms, such as an address bus, a data bus, and a control bus. The communication interface 770 supports wireless internet communication of the computing device 700. Furthermore, the communication interface 770 can also support a variety of communication methods other than internet communication. For this purpose, the communication interface 770 may be configured to include communication modules well known in the art of this disclosure. According to some embodiments, the communication interface 770 may be omitted. Storage 790 can temporarily store one or more programs 791 and various data. The storage 790 may comprise non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which this disclosure belongs.

[0017] When the computer program 791 is loaded into memory 730, it may include one or more instructions that cause the processor 710 to perform methods / operations according to various embodiments of the present disclosure. That is, the processor 710 can perform methods / operations according to various embodiments of the present disclosure by executing one or more instructions. Although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and it is of course possible for a person with ordinary skill in the art to carry out various modifications without departing from the gist of the present invention as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospects of the present invention.

Claims

1. A task assignment module that assigns tasks to each of multiple robots to perform; a path planning module that calculates a travel path that allows each of the robots to move to a destination in order to complete the assigned task without colliding with each other; and a traffic control module that generates traffic signals to control the entry order and direction of movement of at least one of the robots at an intersection waypoint node. A semi-centralized robotic flit management system, including a communication interface for communicating the aforementioned travel path and traffic signals to the robot.

2. The semi-centralized robotic frit management system according to claim 1, wherein each of the robots that receives the aforementioned movement path can determine its position via attached sensors, recognize obstacles, and move autonomously.

3. The semi-centralized robot frit management system according to claim 2, characterized in that the area along the aforementioned travel path in which the robot can autonomously move is an autonomous driving area with two or more lanes, and the autonomous driving area does not include the waypoint nodes.

4. The semi-centralized robotic frit management system according to claim 3, wherein in the autonomous driving area, the robot is capable of autonomous movement in accordance with a pre-set right-hand traffic rule.

5. The semi-centralized robotic traffic control system according to claim 1, wherein the traffic signals include information regarding the entry order and direction of movement of at least one robot in a single-lane passage.

6. The traffic control module stores the movement sequence of at least one robot at the waypoint nodes in the form of a dependency graph, the dependency graph displays each of a plurality of waypoint nodes that the at least one robot should pass over time as a vertex, and represents the movement conditions between robots for the at least one waypoint node displayed at the vertex as an edge, the semi-centralized robot frit management system according to claim 1.

7. The semi-centralized robot frit management system according to claim 1, characterized in that the calculation of the movement path by the path planning module is performed using multi-agent pathfinding (MAPF), and the objective function of the multi-agent pathfinding (MAPF) is the sum of the travel distance / time for each robot along the path (sum of costs) or the maximum required time / distance for each robot (makespan).

8. A semi-centralized robot frit management method comprising: a task assignment step of assigning tasks to be performed to each of several robots; a path planning step of calculating a travel path that allows each of the robots to move to a destination in order to complete the assigned tasks without colliding with each other; a traffic control step of generating traffic signals to control the entry order and direction of movement of at least one of the robots at a waypoint node at an intersection; and a communication step of communicating the travel paths and the traffic signals to the robots.

9. The semi-centralized robot frit management method according to claim 8, wherein each of the robots that receives the aforementioned movement path can determine its position via attached sensors, recognize obstacles, and move autonomously.

10. The semi-centralized robot frit management method according to claim 9, characterized in that the area along the aforementioned travel path in which the robot can autonomously move is an autonomous driving area with two or more lanes, and the autonomous driving area does not include the waypoint nodes.

11. The semi-centralized robot frit management method according to claim 10, wherein the robot is capable of autonomous movement in the autonomous driving area in accordance with a pre-set right-hand traffic rule.

12. The semi-centralized robot frit management method according to claim 8, wherein the traffic signal includes information regarding the entry order and direction of movement of at least one robot in a single-lane passage.

13. The semi-centralized robot frit management method according to claim 8, wherein the traffic control step includes a step of generating a dependency graph in which the movement order of at least one robot at the waypoint nodes is represented, the dependency graph displays each of a plurality of waypoint nodes that the at least one robot must pass through over time as a vertex, and the movement conditions between robots for the at least one waypoint node displayed at the vertex are represented as edges.

14. The semi-centralized robot frit management method according to claim 8, wherein the path planning step includes a step of calculating a movement path using multi-agent pathfinding (MAPF), and the objective function of the multi-agent pathfinding (MAPF) is the sum of the travel distance / time for each robot along the path (sum of costs) or the maximum required time / distance for each robot (makespan).

Images