Communication method and device for in-field carrying robot based on mesh communication

Abstract

The application discloses a kind of in-field carrying robot communication method and device based on mesh communication, comprising: constructing mesh network;Control each carrying robot respectively broadcast, determine the distance between each node in mesh network, construct dynamic neighbor list;Carrying task is decomposed into multiple task instructions, and is issued to corresponding carrying robot through mesh network;In the case where data needs to be transmitted in carrying process, the transmission priority is determined according to the data type of the data to be transmitted, and the corresponding multi-hop routing algorithm is called;According to the position information, the power information, the load state and the dynamic neighbor list of each carrying robot, the optimal data transmission path is selected by multi-hop routing algorithm;The data to be transmitted is transmitted between each carrying robot through the optimal data transmission path.The application can effectively improve the communication stability and fault tolerance of in-field carrying robot, and guarantee the work efficiency and reliability of robot cluster.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot communication technology, in particular to a mesh communication based in-facility transport robot communication method and device. BACKGROUND

[0002] In in-facility scenarios such as warehouses and intelligent manufacturing workshops, multiple transport robots working collaboratively has become a key way to improve logistics efficiency. However, in-facility environments often have problems such as shelf occlusion, device electromagnetic interference, and signal instability caused by robot movement. Traditional centralized wireless communication (such as single WiFi, base station, Bluetooth) is prone to signal coverage blind spots or communication delays, affecting the collaborative efficiency of robot clusters.

[0003] In the prior art, some solutions use fixed routing wireless communication, but when a node fails, it will cause the entire communication link to be interrupted. Another solution relies on a central controller to forward data, which has a single point of failure risk and is difficult to adapt to dynamic robot movement scenarios. Therefore, there is an urgent need for a communication method that is self-organizing, anti-interference, and highly fault-tolerant to meet the real-time collaboration needs of in-facility transport robots. SUMMARY

[0004] The main purpose of the present application is to provide a mesh communication based in-facility transport robot communication method and device, aiming to solve the technical problems of poor stability and weak fault tolerance of in-facility transport robot communication in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a mesh communication based in-facility transport robot communication method, which comprises:

[0006] Constructing a mesh network based on multiple transport robots in the in-facility working area, wherein the mesh network is composed of multiple transport robots connected to each other as communication nodes, and each transport robot is configured with an independent mesh communication module;

[0007] Controlling each transport robot to broadcast respectively to determine the distance between nodes in the mesh network and construct a dynamic neighbor list;

[0008] Obtaining a transport task, decomposing the transport task into multiple task instructions, and issuing the multiple task instructions to the corresponding transport robots through the mesh network to enable each transport robot to perform transport operations according to the received task instructions;

[0009] In the case where data needs to be transmitted during the transport operation of the transport robot, the transmission priority is determined according to the data type of the data to be transmitted, and the corresponding multi-hop routing algorithm is called according to the transmission priority.

[0010] acquire the position information, the power information and the load state uploaded by each of the transport robots through the mesh network, and select an optimal data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list;

[0011] transmit the data to be transmitted among the transport robots through the optimal data transmission path.

[0012] In an embodiment, the control respectively broadcasts each of the transport robots, determines the distance between each node in the mesh network, and constructs a dynamic neighbor list, including:

[0013] selects any transport robot as a sending node from the plurality of transport robots, and selects other transport robots as other nodes;

[0014] broadcasts a beacon frame containing the ID, position coordinates and current task state information of the sending node through the mesh communication module of the sending node to the surrounding;

[0015] after the other nodes receive the beacon frame, calculates a first distance between the sending node and the other nodes based on the signal strength of the beacon frame;

[0016] when the first distance is less than a preset threshold, takes the corresponding other node as a neighbor node, and updates the neighbor list according to the ID, position coordinates, task state information and first distance of the sending node;

[0017] after completing a broadcast and neighbor list update, selects the next transport robot as a new sending node, repeats the broadcast and distance calculation process until all transport robots complete a round of broadcast and neighbor list update as sending nodes to generate a complete dynamic neighbor list.

[0018] In an embodiment, the transport task is acquired, the transport task is decomposed into a plurality of task instructions, and the plurality of task instructions are issued to corresponding in-plant transport robots through the mesh network, so that each of the in-plant transport robots performs a transport operation according to the received task instructions, including:

[0019] acquires a transport task and determines transport information, wherein the transport information includes a starting position, a target position and transport object information;

[0020] analyzes the transport information, determines the transport steps required to complete the transport task, and converts each transport step into a corresponding task instruction;

[0021] According to the position information of each of the carrying robots and the requirements of the task instructions, each task instruction is distributed to the corresponding carrying robot through the mesh network, so that each of the carrying robots performs the carrying operation according to the received task instruction.

[0022] In an embodiment, after the carrying robots perform the carrying operation according to the position information of each of the carrying robots and the requirements of the task instructions through the mesh network, the method further comprises:

[0023] Obtaining real-time position information, moving speed and task execution progress of each of the carrying robots during the carrying operation;

[0024] According to the real-time position information, moving speed and task execution progress, the position state of each carrying robot at a future time point is estimated;

[0025] Based on the position state at the future time point, it is determined whether there is a conflict between each of the carrying robots;

[0026] In the case of conflict, the type of conflict is determined according to the position state at the future time point, wherein the type of conflict includes chase conflict, intersection conflict, face-to-face conflict and fault conflict;

[0027] According to the type of conflict, a corresponding conflict resolution strategy is generated, wherein the conflict resolution strategy at least includes adjusting the moving speed, changing the moving path, pausing the task execution or reassigning the task instruction;

[0028] The conflict resolution strategy is distributed to the carrying robots involved in the conflict through the mesh network, so that each of the carrying robots adjusts the carrying operation according to the received conflict resolution strategy;

[0029] The execution of the conflict resolution strategy is monitored in real time, and the dynamic adjustment is made according to the execution effect until the conflict is completely resolved.

[0030] In an embodiment, in the case that the carrying robot needs to transmit data during the carrying operation, the transmission priority is determined according to the data type of the data to be transmitted, and the corresponding multi-hop routing algorithm is called according to the transmission priority, comprising:

[0031] In the case that the carrying robot needs to transmit data during the carrying operation, the data to be transmitted of the carrying robot is obtained, and the data type of the data to be transmitted is determined, wherein the data type includes one or more of real-time control instruction, task scheduling information and environment perception data;

[0032] determine transmission delay tolerance according to the data type of the data to be transmitted, wherein the transmission delay tolerance of the real-time control instruction is the lowest, the transmission delay tolerance of the environment perception data is the highest, and the transmission delay tolerance of the task scheduling information is between the real-time control instruction and the environment perception data;

[0033] determine transmission priority according to the transmission delay tolerance, wherein the transmission delay tolerance is inversely proportional to the transmission priority;

[0034] invoke a corresponding multi-hop routing algorithm from a preset multi-hop routing algorithm library according to the transmission priority, wherein the multi-hop routing algorithm library stores multi-hop routing algorithms corresponding to different transmission priorities, a high priority corresponds to a greedy routing algorithm based on link quality, a secondary priority corresponds to a hybrid routing algorithm based on distance and residual energy, and a low priority corresponds to a load balancing routing algorithm.

[0035] In an embodiment, the position information, the power information, and the load state uploaded by each of the transport robots through the mesh network are obtained, and an optimal data transmission path is selected through the multi-hop routing algorithm according to the position information, the power information, the load state, and the dynamic neighbor list, comprising:

[0036] obtaining the position information, the power information, and the load state uploaded by each of the transport robots through the mesh network;

[0037] generating an initial data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state, and the dynamic neighbor list;

[0038] optimizing the initial data transmission path based on an improved discrete whale algorithm to generate an optimal data transmission path.

[0039] In an embodiment, the initial data transmission path is generated through the multi-hop routing algorithm according to the position information, the power information, the load state, and the dynamic neighbor list, comprising:

[0040] in the case that the multi-hop routing algorithm is a greedy routing algorithm based on link quality, selecting the transport robot closest to the data sending end as an initial transmission node according to the position information of each transport robot;

[0041] obtaining link quality information of the initial transmission node and surrounding neighbor nodes through the mesh communication module of the initial transmission node, wherein the link quality information includes signal strength, packet loss rate, and transmission delay;

[0042] According to the link quality information, a node with optimal link quality is selected from the neighbor nodes as a next-hop transmission node;

[0043] The process of selecting the next-hop transmission node is repeated until the data is transmitted to the target transport robot, and an initial data transmission path is generated.

[0044] In an embodiment, the generating of the initial data transmission path by the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list further comprises:

[0045] In the case that the multi-hop routing algorithm is a hybrid routing algorithm based on distance and residual energy, a second distance from the data sending end to all potential next-hop nodes is calculated according to the position information of each transport robot;

[0046] Residual power information and a current load state of each potential next-hop node are obtained;

[0047] According to the second distance, the residual power and the load state, each potential next-hop node is comprehensively evaluated to determine a comprehensive score of each potential next-hop node;

[0048] The potential next-hop node with the highest comprehensive score is selected as the next-hop transmission node;

[0049] The process of selecting the next-hop transmission node is repeated until the data is transmitted to the target transport robot, and an initial data transmission path is generated.

[0050] In an embodiment, the optimizing of the initial data transmission path based on the improved discrete whale optimization algorithm to generate an optimal data transmission path comprises:

[0051] The initial data transmission path is encoded as a whale individual position, and a whale population is initialized based on the whale individual position by a genetic algorithm, each whale individual position in the whale population containing multiple dimensions, and each dimension value representing a node number on the data transmission path;

[0052] The fitness value of each whale individual in the whale population is calculated, and the fitness value is calculated according to the total transmission delay, the total packet loss rate of the path, and the power consumption and the load state of the transport robot;

[0053] An optimal whale individual in the whale population is selected as a leader according to the fitness value, and position updating is performed on the whale population, to obtain an updated whale population, wherein the position updating process comprises leader-guided updating and random walk updating, the leader-guided updating refers to that other whale individuals learn from the leader, and a new position is calculated according to the leader position and the current position of the whale individual, the random walk updating refers to that a whale individual randomly selects a direction to move in the search space, and in the position updating process, if the path node corresponding to the new position exceeds the feasible range or violates the dynamic neighbor list constraint, a new position meeting the constraint condition is regenerated;

[0054] The fitness value of the updated whale population is calculated again, and it is determined whether a preset optimization termination condition is met;

[0055] If the preset optimization termination condition is not met, the steps of calculating the fitness value, selecting the optimal whale individual, performing position updating on the whale population, and calculating the fitness value again are iteratively executed, until the preset optimization termination condition is met, wherein the preset optimization termination condition comprises that a preset maximum number of iterations is reached, or the variation amplitudes of the fitness values of all whale individuals in the whale population in continuous iterations are all less than a preset threshold;

[0056] If the preset optimization termination condition is met, the position of the whale individual with the highest current fitness value is decoded as an optimal data transmission path.

[0057] In addition, to achieve the above object, the application further provides an in-field carrying robot communication device based on mesh communication, which comprises:

[0058] A construction module is configured to construct a mesh network based on a plurality of carrying robots in an in-field working area, wherein the mesh network is composed of a plurality of carrying robots as communication nodes and is connected to each other, and each carrying robot is configured with an independent mesh communication module;

[0059] The construction module is further configured to control each carrying robot to broadcast respectively, to determine the distance between each node in the mesh network, and to construct a dynamic neighbor list.

[0060] A delivery module is configured to obtain a carrying task, to decompose the carrying task into a plurality of task instructions, and to deliver the plurality of task instructions to corresponding carrying robots through the mesh network, so that each carrying robot performs a carrying operation according to the received task instructions.

[0061] determining a transmission priority according to a data type of the data to be transmitted, and calling a corresponding multi-hop routing algorithm according to the transmission priority, when transmission of data is required during execution of the carrying operation by the carrying robots;

[0062] selecting an optimal data transmission path according to the location information, the power information, the load state, and the dynamic neighbor list through the multi-hop routing algorithm;

[0063] transmitting the data to be transmitted between the carrying robots through the optimal data transmission path.

[0064] The one or more technical solutions provided in the present application are based on a plurality of carrying robots in an in-field working area to construct a mesh network, wherein the mesh network is composed of a plurality of carrying robots as communication nodes connected to each other, and each carrying robot is configured with an independent mesh communication module; each of the carrying robots broadcasts to determine distances between nodes in the mesh network and construct a dynamic neighbor list; a carrying task is obtained, the carrying task is decomposed into a plurality of task instructions, and the plurality of task instructions are distributed to corresponding carrying robots through the mesh network, so that each of the carrying robots executes a carrying operation according to the received task instructions; when transmission of data is required during execution of the carrying operation by the carrying robots, a transmission priority is determined according to a data type of the data to be transmitted, and a corresponding multi-hop routing algorithm is called according to the transmission priority; location information, power information, and a load state uploaded by each of the carrying robots through the mesh network are obtained, and an optimal data transmission path is selected according to the location information, the power information, the load state, and the dynamic neighbor list through the multi-hop routing algorithm; and the data to be transmitted is transmitted between the carrying robots through the optimal data transmission path. In this way, by introducing a robot self-organizing mesh network, the integrity of network coverage is improved, a multi-hop routing algorithm is called according to a data transmission priority to plan a data transmission path, fault tolerance is strong, continuous communication of a robot cluster in a complex environment is ensured, communication stability and fault tolerance of in-field carrying robots are effectively improved, and work efficiency and reliability of the robot cluster are further ensured. BRIEF DESCRIPTION OF DRAWINGS

[0065] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, the other drawings can also be obtained based on these drawings without any creative work.

[0067] Figure 1 The flowchart provided by the mesh communication-based indoor carrying robot communication method embodiment one of the present application;

[0068] Figure 2 The flowchart provided by the mesh communication-based indoor carrying robot communication method embodiment two of the present application;

[0069] Figure 3 The module structure diagram of the mesh communication-based indoor carrying robot communication device of the embodiment of the present application.

[0070] The object realization, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0072] In order to better understand the technical solutions of the present application, the following will be described in detail with reference to the drawings and specific embodiments of the present application.

[0073] The present application provides a solution, by introducing robot self-organizing mesh network, improves the integrity of network coverage, according to the data transmission priority calling multi-hop routing algorithm for data transmission path planning, fault tolerance ability is strong, ensure the continuous communication of robot cluster in complex environment, effectively improve the communication stability and fault tolerance of indoor carrying robot, further guarantee the work efficiency and reliability of robot cluster.

[0074] It should be noted that the execution subject of the present embodiment can be a computing service device with data processing, network communication and program running functions, such as tablet computer, personal computer, mobile phone, etc., or an electronic device capable of realizing the above functions, mesh communication-based indoor carrying robot communication equipment, etc. The mesh communication-based indoor carrying robot communication equipment is taken as an example to describe the present embodiment and the following embodiments.

[0075] Based on this, the mesh communication-based indoor carrying robot communication method is provided in the embodiment of the present application, referring to Figure 1 , Figure 1Flowchart of the first embodiment of the mesh communication-based in-factory transport robot communication method of the present application.

[0076] In this embodiment, the mesh communication-based in-factory transport robot communication method comprises steps S10-S60:

[0077] Step S10: Construct a mesh network based on a plurality of transport robots in the in-factory working area, wherein the mesh network is composed of a plurality of transport robots connected to each other as communication nodes, and each transport robot is configured with an independent mesh communication module.

[0078] It should be noted that the in-factory working area refers to a specific area such as a factory or warehouse that needs to transport materials. A plurality of transport robots are distributed in this area, and these robots are equipped with independent mesh communication modules. The mesh communication module is a wireless communication unit with self-organizing and multi-hop transmission capabilities. The mesh communication module supports self-organizing network protocols, can automatically discover surrounding nodes and support direct communication links between robots, and does not need to rely on fixed infrastructure to form a self-organizing mesh network.

[0079] It can be understood that in the mesh network, each transport robot is not only an individual performing transport tasks, but also a communication node that can directly communicate with other robots without relying on a central controller. This decentralized network structure greatly enhances the reliability and fault tolerance of the network. The communication node is the basic unit of the mesh network, each communication node has data receiving and processing capabilities, and can automatically adjust the communication path according to the network protocol to ensure the stability and efficiency of data transmission.

[0080] In a specific implementation, each indoor handling robot is equipped with a mesh communication module supporting IEEE 802.11s or LoRamesh protocol, which integrates an antenna and a processor, can automatically scan the mesh modules of other robots in the surrounding, establish a bidirectional connection through a handshake protocol, and form a distributed mesh network without a central node. Each node in the mesh network can act as a data sender or receiver, or as a relay node forwarding data packets of other nodes. This multi-hop transmission mechanism significantly expands the network coverage, especially for long-distance communication needs in complex factory environments. When a node is blocked by an obstacle or has insufficient power, causing signal attenuation, adjacent nodes can automatically relay the transmission to ensure the continuity of the data link. For example, in the cross-warehouse material transfer scenario, if handling robot A needs to transmit task instructions to robot D, but there is a signal blind area between them, robots B and C can act as relay nodes to deliver data to the target node through multi-hop routing. This dynamic path adjustment capability enables the mesh network to maintain stable data transmission success rate in complex industrial environments.

[0081] Step S20: Control each handling robot to broadcast respectively to determine the distance between each node in the mesh network and construct a dynamic neighbor list.

[0082] It should be noted that each handling robot will periodically broadcast its existence and location information to the surrounding, while receiving the broadcast information of other robots. Through these information, the robot can calculate the distance with other nodes in the surrounding, and construct a dynamic neighbor list accordingly. This list will be updated in real time with the movement of the robot and the change of the environment, ensuring that each robot can accurately grasp the position and state of other nodes in the surrounding network.

[0083] The construction of the dynamic neighbor list is a key step for the mesh network to realize self-organization and self-adaptation, which enables each robot to dynamically adjust its communication strategy according to the current network status and select the optimal communication path. For example, when a robot finds that the distance with the original neighbor node exceeds the preset threshold, it will automatically remove the node from the neighbor list and try to establish a connection with a new node that is closer. This dynamic adjustment mechanism ensures that the mesh network can maintain efficient and stable communication when the robot moves or the environment changes.

[0084] It is worth noting that in the construction of the mesh network, each handling robot broadcasts through its mesh communication module, detects and records the existence and distance information of other robots in the surrounding, and then forms a dynamic neighbor list. The dynamic neighbor list is updated in real time by each handling robot in the mesh network based on the broadcast detection results, which records the information of other robots that can directly communicate with the current robot, including robot number, distance, and signal strength, etc.

[0085] In an implementable embodiment, step S20 can include: selecting any of the plurality of handling robots as a sending node and the other handling robots as other nodes; broadcasting a beacon frame containing its ID, position coordinates and current task state information through the mesh communication module of the sending node to the surroundings; after the other nodes receive the beacon frame, calculating the first distance between the other nodes and the sending node based on the signal strength of the beacon frame; when the first distance is less than a preset threshold, regarding the corresponding other node as a neighbor node and updating its neighbor list according to the ID, position coordinates, task state information and first distance of the sending node; after completing one round of broadcasting and neighbor list updating, selecting the next handling robot as a new sending node and repeating the broadcasting and distance calculation process until all handling robots complete a round of broadcasting and neighbor list updating as sending nodes to generate a complete dynamic neighbor list.

[0086] It should be noted that in the process of generating a complete dynamic neighbor list, each handling robot plays the role of a sending node in turn, ensuring that the distance information between all nodes in the network can be accurately obtained. This polling broadcast mechanism not only improves the comprehensiveness and accuracy of the neighbor list, but also enhances the robustness of the network, because even if part of the nodes cannot participate in broadcasting due to failure or signal interference, other nodes can still complete the construction of the neighbor list through the remaining nodes. In addition, the real-time updating feature of the dynamic neighbor list enables the handling robots to quickly adapt to changes in the network topology, such as the addition of new robots or the departure of old robots, thereby ensuring the continuous stability and efficiency of the mesh network. In practical applications, this dynamic adjustment capability is crucial for maintaining the collaborative work efficiency of the robot cluster, especially in high-density and high-dynamic industrial environments.

[0087] It can be understood that the self ID is a unique identifier of each carrying robot in the mesh network, which is used to distinguish different robot nodes. The position coordinates are accurate position information obtained through GPS / barcode positioning or pre-set positioning base station in the field, which reflects the specific position of the robot in the working area in the field. The current task state information includes idle, load, driving, etc. These information can help other robots understand the working state of the current robot, so as to better allocate tasks and cooperate. The beacon frame, i.e. Beacon frame, is a wireless signal frame containing specific information. In the mesh network, the sending node broadcasts the beacon frame to announce its existence, position and task state and other key information. The design of the beacon frame needs to consider the information integrity and transmission efficiency. It should contain enough information for other nodes to accurately identify and locate, and avoid too long frame length affecting transmission efficiency. In the embodiment, the beacon frame includes the self ID, position coordinates and current task state information of the sending node. In actual application, the beacon frame usually adopts a simple coding format, such as using a specific binary sequence to represent different task states, and using a compressed coordinate representation method to reduce data volume.

[0088] The first distance is calculated based on the signal strength of the beacon frame. There is a certain inverse relationship between signal strength and distance, that is, the stronger the signal, the closer the distance. The calculation formula of the first distance is:

[0089]

[0090] Wherein, is the signal strength at a distance d, is the signal strength at a known distance n is the path loss factor determined by environmental factors, and d is the actual measured distance, is the reference distance.

[0091] It can be understood that when the first distance is less than the preset threshold, it indicates that the two carrying robots are within the effective communication range, at this time the corresponding other node is taken as a neighbor node, and the neighbor list is updated according to the self ID, position coordinates, task state information and first distance of the sending node. This process ensures that the neighbor list records all the robot information that can currently communicate directly with itself, providing a basis for subsequent data transmission and task cooperation.

[0092] After completing a broadcast and neighbor list update, the next transport robot is selected as the new sending node, and the above broadcast and distance calculation process is repeated. This polling mechanism ensures that all transport robots in the network can take turns as sending nodes to complete a round of broadcast and neighbor list update, and finally generate a complete dynamic neighbor list. When a node does not send a Beacon frame for more than 30 seconds, it is automatically removed from the neighbor list, ensuring the real-time nature of the list.

[0093] It is worth noting that in the process of generating a complete dynamic neighbor list, each transport robot plays the role of a sending node. This comprehensive polling method not only improves the comprehensiveness and accuracy of the neighbor list, but also enhances the robustness of the network. Because even if some nodes fail or are interfered with signals and cannot participate in the broadcast, other nodes can still complete the construction of the neighbor list through the remaining nodes, ensuring the continuous stability and efficiency of the mesh network.

[0094] Step S30: Obtain a transport task, decompose the transport task into multiple task instructions, and distribute the multiple task instructions to the corresponding transport robots through the mesh network, so that each transport robot executes the transport operation according to the received task instructions.

[0095] It should be noted that the transport task is usually generated by the management system or dispatch center of the factory. These tasks may involve transporting materials from a certain location in the warehouse to a designated station on the production line, or transporting materials between multiple workshops. The management system will consider various factors such as production plans, material requirements, current state of robots, and in-plant environmental information when generating transport tasks, to ensure the rationality and feasibility of the tasks.

[0096] After obtaining the transport task, the task is decomposed into multiple specific task instructions. This decomposition is based on the functional characteristics of the transport robots, the layout of the in-plant work area, and the specific requirements of the task. For example, a transport task may be decomposed into specific instructions such as "transport material X from warehouse A area to production line B station". Each task instruction will clearly specify the transport robot number, material information, starting position, target position, and completion time of the executing instruction, and other key elements.

[0097] The decomposed task instructions are distributed to the corresponding transport robots through the previously constructed mesh network. During transmission, the mesh network dynamically selects the optimal transmission path according to the current network status and the priority of the task instructions, ensuring that the task instructions can be delivered to the target robots in a timely and accurate manner. This mesh network-based transmission method not only improves the reliability of data transmission, but also enhances the fault tolerance of the network. Even if some nodes fail, data can still be transmitted smoothly through other nodes. After receiving the task instructions, each transport robot will perform the transport operation according to the specific requirements in the instructions. For example, for the instruction "transport material X from warehouse A area to production line B station", the corresponding transport robot will first move to warehouse A area, confirm the location of material X through its own identification system, then transport it to production line B station, and send a task completion feedback to the management system after arrival. During this process, the transport robot will update its position and task status information in real time and feedback these information to the management system through the mesh network, so that the management system can monitor and adjust the overall task progress.

[0098] During task execution, if unexpected situations occur, such as obstacles on the transport path or target materials being taken away by other robots, the transport robot will immediately pause the current task and send an abnormal report to the management system through the mesh network. After receiving the abnormal report, the management system will re-plan the task instructions or adjust the task arrangement of other robots according to the pre-set emergency handling strategy, to ensure the smooth progress of the overall transport task. For example, if an obstacle appears on the transport path, the management system may re-plan a path for the blocked robot to bypass the obstacle; if the target material is taken away by other robots, the management system may assign the task instruction to another idle robot for execution.

[0099] In addition, to further improve the efficiency and accuracy of transport task execution, the management system can also use the mesh network to realize the cooperative work between transport robots. For example, when multiple robots need to transport the same batch of materials to different stations at the same time, the management system can send cooperative instructions to each robot through the mesh network, specifying the transport order and handover method of each robot, thereby avoiding confusion and delay of materials during transport.

[0100] Meanwhile, the mesh network also supports real-time data sharing and state monitoring among the carrying robots. Each robot can upload its own position, speed, power, and task execution progress information in real time through the mesh network, and the management system can dynamically adjust and optimize the overall carrying task according to these information. For example, when the management system finds that the power of a robot is about to run out, it can timely allocate a charging task for the robot or adjust the task arrangement of other robots to ensure that the robot can be charged in time and continue to perform subsequent tasks.

[0101] In a feasible implementation, step S30 can include: obtaining a carrying task and determining carrying information, wherein the carrying information includes a starting position, a target position, and carrying object information; analyzing the carrying information, determining carrying steps required to complete the carrying task, and converting each carrying step into a corresponding task instruction; and according to the position information of each carrying robot and the requirements of the task instructions, issuing each task instruction to the corresponding carrying robot through the mesh network, so that each carrying robot performs carrying operations according to the received task instructions.

[0102] It should be noted that after obtaining the carrying task, the carrying information including the core elements such as the starting position, the target position, and the carrying object information is determined. The starting position determines the initial storage point of the material or goods, the target position points out the place where it should finally arrive, and the carrying object information is the specific object of carrying and the key details such as the material type, weight, size, etc.

[0103] It can be understood that when analyzing the carrying information, the carrying capacity, moving speed, working radius, and in-plant environment layout of the carrying robot need to be considered comprehensively to determine the specific carrying steps required to complete the carrying task. The overall carrying task is refined into multiple executable carrying steps. Each step corresponds to a specific task instruction, which clearly indicates the action to be performed by the carrying robot, the material to be carried, the starting and target positions, and the expected completion time. In the task instruction issuing stage, the management system will accurately send the instructions to the target robot through the mesh network according to the real-time position information of each carrying robot and the specific requirements of the task instructions. In this process, the mesh network will dynamically select the optimal transmission path according to the current network status and the priority of the task instructions to ensure that the instructions can be quickly and accurately delivered. After receiving the task instructions, each carrying robot will immediately analyze the instruction content and perform the corresponding carrying operation according to the instruction requirements. During the execution process, the carrying robot will update its own position and task state information in real time and feed back these key data to the management system through the mesh network. The management system will continuously monitor and dynamically adjust the overall carrying task according to these feedback information to ensure that the task can be completed smoothly according to the predetermined plan.

[0104] In an implementable embodiment, the task instructions are issued to the corresponding transport robots through the mesh network according to the position information of each transport robot and the requirements of the task instructions, so that each transport robot performs the transport operation according to the received task instructions, and further comprising: acquiring real-time position information, moving speed and task execution progress of each transport robot during the transport operation; estimating the position state of each transport robot at a future time point according to the real-time position information, moving speed and task execution progress; determining whether there is a conflict between the transport robots based on the position state at the future time point; in the case of conflict, determining the conflict type according to the position state at the future time point, wherein the conflict type includes chase conflict, intersection conflict, face-to-face conflict and fault conflict; generating a corresponding conflict resolution strategy according to the conflict type, wherein the conflict resolution strategy at least includes adjusting the moving speed, changing the moving path, pausing the task execution or reassigning the task instructions; issuing the conflict resolution strategy to the transport robots involved in the conflict through the mesh network, so that each transport robot adjusts the transport operation according to the received conflict resolution strategy; real-time monitoring the execution of the conflict resolution strategy, and dynamically adjusting according to the execution effect until the conflict is completely resolved.

[0105] It should be noted that there may be a variety of potential conflict situations during the transport operation of each transport robot. If these conflicts are not handled in time, it is easy to cause the interruption of the transport task or the reduction of efficiency.

[0106] It can be understood that each transport robot will continuously collect its real-time position information through the built-in positioning system such as GPS, laser radar or visual recognition technology, and at the same time, accurately measure the moving speed by means of wheel encoder or inertial measurement unit and other devices. In terms of task execution, the robot will record the current task stage, the completed transport volume and the remaining work volume as the task execution progress according to the preset task instructions. These real-time data will be uploaded to the mesh network in time.

[0107] Based on the acquired real-time position information, moving speed and task execution progress, the management system uses a prediction algorithm to estimate the position state of each transport robot at a future time point. The prediction algorithm will comprehensively consider the current moving track of the robot, the speed change trend and the environmental layout in the field and other factors, simulate the subsequent motion of the robot by establishing a mathematical model, so as to accurately determine the position of the robot at a specific time in the future, such as the following formula:

[0108]

[0109] wherein, is the predicted position of the robot at a future time point, is the position of the robot at the current time t, is the velocity of the robot at the current time t, is the acceleration of the robot at the current time t, is the predicted time interval, i.e., from the current time to the target future time, is the task progress factor, is the acceleration adjustment function related to the task progress, is the environment factor, which is modeled based on the distance between the robot and obstacles, the curvature of the path, and other factors.

[0110] Based on the estimated position state at the future time point, it is further determined whether there is a conflict between the carrying robots. When the estimation result shows that there is a conflict between the carrying robots, the specific type of the conflict is determined according to the position state at the future time point. Chase conflict usually occurs when two robots move in the same direction and the rear robot may catch up with the front robot; intersection conflict refers to the conflict caused by two robots possibly reaching an intersection at the same time; face-to-face conflict is that two robots move towards each other and may collide on the path; and fault conflict is the situation that a robot fails and affects the normal execution of tasks by other robots.

[0111] For different types of conflicts, the management system generates corresponding conflict resolution strategies. For chase conflict, the moving speed of the rear robot can be adjusted to maintain a safe distance from the front robot; for intersection conflict, the moving path of one or both robots can be changed to avoid reaching the intersection at the same time; for face-to-face conflict, one of the robots can be temporarily suspended from task execution and wait for the other robot to pass before continuing; for fault conflict, the task affected by the fault can be assigned to other idle robots for execution.

[0112] After generating the conflict resolution strategy, the management system will issue these strategies to the carrying robots involved in the conflict through the mesh network. After receiving the strategy, each carrying robot will immediately adjust its carrying operation according to the strategy requirements. At the same time, the management system will monitor the execution of the conflict resolution strategy in real time, collect feedback information from each robot, and evaluate the execution effect of the strategy. If the execution effect of the strategy is not ideal or the conflict is not completely resolved, the management system will timely make dynamic adjustments, regenerate or optimize the conflict resolution strategy until the conflict is completely resolved, ensuring the smooth progress of the carrying task.

[0113] Step S40: In the case that data transmission is required during the carrying operation of the carrying robot, determine the transmission priority according to the data type of the data to be transmitted, and call the corresponding multi-hop routing algorithm according to the transmission priority.

[0114] It should be noted that the multi-hop routing algorithm is a key technology for efficient data transmission in a network, and is particularly suitable for an in-field carrying robot system based on mesh communication. During the carrying operation of the carrying robot, various types of data may be generated and need to be transmitted, including but not limited to real-time control instructions of the robot, task scheduling information, environmental perception data, etc. In the present embodiment, the multi-hop routing algorithm preferentially selects nodes with less hop count, high signal strength and low load as relays, and low load means that the amount of data currently being transmitted by the node is lower than a preset threshold.

[0115] Different types of data have different importance for the smooth execution of the carrying task, so it is necessary to determine the transmission priority according to the data type. Different multi-hop routing algorithms are called according to the data priority, for example, high-priority data uses a greedy routing algorithm based on link quality (LQ), which preferentially selects a short path with stable signal; low-priority data uses a load balancing routing algorithm to avoid concentrated occupation of the bandwidth of a node.

[0116] The greedy routing algorithm based on link quality (LQ) considers link quality indicators such as signal strength and bit error rate when selecting a path, and preferentially selects a path with the most stable signal and fewer hops for data transmission. This algorithm can ensure that high-priority data such as real-time control instructions and critical task scheduling information can reduce packet loss and delay as much as possible during transmission, thereby ensuring that the carrying robot can receive instructions in a timely and accurate manner and perform corresponding operations. For example, when the management system needs to send an emergency stop instruction to a carrying robot, the greedy routing algorithm based on link quality can quickly find a path with the best signal quality to quickly deliver the instruction, avoiding safety accidents caused by the failure of the instruction to be executed in time due to signal problems.

[0117] The load balancing routing algorithm mainly focuses on the load of each node in the network. It monitors the data transmission volume of each node in real time, and when it finds that the load of a certain node is close to or exceeds the preset threshold, it will actively avoid that node and select other nodes with lower load as relays for data transmission. This can effectively avoid the situation where a node in the network is overloaded and its performance is degraded or even paralyzed, ensuring the data transmission efficiency and stability of the entire network. For low-priority data, such as some non-critical environmental sensing data, using the load balancing routing algorithm can reasonably allocate network resources without affecting the transmission of high-priority data, improving the overall utilization of the network. For example, when multiple handling robots are working simultaneously and generating a large amount of environmental sensing data, the load balancing routing algorithm can evenly distribute the data to different paths for transmission according to the real-time load of each node, avoiding the situation where a node is congested and affects the performance of the entire network.

[0118] In a specific implementation, after determining the transmission priority of the data to be transmitted, the management system will select the corresponding multi-hop routing algorithm for data transmission path planning according to the transmission priority. The multi-hop routing algorithm selects appropriate relay nodes in the network to realize multi-hop transmission of data from the source node to the target node. In this embodiment, the multi-hop routing algorithm considers factors such as the number of hops, signal strength, and load of the nodes, and preferentially selects nodes with fewer hops, high signal strength, and low load as relays to ensure that data can be transmitted in the optimal path.

[0119] When the data transmission process encounters network congestion or node failure, etc., the multi-hop routing algorithm can also dynamically adjust the transmission path and select other available relay nodes for data transmission, thereby ensuring reliable data transmission. This data transmission method based on multi-hop routing algorithm not only improves the efficiency and reliability of data transmission, but also enhances the flexibility and fault tolerance of the network, providing stable and efficient data transmission support for in-field handling robot systems based on mesh communication.

[0120] In an implementable embodiment, step S40 can include: in the case where transmission data is required during the execution of the carrying operation by the carrying robot, obtaining the to-be-transmitted data of the carrying robot, and determining the data type of the to-be-transmitted data, wherein the data type includes one or more of real-time control instructions, task scheduling information, and environmental perception data; determining the transmission delay tolerance according to the data type of the to-be-transmitted data, wherein the transmission delay tolerance of the real-time control instructions is the lowest, the transmission delay tolerance of the environmental perception data is the highest, and the transmission delay tolerance of the task scheduling information is between the real-time control instructions and the environmental perception data; determining the transmission priority according to the transmission delay tolerance, wherein the transmission delay tolerance is inversely proportional to the transmission priority; and calling a corresponding multi-hop routing algorithm from a pre-set multi-hop routing algorithm library according to the transmission priority, wherein the multi-hop routing algorithm library stores multi-hop routing algorithms corresponding to different transmission priorities, a high priority corresponds to a greedy routing algorithm based on link quality, a secondary priority corresponds to a hybrid routing algorithm based on distance and residual energy, and a low priority corresponds to a load-balanced routing algorithm.

[0121] It should be noted that the to-be-transmitted data generated by the robot can be divided into three categories, and the transmission delay tolerance of different data types is different: 1. Real-time control instructions, such as obstacle avoidance interaction instructions with other robots and emergency stop signals, with a transmission delay tolerance of less than 100 ms; 2. Task scheduling information, such as task allocation and path adjustment instructions issued by the management system or scheduling center, with a transmission delay tolerance of less than 500 ms; 3. Environmental perception data, such as laser radar scan maps and cargo recognition images, with a transmission delay tolerance of 1-2 s. The transmission delay tolerance is inversely proportional to the transmission priority, so the transmission priority of the real-time control instructions is set to the highest level to ensure that the control instructions can be transmitted with the shortest delay and the highest reliability, ensuring the real-time and accuracy of the carrying robot operation; the transmission priority of the task scheduling information is set to the second highest level to ensure that the task allocation and adjustment information can be transmitted in time, maintaining the coordination and efficiency of the overall carrying task; and the transmission priority of the environmental perception data is set to the lowest level to complete the collection and transmission of environmental information without affecting real-time control and task scheduling.

[0122] It can be understood that the multi-hop routing algorithm library pre-stores multi-hop routing algorithms for different transmission priorities. When the management system determines the transmission priority according to the transmission delay tolerance, it quickly calls the algorithm matching the priority from the algorithm library. For example, for high-priority data such as real-time control instructions, the system calls a greedy routing algorithm based on link quality, which evaluates the link quality of each node in the network in real time, and preferentially selects a short path with stable signal and reliable transmission for data transmission, thereby ensuring that the control instruction reaches the target robot in the shortest time and with the highest accuracy. For task scheduling information of secondary priority data, the system uses a hybrid routing algorithm based on distance and residual energy, which considers the distance between nodes and combines the residual energy of the nodes to select relay nodes that can ensure transmission efficiency and prolong the overall life of the network. For low-priority environmental sensing data, the system uses a load balancing routing algorithm that dynamically monitors the load of each node in the network and evenly distributes data transmission tasks to nodes with lower load, avoiding concentrated bandwidth occupation of a node, thereby improving the overall transmission efficiency and stability of the network. Through this flexible use of multi-hop routing algorithms according to data type and transmission priority, the system can ensure that various types of data are transmitted with optimal paths and highest efficiency during the operation of the transport robot.

[0123] Step S50: Obtain the position information, power information and load state uploaded by each transport robot through the mesh network, and select the optimal data transmission path through the multi-hop routing algorithm according to the position information, power information, load state and dynamic neighbor list.

[0124] It should be noted that the transport robot uploads its own position information in real time through the mesh network. The position information can clearly indicate the specific coordinates of the robot in the field, allowing the management system to accurately grasp its distribution and movement trajectory in the field. The power information reflects the current energy reserve of the robot, which is crucial for reasonable task arrangement and avoiding task interruption due to power depletion. The load state refers to the weight or volume of the goods currently carried by the robot, which helps the management system understand the workload of the robot to more reasonably allocate tasks. After obtaining this information, the management system will combine the dynamic neighbor list to select the optimal data transmission path through the multi-hop routing algorithm. The dynamic neighbor list records the neighbor node information currently connectable by each robot, including the ID, signal strength, load status, etc. The multi-hop routing algorithm will consider these factors, as well as the priority and characteristics of the data to be transmitted, to plan an efficient and reliable transmission path.

[0125] Specifically, the multi-hop routing algorithm prioritizes neighboring nodes with high signal strength and low load as relays to ensure data stability and speed during transmission. Simultaneously, the algorithm dynamically adjusts the transmission path based on the robot's location information and dynamic neighbor list to adapt to changes in the environment and the robot's movement needs. For example, when a robot moves to a new location, its dynamic neighbor list is updated accordingly, and the multi-hop routing algorithm replans the transmission path based on the new list information.

[0126] By employing a multi-hop routing algorithm based on location information, power levels, load status, and a dynamic neighbor list to select the optimal data transmission path, this method ensures that data is transmitted along the optimal path and with the highest efficiency during the handling operation of the robot. This not only improves the reliability and real-time performance of data transmission but also enhances the coordination and efficiency of the entire handling robot system.

[0127] Step S60: Transmit the data to be transmitted between each handling robot through the optimal data transmission path.

[0128] It should be noted that in this embodiment, a media access control mechanism combining Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) is used during data transmission to avoid signal conflicts and ensure the real-time performance of high-priority data. The TDMA mechanism allocates fixed time slots for high-priority data, while the CSMA mechanism is used for contention-based access of low-priority data in non-fixed time slots. Furthermore, when high-priority data needs to be transmitted, it can preempt the transmission resources of low-priority data.

[0129] Time Division Multiple Access (TDMA) ensures that high-priority data is transmitted conflict-free within a predetermined time by pre-allocating specific time slices for each handling robot or data stream. This mechanism is particularly suitable for scenarios requiring strict timing control, such as the transmission of real-time control commands, and can avoid data loss or delays caused by signal collisions. For example, the management system can allocate independent time slices for emergency stop commands to each handling robot, ensuring that at any time when an emergency stop command needs to be sent, the command reaches the target robot accurately within the allocated time slice, thereby ensuring the safety of operations within the facility.

[0130] The carrier sense multiple access (CSMA) mechanism allows low-priority data to compete for access by listening to the channel state in non-fixed time slots. When the channel is idle, data can be sent immediately; if the channel is occupied, wait for a period of time and try again. This mechanism improves the utilization of network resources, and is particularly suitable for the transmission of non-critical data such as environmental sensing data. For example, when multiple handling robots are simultaneously uploading laser radar scan maps, the CSMA mechanism can allow each robot to dynamically adjust the transmission opportunity according to the channel state, avoiding data conflicts caused by simultaneous transmission, thereby achieving effective transmission of environmental sensing data without affecting the transmission of high-priority data.

[0131] When high-priority data needs to be transmitted, the mechanism combining TDMA and CSMA allows high-priority data to preempt the transmission resources of low-priority data. This means that if high-priority data (such as real-time control instructions) encounters a situation where the channel is occupied by low-priority data while waiting to transmit under the CSMA mechanism, the high-priority data can immediately interrupt the transmission of low-priority data and take priority to occupy the channel for transmission. This preemption mechanism ensures the real-time and reliability of high-priority data, even in the case of high network load, it can ensure the timely transmission of critical instructions.

[0132] Through the medium access control mechanism combining time division multiple access (TDMA) and carrier sense multiple access (CSMA), the embodiment not only guarantees the real-time of high-priority data, but also improves the transmission efficiency of low-priority data, thereby optimizing the data transmission performance of the entire handling robot system.

[0133] Specifically, by adopting the hybrid mechanism of TDMA and CSMA, the time is divided into 10ms time slots, and fixed time slots are allocated for real-time control instructions, such as each robot occupying 1 exclusive time slot to ensure conflict-free transmission; task scheduling information and environmental sensing data compete for the remaining time slots through the CSMA mechanism, and when the channel is detected to be idle, the data is transmitted, and if a conflict is detected, the data is retransmitted after random backoff. When high-priority data bursts, the time slot preemption mechanism can be triggered to suspend the transmission of low-priority data and prioritize the delivery of critical instructions.

[0134] It is worth noting that during data transmission, there is also a routing reselection mechanism. In the operation of the mesh network, each node monitors the link quality in real time, such as packet loss rate and delay, and triggers routing reselection when the following conditions occur: ① 3 consecutive transmission failures; ② neighbor node heartbeat signal loss for more than 2 seconds; ③ link quality index (LQ) is less than 60 (full score is 100). When reselecting, the node recalculates the optimal path to the target node based on the latest neighbor list and link quality data, and the entire process is completed within 50ms, ensuring uninterrupted communication, strong fault tolerance, and ensuring continuous communication of the robot cluster in complex environments.

[0135] The management system or dispatch center can access any node in the mesh network through wired or wireless means, such as a relay node fixed at the edge of the site, and can issue task instructions to all robots, such as "robot A moves goods from shelf 3 to sorting table 2", and receive position, power, load status and other data uploaded by each robot. Based on these data, global path planning is performed to avoid robot congestion, and the adjusted path is synchronized to the relevant robots in real time through the mesh network, realizing global monitoring and dispatching, reducing the dependence on fixed communication infrastructure, reducing the deployment cost in the field, and being suitable for the scene of dynamic movement of robots.

[0136] It is worth noting that all data transmitted through the mesh network is encrypted using the AES-128 encryption algorithm, and the node needs to be authenticated by a pre-set key before joining the network to prevent unauthorized devices from accessing the network. In addition, the encryption key is also periodically changed during data transmission to enhance the security of data transmission. AES-128 encryption algorithm has high security and encryption efficiency, which can effectively protect sensitive data in the handling robot system, such as real-time control instructions, task scheduling information and environmental perception data, to prevent data from being stolen or tampered with during transmission. The pre-set key authentication mechanism before the node joins the network further ensures that only authorized devices can access the mesh network, avoiding interference and security threats caused by the access of illegal devices. The combination of encryption and authentication mechanisms provides reliable security protection for the in-field handling robot system based on mesh communication.

[0137] The embodiment provides a communication method of an indoor carrying robot based on mesh communication, and a plurality of carrying robots in an indoor working area construct a mesh network, wherein the mesh network is composed of a plurality of carrying robots as communication nodes and is connected with each other, each carrying robot is provided with an independent mesh communication module; each carrying robot broadcasts to determine the distance between nodes in the mesh network and construct a dynamic neighbor list; a carrying task is acquired, the carrying task is decomposed into a plurality of task instructions, the plurality of task instructions are distributed to corresponding carrying robots through the mesh network, so that each carrying robot executes a carrying operation according to the received task instruction; in the case that data transmission is required in the process of carrying operation of the carrying robot, the transmission priority is determined according to the data type of the to-be-transmitted data, and the corresponding multi-hop routing algorithm is called according to the transmission priority; the position information, the power information and the load state uploaded by each carrying robot through the mesh network are acquired, and the optimal data transmission path is selected through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list; and the to-be-transmitted data is transmitted between each carrying robot through the optimal data transmission path. Through the above method, the integrity of network coverage is improved by introducing a robot self-organizing mesh network, the multi-hop routing algorithm is called according to the data transmission priority to plan a data transmission path, the fault tolerance is high, the continuous communication of the robot cluster in a complex environment is ensured, the communication stability and the fault tolerance of the indoor carrying robot are effectively improved, and the working efficiency and the reliability of the robot cluster are further ensured.

[0138] Based on the first embodiment of the application, in the second embodiment of the application, the same or similar contents as the above embodiment one can refer to the above introduction, and the subsequent will not be described in detail. On this basis, please refer to Figure 2 , and step S50 includes steps S501-S503:

[0139] Step S501: acquiring position information, power information and load state uploaded by each carrying robot through the mesh network.

[0140] It should be noted that each carrying robot uploads its position information, power information, and load state and other key data to the mesh network in real time through the built-in mesh communication module. Among them, the position information is obtained through high-precision positioning technology, which can accurately reflect the specific coordinates and movement trajectory of the robot in the field; the power information is monitored in real time through the power sensor built-in the robot, ensuring that the management system can accurately master the energy reserve of the robot; the load state information is obtained through the weighing sensor or volume measuring device carried by the robot, which helps the management system to understand the current workload of the robot. After these information is uploaded to the mesh network, it will be received and processed by the management system.

[0141] Step S502: generating an initial data transmission path according to the position information, the power information, the load state, and the dynamic neighbor list through the multi-hop routing algorithm.

[0142] It should be noted that the multi-hop routing algorithm can be a greedy routing algorithm based on link quality, a hybrid routing algorithm based on distance and residual energy, or a load balancing routing algorithm. These algorithms will consider the position information, power information, load state, and dynamic neighbor list of the carrying robot when generating the initial data transmission path. For example, the greedy routing algorithm based on link quality will preferentially select the neighbor node with the best link quality for data transmission to ensure the stability of data transmission; the hybrid routing algorithm based on distance and residual energy will consider the distance between nodes while taking into account the remaining power of the robot to avoid data transmission interruption due to insufficient power; the load balancing routing algorithm will reasonably allocate data transmission tasks according to the load state of each node to prevent some nodes from being overloaded and affecting the overall transmission efficiency. Through these multi-hop routing algorithms, an efficient and reliable initial data transmission path can be generated.

[0143] In a feasible implementation, step S502 can include: in the case where the multi-hop routing algorithm is a greedy routing algorithm based on link quality, selecting the carrying robot closest to the data sending end as the initial transmission node according to the position information of each carrying robot; obtaining the link quality information of the initial transmission node and its surrounding neighbor nodes through the mesh communication module of the initial transmission node, wherein the link quality information includes signal strength, packet loss rate, and transmission delay; selecting the node with the best link quality from the neighbor nodes as the next hop transmission node according to the link quality information; repeating the process of selecting the next hop transmission node until the data is transmitted to the target carrying robot, generating the initial data transmission path.

[0144] It should be noted that when the data to be transmitted is high-priority data such as real-time control instructions, a greedy routing algorithm based on link quality (LQ) is adopted to prioritize the stability and real-time performance of data transmission. First, according to the position information of each carrying robot, the robot closest to the data sending end is selected as the initial transmission node. Then, through the mesh communication module of the initial node, the link quality data of its neighboring nodes is collected, including signal strength, packet loss rate, and transmission delay. Based on these link quality data, the algorithm selects the node with the best link quality from the neighboring nodes as the next hop node for data transmission. This process continues, i.e., the next hop transmission node is selected continuously until the data successfully reaches the target carrying robot, thereby constructing an initial data transmission path. This path selection method is particularly effective for high-priority data transmission, minimizing transmission delay and packet loss to ensure timely and accurate transmission of critical instructions.

[0145] It can be understood that selecting the node with the best link quality from the neighboring nodes as the next hop transmission node includes quantitatively evaluating the collected link quality information, weighting the signal strength, packet loss rate, and transmission delay according to the preset weights, and calculating the comprehensive link quality score of each neighboring node. For example, the weight of signal strength can be set to 0.4, the weight of packet loss rate to 0.3, and the weight of transmission delay to 0.3. Then, according to the actual signal strength value, packet loss rate value, and transmission delay value of each neighboring node, the comprehensive score is calculated by substituting the values into the weighting formula. Next, the neighboring nodes are sorted in descending order of comprehensive link quality score, and the neighboring node with the highest score is selected as the next hop transmission node. If there are multiple neighboring nodes with the same score, further secondary selection is performed according to other auxiliary indicators such as the historical transmission success rate of the node, the remaining bandwidth, etc., to ensure that the node with the best link quality is selected. In this way, the node with the best link quality can be more scientifically and accurately selected from the neighboring nodes as the next hop transmission node, thereby effectively improving the stability and reliability of data transmission.

[0146] In a feasible implementation, step S502 can further include: in the case where the multi-hop routing algorithm is a hybrid routing algorithm based on distance and residual energy, calculating the second distances from the data sending end to all potential next hop nodes according to the position information of each carrying robot; obtaining the residual energy information and the current load state of each potential next hop node; comprehensively evaluating each potential next hop node according to the second distances, the residual energy, and the load state to determine the comprehensive score of each potential next hop node; selecting the potential next hop node with the highest comprehensive score as the next hop transmission node; repeating the process of selecting the next hop transmission node until the data is transmitted to the target carrying robot, generating an initial data transmission path.

[0147] It should be noted that when the data to be transmitted is task scheduling information or other secondary priority data, a hybrid routing algorithm based on distance and residual energy is adopted, which comprehensively considers the distance from the data sending end to the potential next hop node, the residual energy of each potential next hop node, and the current load state. First, according to the position information of each transport robot, the second distance from the data sending end to all potential next hop nodes is accurately calculated. At the same time, the residual energy information of each potential next hop node is obtained through the mesh communication module, which reflects the current energy reserve of the node. In addition, the current load state of each node needs to be obtained to understand the working busy degree of the node. According to the second distance, the residual energy and the load state, each potential next hop node is comprehensively evaluated, and different weights can be set for the distance, the residual energy and the load state in the evaluation process to reflect their relative importance in data transmission. For example, the node with shorter distance, sufficient residual energy and lighter load may obtain a higher comprehensive score. Through this comprehensive evaluation method, the comprehensive score of each potential next hop node is determined. Finally, the potential next hop node with the highest comprehensive score is selected as the next hop transmission node, and the selection process is repeated until the data is successfully transmitted to the target transport robot, thereby generating an initial data transmission path. This hybrid routing algorithm based on distance and residual energy can balance the transmission efficiency and node resource utilization when transmitting secondary priority data, ensuring the reliability and stability of data transmission.

[0148] In a feasible implementation, step S502 can further include: in the case that the multi-hop routing algorithm is a load balancing routing algorithm, according to the load state of each transport robot, a transport robot with lighter current load is preferentially selected as an initial transmission node; the load state of neighbor nodes around the initial transmission node is obtained, and the load balancing coefficient of each neighbor node is calculated in combination with the position information of each neighbor node and the initial transmission node; according to the load balancing coefficient, a node with the optimal load balancing coefficient (i.e., the relative lightest load and suitable position) is selected from the neighbor nodes as the next hop transmission node; the process of selecting the next hop transmission node is repeated, and the load state of each node is continuously dynamically evaluated during the transmission process, and the transmission path is timely adjusted until the data is transmitted to the target transport robot, thereby generating an initial data transmission path.

[0149] It should be noted that the load balancing coefficient is an important parameter for comprehensively considering the load and position relationship of the node, which is as follows:

[0150]

[0151] wherein, is the load balancing coefficient, is the load of the neighbor node i, indicating the current task workload or load of the neighbor node i, is the load of the initial transmission node, is the distance between the initial transmission node and the neighbor node i, is the load coefficient, is the distance coefficient.

[0152] It can be understood that the load coefficient and the distance coefficient can be set according to actual scene requirements and experience, for example, the load coefficient can be set to 0.6 and the distance coefficient can be set to 0.4. Through such setting, when calculating the load balancing coefficient, both the load condition of the node and the distance factor between the node and the initial transmission node can be considered.

[0153] According to the calculated load balancing coefficient, the node with the optimal load balancing coefficient, that is, the node with the relatively lightest load and the most appropriate position, is selected from the neighbor nodes as the next hop transmission node. Then the process of selecting the next hop transmission node is repeated, and the load state of each node is continuously dynamically evaluated during the transmission process. Because the load condition of each node may change as the data transmission proceeds, the transmission path needs to be adjusted in a timely manner until the data is successfully transmitted to the target transport robot, thereby generating an initial data transmission path. This load balancing routing algorithm can effectively avoid that some nodes are overloaded and affect the overall transmission efficiency, and ensure smooth data transmission.

[0154] Step S503: optimizing the initial data transmission path based on the improved discrete whale algorithm to generate an optimal data transmission path.

[0155] It should be noted that the improved discrete whale algorithm is a kind of optimization algorithm based on swarm intelligence, which is inspired by the hunting behavior of whale groups. In the data transmission path optimization problem, the algorithm iteratively optimizes the initial data transmission path by simulating the search and surrounding mechanism of the whale group. Specifically, the algorithm first encodes the initial data transmission path as the position information of a whale individual, and each whale individual represents a possible data transmission path scheme. Then, the algorithm defines a fitness function according to the transmission efficiency, stability and other indicators of the path, which is used to evaluate the pros and cons of each whale individual. In the iteration process, the whale group constantly updates its position information, i.e., adjusts the data transmission path scheme, to gradually approach the optimal solution.

[0156] The improved discrete whale optimization algorithm introduces multiple strategies in the optimization process to improve the convergence speed and global search ability of the algorithm. For example, the algorithm adopts a discretized position updating method to adapt to the discrete characteristics of the data transmission path. At the same time, an inertia weight and an adaptive adjustment mechanism are introduced to enable the algorithm to maintain a larger search step size in the early search stage, quickly approach the optimal solution region, and reduce the search step size in the later search stage for fine search. In addition, the algorithm introduces the simulated annealing idea to avoid falling into local optimal solutions and improve the global search ability of the algorithm.

[0157] By improving the optimization of the discrete whale optimization algorithm, the redundant nodes and inefficient transmission segments in the initial data transmission path are effectively eliminated, generating an optimal data transmission path. This path achieves optimal state in terms of transmission efficiency, stability, and fault tolerance, ensuring that the indoor transport robot can achieve efficient and reliable communication in complex environments. At the same time, the generation of the optimal data transmission path also provides a strong guarantee for the efficiency and reliability of the robot cluster.

[0158] In one possible implementation, step S503 can include: encoding the initial data transmission path into whale individual positions and initializing a whale population based on the whale individual positions through a genetic algorithm, each whale individual position in the whale population containing multiple dimensions, each dimension value representing a node number on the data transmission path; calculating the fitness value of each whale individual in the whale population, the fitness value being calculated according to the total transmission delay, total packet loss rate of the path, and power consumption and load state of the transport robot; selecting the optimal whale individual in the whale population as the leader according to the fitness value, and updating the positions of the whale population to obtain an updated whale population, wherein the position updating process includes leader-guided updating and random walk updating, the leader-guided updating refers to other whale individuals learning from the leader to calculate a new position according to the leader position and the current position of the individual, and the random walk updating refers to the whale individual randomly selecting a direction to move within the search space; recalculating the fitness value of the updated whale population and determining whether a preset optimization termination condition is met; if the preset optimization termination condition is not met, continuing to iteratively perform the steps of calculating the fitness value, selecting the optimal whale individual, updating the positions of the whale population, and recalculating the fitness value until the preset optimization termination condition is met, wherein the preset optimization termination condition includes reaching a preset maximum number of iterations or the fitness value of all whale individuals in the whale population changing by less than a preset threshold in consecutive iterations; and if the preset optimization termination condition is met, decoding the position of the whale individual with the highest current fitness value into the optimal data transmission path.

[0159] It should be noted that the introduction of genetic algorithm provides a more diverse and effective starting point for the initialization of whale population. By encoding the initial data transmission path through genetic algorithm and generating the position of whale individuals, the whale population can cover a wider area in the search space, thereby increasing the possibility of finding the global optimal solution.

[0160] When calculating the fitness value of whale individuals, multiple factors such as total transmission delay, total packet loss rate, and power consumption and load state of the transport robot are considered comprehensively. Such setting makes the fitness value more comprehensively reflect the pros and cons of data transmission path. The calculation formula of fitness function is:

[0161]

[0162] wherein, , , , are the weights of total transmission delay, total packet loss rate, power consumption, and load state, and are the numbers of consecutive nodes in the path, represents the transmission time from node to node , represents the packet loss rate from node to node , represents the power consumption from node to node , indicates the load state of node .

[0163] The position updating method combining leader-guided updating and random walk updating not only ensures the directionality of whale population in the search process, but also increases the randomness and globality of the search. Leader-guided updating enables other whale individuals to learn from the leader with higher fitness value, thereby gradually approaching the optimal solution; while random walk updating enables whale individuals to perform random exploration in the search space, avoiding the dilemma of falling into local optimal solution. This hybrid updating strategy effectively balances the exploration and exploitation ability of the algorithm, making the improved discrete whale algorithm perform well in data transmission path optimization.

[0164] In the position updating process, the new position that exceeds the feasible range or violates the dynamic neighbor list constraint is regenerated, ensuring the feasibility and effectiveness of the path, which avoids the generation of invalid path, thereby improving the overall transmission efficiency.

[0165] When the preset optimization termination condition is met, such as reaching the maximum number of iterations or the fitness value variation amplitude being less than the preset threshold, the algorithm will stop iteration and decode the current fitness value highest whale individual position as the optimal data transmission path. This process ensures that the algorithm can find a solution close to the global optimum within a reasonable time.

[0166] In addition, by introducing a genetic algorithm for whale population initialization and considering multiple factors to calculate the fitness value of the whale individual, the improved discrete whale algorithm shows higher flexibility and adaptability in data transmission path optimization, and can adjust algorithm parameters and fitness functions according to different application scenarios and requirements to find the most suitable data transmission path for the current environment.

[0167] In summary, the data transmission path optimization method based on the improved discrete whale algorithm effectively eliminates redundant nodes and inefficient transmission segments in the initial path by simulating the hunting behavior of the whale population and combining multiple optimization strategies, generating the optimal data transmission path. This method not only improves the efficiency and stability of data transmission, but also provides efficient and reliable communication for the indoor handling robot in complex environments.

[0168] In this embodiment, by introducing a multi-hop routing algorithm based on transmission priority, an efficient and reliable initial data transmission path can be generated, and then the improved discrete whale algorithm-based data transmission path optimization effectively eliminates redundant nodes and inefficient transmission segments in the initial path, generating the optimal data transmission path, effectively improving the efficiency and stability of data transmission.

[0169] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the mesh communication-based indoor handling robot communication method of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0170] The present application also provides a mesh communication-based indoor handling robot communication device, please refer to Figure 3 The mesh communication-based indoor handling robot communication device comprises:

[0171] The construction module 10 is configured to construct a mesh network based on a plurality of handling robots in an indoor working area, wherein the mesh network is composed of a plurality of handling robots connected to each other as communication nodes, and each handling robot is configured with an independent mesh communication module.

[0172] The construction module 10 is further configured to control each handling robot to broadcast respectively, determine the distance between each node in the mesh network, and construct a dynamic neighbor list.

[0173] The delivery module 20 is configured to acquire a carrying task, decompose the carrying task into a plurality of task instructions, and deliver the plurality of task instructions to corresponding carrying robots through the mesh network, so that each carrying robot performs a carrying operation according to the received task instructions.

[0174] The determination module 30 is configured to determine a transmission priority according to a data type of the data to be transmitted when the carrying robot needs to transmit data during the carrying operation, and call a corresponding multi-hop routing algorithm according to the transmission priority.

[0175] The selection module 40 is configured to acquire position information, power information and load states uploaded by each carrying robot through the mesh network, and select an optimal data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load states and the dynamic neighbor list.

[0176] The transmission module 50 is configured to transmit the data to be transmitted between the carrying robots through the optimal data transmission path.

[0177] The in-field carrying robot communication device based on mesh communication provided by the present application adopts the in-field carrying robot communication method based on mesh communication in the above embodiment, and can solve the technical problems of poor stability and weak fault tolerance of the in-field carrying robot communication in the prior art. Compared with the prior art, the in-field carrying robot communication device based on mesh communication provided by the present application has the same beneficial effects as the in-field carrying robot communication method based on mesh communication provided by the above embodiment, and other technical features in the in-field carrying robot communication device based on mesh communication are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0178] The above is only some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the present application specification and drawings, or direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. A communication method for an in-field transport robot based on mesh communication, characterized in that, The method comprises: constructing a mesh network based on a plurality of carrying robots in an indoor working area, wherein the mesh network is composed of a plurality of carrying robots connected to each other as communication nodes, and each carrying robot is configured with an independent mesh communication module; controlling each carrying robot to broadcast respectively to determine the distance between each node in the mesh network and to construct a dynamic neighbor list; obtaining a carrying task, decomposing the carrying task into a plurality of task instructions, and issuing the plurality of task instructions to the corresponding carrying robots through the mesh network, so that each carrying robot executes a carrying operation according to the received task instructions; in the case that data transmission is required during the carrying operation of the carrying robot, determining the transmission priority according to the data type of the data to be transmitted, and calling the corresponding multi-hop routing algorithm according to the transmission priority; obtaining the position information, power information and load state uploaded by each carrying robot through the mesh network, and selecting an optimal data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list; transmitting the data to be transmitted between each carrying robot through the optimal data transmission path; the obtaining the position information, power information and load state uploaded by each carrying robot through the mesh network, and selecting an optimal data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list, comprises: obtaining the position information, power information and load state uploaded by each carrying robot through the mesh network; generating an initial data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list; optimizing the initial data transmission path based on an improved discrete whale algorithm to generate an optimal data transmission path; the generating an initial data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list, comprises: in the case that the multi-hop routing algorithm is a greedy routing algorithm based on link quality, selecting the carrying robot closest to the data sending end as the initial transmission node according to the position information of each carrying robot; obtaining the link quality information of the initial transmission node and the surrounding neighbor nodes through the mesh communication module of the initial transmission node, wherein the link quality information includes signal strength, packet loss rate and transmission delay; selecting the node with the optimal link quality from the neighbor nodes as the next hop transmission node according to the link quality information; repeating the process of selecting the next hop transmission node until the data is transmitted to the target carrying robot to generate an initial data transmission path; the generating an initial data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load state and the dynamic neighbor list, further comprises: In the case that the multi-hop routing algorithm is a hybrid routing algorithm based on distance and residual energy, according to the position information of each transport robot, the second distance from the data sending end to all potential next hop nodes is calculated; Obtain the residual capacity information and the current load state of each potential next hop node; According to the second distance, the residual capacity and the load state, each potential next hop node is comprehensively evaluated to determine the comprehensive score of each potential next hop node; Select the potential next hop node with the highest comprehensive score as the next hop transmission node; Repeat the process of selecting the next hop transmission node until the data is transmitted to the target transport robot, and generate an initial data transmission path.

2. The method of claim 1, wherein, The control each of the transport robots broadcasts to determine the distance between each node in the mesh network and construct a dynamic neighbor list, including: Select any transport robot as a sending node from a plurality of the transport robots, and select other transport robots as other nodes; Through the mesh communication module of the sending node, a beacon frame containing its own ID, position coordinates and current task state information is broadcasted to the surrounding; After the other nodes receive the beacon frame, the first distance between the sending node and the other nodes is calculated based on the signal strength of the beacon frame; When the first distance is less than a preset threshold, the corresponding other node is regarded as a neighbor node, and the neighbor list of the sending node is updated according to the ID, position coordinates, task state information and first distance of the sending node; After completing one round of broadcasting and neighbor list updating, the next transport robot is selected as a new sending node, and the broadcasting and distance calculation process is repeated until all transport robots complete a round of broadcasting and neighbor list updating as sending nodes, and a complete dynamic neighbor list is generated.

3. The method of claim 1, wherein, The transport task is obtained, the transport task is decomposed into a plurality of task instructions, and the plurality of task instructions are distributed to the corresponding indoor transport robots through the mesh network, so that each indoor transport robot executes the transport operation according to the received task instructions, including: Obtain the transport task and determine the transport information, wherein the transport information includes the starting position, the target position and the transport object information; Analyze the transport information to determine the transport steps required to complete the transport task, and convert each transport step into a corresponding task instruction; According to the position information of each transport robot and the requirements of the task instructions, each task instruction is distributed to the corresponding transport robot through the mesh network, so that each transport robot executes the transport operation according to the received task instructions.

4. The method of claim 3, wherein, After the position information of each transport robot and the requirements of the task instructions are obtained, each task instruction is distributed to the corresponding transport robot through the mesh network, so that each transport robot executes the transport operation according to the received task instructions, including: Obtain the real-time position information, moving speed and task execution progress of each transport robot during the transport operation process; According to the real-time position information, moving speed and task execution progress, the position state of each transport robot at a future time point is estimated; determine whether a conflict exists between the transport robots based on the position state at the future time point; in the case where a conflict exists, determine a conflict type according to the position state at the future time point, wherein the conflict type includes a catch-up conflict, an intersection conflict, a face-to-face conflict, and a failure conflict; generate a corresponding conflict resolution strategy according to the conflict type, wherein the conflict resolution strategy at least includes adjusting a moving speed, changing a moving path, pausing task execution, or reassigning a task instruction; issue the conflict resolution strategy to the transport robots involved in the conflict through the mesh network, so that each transport robot adjusts the transport operation according to the received conflict resolution strategy; monitor the execution of the conflict resolution strategy in real time, and dynamically adjust according to the execution effect until the conflict is completely resolved.

5. The method of claim 1, wherein, In the case where data needs to be transmitted during the execution of the transport operation of the transport robot, the transmission priority is determined according to the data type of the data to be transmitted, and a corresponding multi-hop routing algorithm is called according to the transmission priority, including: In the case where data needs to be transmitted during the execution of the transport operation of the transport robot, the data to be transmitted of the transport robot is obtained, and the data type of the data to be transmitted is determined, wherein the data type includes one or more of real-time control instructions, task scheduling information, and environmental perception data; determine the transmission delay tolerance according to the data type of the data to be transmitted, wherein the transmission delay tolerance of the real-time control instruction is the lowest, the transmission delay tolerance of the environmental perception data is the highest, and the transmission delay tolerance of the task scheduling information is between the real-time control instruction and the environmental perception data; determine the transmission priority according to the transmission delay tolerance, wherein the transmission delay tolerance is inversely proportional to the transmission priority; call a corresponding multi-hop routing algorithm from a preset multi-hop routing algorithm library according to the transmission priority, wherein the multi-hop routing algorithm library stores multi-hop routing algorithms corresponding to different transmission priorities, a high priority corresponds to a greedy routing algorithm based on link quality, a secondary priority corresponds to a hybrid routing algorithm based on distance and residual energy, and a low priority corresponds to a load balancing routing algorithm.

6. The method of claim 1, wherein, The initial data transmission path is optimized based on the improved discrete whale algorithm to generate an optimal data transmission path, including: encoding the initial data transmission path as a whale individual position, and initializing a whale population based on the whale individual position through a genetic algorithm, wherein each whale individual position in the whale population contains multiple dimensions, and each dimension value represents a node number on the data transmission path; calculate the fitness value of each whale individual in the whale population, wherein the fitness value is calculated according to the total transmission delay, the total packet loss rate, and the power consumption and load state of the transport robot; The optimal whale individual in the whale population is selected as a leader according to the fitness value, and the position of the whale population is updated to obtain an updated whale population, wherein the position updating process includes leader-guided updating and random walk updating; the leader-guided updating refers to that other whale individuals learn from the leader, and a new position is calculated according to the position of the leader and the current position of the whale individual; the random walk updating refers to that the whale individual randomly selects a direction to move in the search space; in the position updating process, if the new position corresponds to a path node that is out of the feasible range or violates the dynamic neighbor list constraint, a new position that meets the constraint condition is regenerated; The fitness value of the updated whale population is calculated again, and it is determined whether a preset optimization termination condition is met; If the preset optimization termination condition is not met, the steps of calculating the fitness value, selecting the optimal whale individual, updating the position of the whale population, and calculating the fitness value again are iteratively executed until the preset optimization termination condition is met, wherein the preset optimization termination condition includes reaching a preset maximum number of iterations, or the fitness value of all whale individuals in the whale population changes by less than a preset threshold in consecutive iterations; If the preset optimization termination condition is met, the position of the whale individual with the highest current fitness value is decoded as an optimal data transmission path.

7. An in-field transport robot communication device based on mesh communication, characterized in that, The in-site carrying robot communication device based on mesh communication includes: A construction module is configured to construct a mesh network based on a plurality of carrying robots in an in-site working area, wherein the mesh network is composed of a plurality of carrying robots as communication nodes connected to each other, and each carrying robot is configured with an independent mesh communication module; The construction module is further configured to control each carrying robot to broadcast respectively to determine the distance between each node in the mesh network and construct a dynamic neighbor list; A delivery module is configured to obtain a carrying task, decompose the carrying task into a plurality of task instructions, and distribute the plurality of task instructions to the corresponding carrying robots through the mesh network, so that each carrying robot performs a carrying operation according to the received task instructions; A determination module is configured to determine a transmission priority according to the data type of the data to be transmitted if the carrying robot needs to transmit data during the carrying operation, and call a corresponding multi-hop routing algorithm according to the transmission priority; A selection module is configured to obtain position information, power information, and load status uploaded by each carrying robot through the mesh network, and select an optimal data transmission path through the multi-hop routing algorithm according to the position information, the power information, the load status, and the dynamic neighbor list; A transmission module is configured to transmit the data to be transmitted between the carrying robots through the optimal data transmission path. The selection module is configured to acquire position information, power information, and load states uploaded by each of the transfer robots via the mesh network, generate an initial data transmission path via the multi-hop routing algorithm based on the position information, the power information, the load states, and the dynamic neighbor list, and optimize the initial data transmission path based on an improved discrete whale optimization algorithm to generate an optimal data transmission path. In a case where the multi-hop routing algorithm is a greedy routing algorithm based on link quality, the selection module is further configured to select, according to the position information of each of the transfer robots, a transfer robot closest to a data sending end as an initial transmission node, acquire, via a mesh communication module of the initial transmission node, link quality information of the initial transmission node and surrounding neighbor nodes, where the link quality information includes signal strength, packet loss rate, and transmission delay, select, according to the link quality information, a node with optimal link quality from the neighbor nodes as a next-hop transmission node, and repeat the process of selecting the next-hop transmission node until data is transmitted to a target transfer robot to generate the initial data transmission path. In a case where the multi-hop routing algorithm is a hybrid routing algorithm based on distance and residual energy, the selection module is further configured to calculate, according to the position information of each of the transfer robots, second distances from a data sending end to all potential next-hop nodes, acquire residual power information and current load states of each of the potential next-hop nodes, comprehensively evaluate each of the potential next-hop nodes based on the second distances, the residual power, and the load states to determine a comprehensive score of each of the potential next-hop nodes, select a potential next-hop node with the highest comprehensive score as the next-hop transmission node, and repeat the process of selecting the next-hop transmission node until data is transmitted to the target transfer robot to generate the initial data transmission path.

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