Robot communication control system and method based on core control module

Abstract

The invention discloses a robot communication control system and method based on a core control module, and relates to the technical field of communication control. The method comprises the following steps: acquiring tasks to be processed in a working area, dividing the tasks into different priorities, acquiring distribution coefficients of different tasks to be processed in combination with equipment information of a robot, performing task distribution on the different tasks to be processed, acquiring a channel evaluation coefficient of the robot, and performing channel optimization on the channel evaluation coefficient. The method comprises the following steps: acquiring a communication topological graph in a working area and topological coefficients of different robots, performing topological reconstruction on the communication topological graph by utilizing the topological coefficients, acquiring predicted topological coefficients of the different robots, and performing topological pre-reconstruction on the robots by utilizing the predicted topological coefficients; the flexibility of task allocation can be effectively improved, and the communication connection conditions of different AGVs and mechanical arms can be dynamically adjusted in combination with multiple influence factors.

Description

Technical Field

[0001] The present invention relates to the technical field of communication control, and specifically to a robot communication control system and method based on a core control module. Background Art

[0002] With the development of intelligent technologies, more and more tasks are replaced by devices. For example, in warehousing, by deploying different robots in a warehouse and setting up independent modules for control, the efficiency of warehousing transportation and sorting can be significantly improved. When the scale of the warehouse becomes larger, optimizing the communication of the devices therein has also become a technical problem that urgently needs to be solved.

[0003] In the prior art, firstly, the allocation of warehousing tasks lacks flexibility, and there will be a situation where some devices are frequently used while some are frequently idle. This not only is not conducive to improving efficiency but also indirectly affects the communication control of frequently used devices. Moreover, in the prior art, the communication optimization of different devices often remains limited to a single influencing factor, and there is a lack of technical means to synergistically optimize devices by considering multiple different influencing factors. In view of the deficiencies of the prior art, the present invention provides a robot communication control system and method based on a core control module. Summary of the Invention

[0004] The purpose of the present invention is to provide a robot communication control system and method based on a core control module.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A robot communication control system based on a core control module includes the following modules:

[0006] A data twin module, configured to obtain the device information of the robot and the scene information of its working area, and construct a corresponding digital twin model;

[0007] A task allocation module, configured to obtain the tasks to be processed in the working area, divide them into different priorities, obtain the allocation coefficients of different tasks to be processed in combination with the device information of the robot, and allocate different tasks to be processed according to the allocation coefficients;

[0008] A channel optimization module, configured to obtain the channel evaluation coefficient of the robot and optimize its channel according to the channel evaluation coefficient;

[0009] A topology reconstruction module, configured to obtain the communication topology map in the working area, obtain the topology coefficients of different robots, reconstruct the topology using the topology coefficients, obtain the predicted topology coefficients of different robots, and pre-reconstruct the topology of the robots using the predicted topology coefficients.

[0010] Further, the process of obtaining the device information of the robot and the scenario information of its working area and constructing the corresponding digital twin model includes:

[0011] The robot is an AGV and a robotic arm, the working area is a warehouse, the device information includes a core control module and status parameters, and the scenario information includes a spatial layout and environmental parameters;

[0012] The core control module of the AGV includes an ARM+FPGA dual-core processor, a lidar unit, a positioning unit, a camera unit, a dual-channel transceiver, and a lithium battery pack. The status parameters of the AGV include coordinates, navigation path, speed and acceleration, remaining battery power, signal strength, and data transmission rate;

[0013] The core control module of the robotic arm includes a vision processing unit, a clamping unit, and an antenna array. The status parameters of the robotic arm include joint angles, joint speeds, forces and torques, signal strength, and data transmission rate;

[0014] The spatial layout of the warehouse includes the warehouse boundary, shelf positions, working areas, ground conditions, and driving areas. The environmental parameters of the warehouse include temperature, humidity, light intensity, noise, and air quality. There are several distributed communication base stations in the warehouse;

[0015] Using digital twin technology, a digital twin model is constructed based on the obtained spatial layout of the warehouse and the core control module of the robot, and the environmental parameters of the warehouse and the status parameters of the robot are uploaded to the digital twin model for synchronization.

[0016] Further, the process of obtaining the tasks to be processed in the working area and dividing them into different priorities includes:

[0017] The tasks to be processed refer to various transportation tasks and sorting tasks that have not been processed in the warehouse. When goods are put into storage, their corresponding importance levels are set, and the tasks to be processed corresponding to goods with different importance levels are marked with different priorities, including high priority, medium priority, and low priority.

[0018] Further, the process of obtaining the allocation coefficients of different tasks to be processed in combination with the device information of the robot and performing task allocation for different tasks to be processed according to the allocation coefficients includes:

[0019] Corresponding priority coefficients Q are set for different priorities of tasks to be processed. For transportation tasks, the allocation coefficient S between a single transportation task and a single AGV is obtained a ;

[0020]

[0021] ω1, ω2, ω3 are preset weight values, D sh 、Dmax are the remaining power and the maximum power of the single AGV, respectively, R dq , R max are the number of tasks of the current load of the single AGV and the maximum number of tasks it can carry, respectively, L mb , L max are the shortest distance between the single AGV and the goods corresponding to the single transportation task and the longest distance the single AGV can travel, respectively;

[0022] The task allocation refers to obtaining the allocation coefficients between the single transportation task and each other AGV respectively, and allocating them to the AGV with the largest allocation coefficient among them, and allocating each transportation task to the AGV with the largest corresponding allocation coefficient;

[0023] For the sorting task, obtain the allocation coefficient S between the single sorting task and the single robotic arm b ;

[0024]

[0025] ω4, ω5 are preset weight values, P d is the preset fixture matching degree between the goods corresponding to the single sorting task and the single robotic arm, G dq , G max are the number of tasks of the current load of the single robotic arm and the maximum number of tasks it can carry, respectively;

[0026] The task allocation refers to obtaining the allocation coefficients between the single sorting task and each other robotic arm respectively, and allocating them to the robotic arm with the largest allocation coefficient among them, and allocating each sorting task to the robotic arm with the largest corresponding allocation coefficient.

[0027] Furthermore, the process of obtaining the channel evaluation coefficient of the robot and performing channel optimization according to the channel evaluation coefficient includes:

[0028] Set the evaluation period, and when each evaluation period is reached, obtain the channel evaluation coefficients L between each AGV and robotic arm and the distributed communication base stations they are communicatively connected to respectively q ;

[0029]

[0030] is the preset weight value, B s , B t , B w are the signal-to-noise ratio, throughput, and bit error rate between the corresponding AGV and robotic arm and the distributed communication base stations they are communicatively connected to, respectively;

[0031] The channel optimization refers to dividing the corresponding channels into different load states according to the channel evaluation coefficient, changing the modulation mode of the corresponding channels in the high-load state to 64QAM, changing the modulation mode of the corresponding channels in the medium-load state to 16QAM, and changing the corresponding channels in the low-load state to LoRa channels.

[0032] Further, the process of obtaining the communication topology map in the working area and obtaining the topology coefficients of different robots and performing topology reconstruction using the topology coefficients includes:

[0033] In the digital twin model, each AGV is used as a topology point, and the channel between each AGV and the distributed communication base station to which it is communicatively connected is used as a topology edge to construct the corresponding communication topology map. In the communication topology map, the topology coefficient T between a single AGV and a single distributed communication base station is obtained p ;

[0034]

[0035] α1, α2, α3, α4 are preset weight values, Ltb is the shortest distance between the single AGV and the single distributed communication base station, and V s is the speed of the single AGV;

[0036] The topology reconstruction refers to separately obtaining the topology coefficients between the single AGV and other distributed communication base stations, communicatively connecting it to the distributed communication base station with the largest topology coefficient among them, communicatively connecting each AGV to the distributed communication base station with the largest topology coefficient corresponding to it, and updating the communication topology map.

[0037] Further, the process of obtaining the predicted topology coefficients of different robots and performing topology pre-reconstruction using the predicted topology coefficients includes:

[0038] Set a prediction period, and continuously obtain the speed of a single AGV after one prediction period and the shortest distance between the single AGV and each distributed communication base station in the digital twin model to obtain the predicted topology coefficients between the single AGV and each distributed communication base station;

[0039] The topology pre-reconstruction refers to when the distributed communication base station with the largest predicted topology coefficient is different from the distributed communication base station to which the single AGV is currently communicatively connected, communicatively connecting the single AGV to the distributed communication base station with the largest predicted topology coefficient, communicatively connecting each AGV to the distributed communication base station with the largest predicted topology coefficient corresponding to it, and updating the communication topology map.

[0040] In the embodiment of the present invention, it further includes a robot communication control method based on a core control module, including the following steps:

[0041] Step S1: Obtain the device information of the robot and the scene information of its working area, and construct a corresponding digital twin model;

[0042] Step S2: Obtain the tasks to be processed in the working area, divide them into different priorities, obtain the allocation coefficients of different tasks to be processed in combination with the device information of the robot, and allocate different tasks to be processed according to the allocation coefficients;

[0043] Step S3: Obtain the channel evaluation coefficient of the robot, optimize its channel according to the channel evaluation coefficient, obtain the communication topology map in the working area, obtain the topology coefficients of different robots, use the topology coefficients to perform topology reconstruction on them, obtain the predicted topology coefficients of different robots, and use the predicted topology coefficients to perform topology pre-reconstruction on the robot.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] By dividing the goods into different priorities and combining the device information of the AGVs and robotic arms in the warehouse, the present invention can obtain the allocation coefficients between each AGV and robotic arm and each task to be processed and perform task allocation, which can effectively improve the flexibility of task allocation and ensure that each piece of goods is timely allocated to the most suitable AGV and robotic arm;

[0046] By constructing a communication topology map, obtaining the topology coefficients of different AGVs and robotic arms, and using the topology coefficients to perform topology reconstruction on them, the communication connection conditions of different AGVs and robotic arms can be dynamically adjusted in combination with multiple influencing factors. By obtaining the predicted topology coefficients and performing topology pre-reconstruction, the communication connection conditions that will change can be optimized in advance, which is beneficial to ensuring that it is always connected to the distributed communication base station with the lowest latency and the highest transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] As Figure 1 shown, a robot communication control system based on a core control module includes the following modules:

[0049] A data twin module, configured to obtain the device information of the robot and the scene information of its working area, and construct a corresponding digital twin model;

[0050] A task allocation module, configured to obtain the tasks to be processed in the working area, divide them into different priorities, obtain the allocation coefficients of different tasks to be processed in combination with the device information of the robot, and allocate different tasks to be processed according to the allocation coefficients;

[0051] A channel optimization module, configured to obtain the channel evaluation coefficient of the robot and perform channel optimization on it according to the channel evaluation coefficient;

[0052] A topology reconstruction module, configured to obtain the communication topology map within the working area, obtain the topology coefficients of different robots, perform topology reconstruction on it using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction on the robots using the predicted topology coefficients.

[0053] It should be further noted that in the specific implementation process, the process of obtaining the device information of the robot and the scene information of its working area and constructing the corresponding digital twin model includes:

[0054] In the embodiments of the present invention, the robot refers to an AGV and a robotic arm within the same working area, the working area refers to the warehouse where the robot is located, the device information includes the core control modules and status parameters of the AGV and the robotic arm, and the scene information includes the spatial layout and environmental parameters of the warehouse;

[0055] For the AGV, its core control module includes an ARM+FPGA dual-core processor for path planning and communication acceleration, a lidar unit for obstacle detection, a positioning unit for obtaining the position of the AGV, a camera unit for shelf recognition, a dual-channel transceiver for communication, and a lithium battery pack;

[0056] For the robotic arm, its core control module includes an FPGA-accelerated vision processing unit for target recognition, a clamping unit for grasping box-shaped and bagged goods, and an antenna array for communication;

[0057] The status parameters of the AGV include coordinates, navigation path, speed and acceleration, remaining battery power, signal strength, and data transmission rate, and the status parameters of the robotic arm include joint angles, joint speeds, force and torque, signal strength, and data transmission rate;

[0058] For the warehouse, its spatial layout includes warehouse boundaries, shelf positions, working areas, ground conditions, and driving areas, the environmental parameters of the warehouse include temperature, humidity, light intensity, noise, and air quality, and several distributed communication base stations are also arranged in the warehouse to form a full-field coverage MESH network to support dynamic spectrum switching;

[0059] Using digital twin technology, construct a corresponding digital twin model according to the obtained spatial layout of the warehouse and the core control modules of the robots, and upload the environmental parameters of the warehouse and the status parameters of the robots to the constructed digital twin model for synchronization.

[0060] It should be further noted that in the specific implementation process, the process of obtaining the tasks to be processed in the working area and dividing them into different priorities includes:

[0061] The tasks to be processed refer to various transportation tasks and sorting tasks that have not been processed in the warehouse. The single transportation task and sorting task are divided into several subtasks to be executed by the AGV and the robotic arm. The transportation task is divided into path planning + obstacle avoidance + shelf docking, and the sorting task is divided into visual recognition + grasping trajectory planning + placement verification;

[0062] When the goods are put into storage, their corresponding importance levels are set, including high importance level, medium importance level, and low importance level. The tasks to be processed corresponding to the goods of different importance levels are divided into different priorities, including high priority, medium priority, and low priority;

[0063] Among them, the tasks to be processed corresponding to the goods of high importance level are regarded as high priority, the tasks to be processed corresponding to the goods of medium importance level are regarded as medium priority, and the tasks to be processed corresponding to the goods of low importance level are regarded as low priority.

[0064] It should be further noted that in the specific implementation process, the process of obtaining the allocation coefficients of different tasks to be processed in combination with the equipment information of the robot and performing task allocation on different tasks to be processed according to the allocation coefficients includes:

[0065] Corresponding priority coefficients Q are set for the tasks to be processed of different priorities. Different calculation methods are adopted for the transportation task and the sorting task respectively to obtain their corresponding allocation coefficients. Taking a single transportation task as an example, the allocation coefficient between the single transportation task and a single AGV is obtained and denoted as S a ;

[0066]

[0067] ω1, ω2, ω3 are preset weight values, and the sum of the three is equal to 1, D sh , D max are the remaining power and the maximum power of the single AGV respectively, R dq , R max are the number of tasks currently loaded by the single AGV and the maximum number of tasks it can load respectively, L mb , L max are the shortest distance between the single AGV and the goods corresponding to the single transportation task and the longest distance the single AGV can travel respectively;

[0068] The task allocation refers to obtaining the allocation coefficients between the single transportation task and each other AGV respectively, and allocating it to the AGV with the largest allocation coefficient among them, and allocating each transportation task to the AGV with the largest corresponding allocation coefficient;

[0069] Taking a single sorting task as an example, obtain the allocation coefficient between the single sorting task and the single robotic arm, denoted as S b ;

[0070]

[0071] ω4 and ω5 are preset weight values, and the sum of the two is equal to 1. P d is the preset fixture matching degree between the goods corresponding to the single sorting task and the single robotic arm, and G dq 、G max are the number of tasks currently loaded by the single robotic arm and the maximum number of tasks it can load respectively;

[0072] The task allocation refers to obtaining the allocation coefficients between the single sorting task and each other robotic arms respectively, and allocating it to the robotic arm with the largest allocation coefficient among them, and allocating each sorting task to the robotic arm with the largest corresponding allocation coefficient.

[0073] It should be further noted that in the specific implementation process, the process of obtaining the channel evaluation coefficient of the robot and optimizing its channel according to the channel evaluation coefficient includes:

[0074] Set the evaluation period. Every time an evaluation period is reached, obtain the channel evaluation coefficient L between each AGV and robotic arm and the distributed communication base station to which they are communicatively connected respectively q ;

[0075]

[0076] Among them, is the preset weight value, and the sum of the three is equal to 1. B s 、B t 、B w are the signal-to-noise ratio, throughput, and bit error rate between the AGV and robotic arm and the distributed communication base station to which they are communicatively connected respectively;

[0077] The channel optimization refers to dividing the corresponding channel into different load states according to the channel evaluation coefficient, including high load state, medium load state, and low load state;

[0078] If L q > 80%, then mark it as the high load state and change the modulation mode of its corresponding channel to 64QAM. If 40% < L qIf it is ≤ 80%, it is marked as a medium load state, and the modulation mode of its corresponding channel is changed to 16QAM. If L q ≤ 40%, it is marked as a low load state, and its corresponding channel is changed to the LoRa channel.

[0079] It should be further noted that in the specific implementation process, obtaining the communication topology map in the working area and obtaining the topology coefficients of different robots, the process of using the topology coefficients to perform topology reconstruction includes:

[0080] In the digital twin model, each AGV is used as a topology point, and the channel between each AGV and the distributed communication base station to which it is communicatively connected is used as a topology edge. Based on this, the corresponding communication topology map is constructed. In the communication topology map, the topology coefficient T between a single AGV and a single distributed communication base station is obtained p ;

[0081]

[0082] α1, α2, α3, α4 are preset weight values, and the sum of the four is equal to 1. D sh is the remaining power of this single AGV, and R dq is the number of tasks of the current load of this single AGV, Ltb is the shortest distance between this single AGV and this single distributed communication base station, and V s is the speed of this single AGV;

[0083] The topology reconstruction refers to separately obtaining the topology coefficients between this single AGV and other distributed communication base stations, and communicatively connecting it to the distributed communication base station with the largest topology coefficient among them. Each AGV is respectively communicatively connected to the distributed communication base station with the largest topology coefficient corresponding to it, and the communication topology map is updated.

[0084] It should be further noted that in the specific implementation process, obtaining the predicted topology coefficients of different robots, the process of using the predicted topology coefficients to perform topology pre-reconstruction on the robots includes:

[0085] Set a prediction period. In the digital twin model, continuously obtain the speed of a single AGV after one prediction period and the shortest distance between this single AGV and each distributed communication base station, and then obtain the predicted topology coefficients between this single AGV and each distributed communication base station;

[0086] The topology pre-reconstruction refers to judging whether the distributed communication base station with the largest predicted topology coefficient is the same as the distributed communication base station to which this single AGV is currently communicatively connected. If so, no operation is performed on this single AGV;

[0087] Otherwise, connect the single AGV to the distributed communication base station with the largest predicted topology coefficient, connect each AGV to the distributed communication base station with the largest predicted topology coefficient corresponding to it, and update the communication topology graph.

[0088] An embodiment of the present invention further includes a robot communication control method based on a core control module, which includes the following steps:

[0089] Step S1: Obtain the device information of the robot and the scene information of its working area, and construct a corresponding digital twin model;

[0090] Step S2: Obtain the tasks to be processed in the working area, divide them into different priorities, obtain the allocation coefficients of different tasks to be processed in combination with the device information of the robot, and allocate different tasks to be processed according to the allocation coefficients;

[0091] Step S3: Obtain the channel evaluation coefficient of the robot, optimize its channel according to the channel evaluation coefficient, obtain the communication topology graph in the working area, obtain the topology coefficients of different robots, perform topology reconstruction on them using the topology coefficients, obtain the predicted topology coefficients of different robots, and perform topology pre-reconstruction on the robot using the predicted topology coefficients.

[0092] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A robot communication control system based on a core control module, characterized in that: Includes the following modules: The data twin module is used to obtain the robot's equipment information and the scene information of its working area, and build the corresponding digital twin model; The task allocation module is used to obtain pending tasks in the working area and divide them into different priorities, obtain the allocation coefficients of different pending tasks in combination with the device information of the robot, and allocate tasks to different pending tasks according to the allocation coefficients; A channel optimization module is used to obtain the channel evaluation coefficient of the robot and optimize the channel according to the channel evaluation coefficient; The topology reconstruction module is used to obtain the communication topology map in the working area, obtain the topology coefficients of different robots, use the topology coefficients to reconstruct their topology, obtain the predicted topology coefficients of different robots, and use the predicted topology coefficients to pre-reconstruct the topology of the robots.

2. A robot communication control system based on a core control module according to claim 1, characterized in that: The process of building a digital twin model includes: The robots are AGVs and robotic arms, the work area is a warehouse, the device information includes a chip control module and state parameters, and the scene information includes space layout and environmental parameters; The AGV's chip control module includes an ARM+FPGA dual-core processor, a laser radar unit, a positioning unit, a camera unit, a dual-channel transceiver, and a lithium battery pack. The AGV's status parameters include coordinates, navigation path, speed and acceleration, remaining power, signal strength, and data transmission rate. The core control module of the robot arm includes a visual processing unit, a gripping unit, and an antenna array. The state parameters of the robot arm include joint angles, joint speeds, forces and torques, signal strength, and data transmission rate. The spatial layout of the warehouse includes warehouse boundaries, shelf locations, work areas, ground conditions, and driving areas. The environmental parameters of the warehouse include temperature, humidity, light intensity, noise, and air quality. Several distributed communication base stations are set up in the warehouse. Digital twin technology is used to build a digital twin model based on the acquired spatial layout of the warehouse and the robot's core control module, and the warehouse's environmental parameters and the robot's status parameters are uploaded to the digital twin model for synchronization.

3. A robot communication control system based on a core control module according to claim 2, characterized in that: The process of obtaining pending tasks and classifying them into different priorities includes: The pending tasks refer to the various transportation tasks and sorting tasks that have not been processed in the warehouse. When the goods are put into the warehouse, the corresponding importance is set for them, and the pending tasks corresponding to the goods of different importance are marked with different priorities, including high priority, medium priority, and low priority.

4. A robot communication control system based on a core control module according to claim 3, characterized in that: The process of obtaining the allocation coefficients of different tasks to be processed and allocating them includes: Set the corresponding priority coefficient Q for the pending tasks of different priorities. For the transportation task, obtain the allocation coefficient S between a single transportation task and a single AGV. a ; ω1, ω2, ω3 are the preset weight values, D sh , D max are the remaining power and maximum power of the single AGV, R dq , R max are the number of tasks currently loaded by the single AGV and the maximum number of tasks it can load, L mb , L max The shortest distance between the single AGV and the goods corresponding to the single transport task and the longest distance that the single AGV can travel are respectively; The task allocation refers to respectively obtaining the allocation coefficients between the single transport task and other AGVs, and allocating them to the AGV with the largest allocation coefficient, and respectively allocating each transport task to the AGV with the largest allocation coefficient corresponding to it; For the sorting task, obtain the allocation coefficient S between a single sorting task and a single robot arm b ; ω4 and ω5 are the preset weight values, P d is the preset fixture matching degree between the goods corresponding to the single sorting task and the single robot arm, G dq , G max They are the number of tasks currently loaded by the single robot arm and the maximum number of tasks it can load; The task allocation refers to respectively obtaining the allocation coefficients between the single sorting task and other robot arms, and allocating them to the robot arm with the largest allocation coefficient, and allocating each sorting task to the robot arm with the largest allocation coefficient corresponding to it.

5. A robot communication control system based on a core control module according to claim 4, characterized in that: The process of obtaining the robot's channel evaluation coefficient and performing channel optimization includes: Set the evaluation cycle. When each evaluation cycle is reached, obtain the channel evaluation coefficient L between each AGV and the distributed communication base station connected to it. q ; is the preset weight value, B s , B t , B w They are the signal-to-noise ratio, throughput, and bit error rate between the distributed communication base stations corresponding to the AGV and the robotic arm and their communication connections; The channel optimization refers to dividing the corresponding channel into different load states according to the channel evaluation coefficient, changing the modulation mode of the corresponding channel in the high load state to 64QAM, changing the modulation mode of the corresponding channel in the medium load state to 16QAM, and changing the corresponding channel in the low load state to the LoRa channel.

6. A robot communication control system based on a core control module according to claim 5, characterized in that: The process of obtaining the topological coefficients of the robot and reconstructing its topology includes: In the digital twin model, each AGV is taken as a topological point, and the channel between each AGV and its distributed communication base station is taken as a topological edge to construct the corresponding communication topology graph. In the communication topology graph, the topological coefficient T between a single AGV and a single distributed communication base station is obtained. p ; α1, α2, α3, and α4 are preset weight values, Ltb is the shortest distance between the single AGV and the single distributed communication base station, and V s is the speed of the single AGV; The topology reconstruction refers to obtaining the topology coefficients between the single AGV and other distributed communication base stations respectively, and connecting its communication to the distributed communication base station with the largest topology coefficient, connecting each AGV to the distributed communication base station with the largest topology coefficient corresponding to it, and updating the communication topology map.

7. A robot communication control system based on a core control module according to claim 6, characterized in that: The process of obtaining the predicted topological coefficients of the robot and pre-reconstructing its topology includes: Set a prediction cycle, and continuously obtain the speed of a single AGV after a prediction cycle and the shortest distance between the single AGV and each distributed communication base station in the digital twin model to obtain the predicted topology coefficient between the single AGV and each distributed communication base station; The topology pre-reconstruction means that when the distributed communication base station with the largest predicted topology coefficient is different from the distributed communication base station to which the single AGV is currently connected, the single AGV is connected to the distributed communication base station with the largest predicted topology coefficient, each AGV is connected to the distributed communication base station with the largest predicted topology coefficient corresponding to it, and the communication topology map is updated.

8. A robot communication control method based on a core control module, which is implemented based on a robot communication control system based on a core control module according to any one of claims 1 to 7, characterized in that: The method comprises: Step S1: Obtain the device information of the robot and the scene information of its working area, and build a corresponding digital twin model; Step S2: Obtain the pending tasks in the working area and divide them into different priorities, obtain the allocation coefficients of different pending tasks in combination with the device information of the robot, and allocate tasks to different pending tasks according to the allocation coefficients; Step S3: Obtain the channel evaluation coefficient of the robot, optimize its channel according to the channel evaluation coefficient, obtain the communication topology map in the working area, obtain the topology coefficients of different robots, use the topology coefficients to reconstruct its topology, obtain the predicted topology coefficients of different robots, and use the predicted topology coefficients to pre-reconstruct the topology of the robot.

Images