In-field transfer robot communication method and device based on mesh communication

Abstract

The invention discloses an in-field transfer robot communication method and device based on mesh communication. The method comprises the following steps: constructing a mesh network; each transfer robot is controlled to broadcast respectively, the distance between nodes in the mesh network is determined, and a dynamic neighbor list is constructed; decomposing the carrying task into a plurality of task instructions, and issuing the task instructions to corresponding carrying robots through a mesh network; under the condition that data needs to be transmitted in the carrying process, 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; selecting an optimal data transmission path through a multi-hop routing algorithm according to the position information, the electric quantity information, the load state and the dynamic neighbor list of each transfer robot; and transmitting the to-be-transmitted data among the transfer robots through the optimal data transmission path. According to the invention, the communication stability and fault-tolerant capability of the in-field transfer robot can be effectively improved, and the operation efficiency and reliability of the robot cluster are guaranteed.

Description

Technical Field

[0001] This application relates to the field of robot communication technology, and in particular to a communication method and apparatus for in-field handling robots based on mesh communication. Background Technology

[0002] In warehouses, smart manufacturing workshops, and other on-site scenarios, the collaborative operation of multiple handling robots has become a key way to improve logistics efficiency. However, the on-site environment often has problems such as shelving obstruction, electromagnetic interference from equipment, and signal instability caused by robot movement. Traditional centralized wireless communication (such as single WiFi, base stations, Bluetooth) is prone to signal coverage blind spots or communication delays, affecting the collaborative efficiency of robot swarms.

[0003] In existing technologies, some solutions employ fixed-route wireless communication, but a failure in any node can lead to the interruption of the entire communication link. Other solutions rely on a central controller for data forwarding, which carries the risk of a single point of failure and is difficult to adapt to scenarios involving dynamic robot movement. Therefore, there is an urgent need for a communication method with self-organizing, interference-resistant, and highly fault-tolerant capabilities to meet the real-time collaborative needs of in-field handling robots. Summary of the Invention

[0004] The main purpose of this application is to provide a communication method and device for in-field handling robots based on mesh communication, aiming to solve the technical problems of poor stability and weak fault tolerance in the existing in-field handling robot communication.

[0005] To achieve the above objectives, this application proposes a mesh communication-based communication method for in-field transport robots. The mesh communication-based communication method for in-field transport robots includes: A mesh network is constructed based on multiple handling robots in the on-site work area. The mesh network consists of multiple handling robots connected to each other as communication nodes, and each handling robot is equipped with an independent mesh communication module. Each of the transport robots is controlled to broadcast, the distance between nodes in the mesh network is determined, and a dynamic neighbor list is constructed. The system acquires a handling task, breaks it down into multiple task instructions, and sends these instructions to the corresponding handling robots via the mesh network, so that each handling robot can perform handling operations according to the received task instructions. When the handling robot needs to transmit data during the handling 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. The system acquires the location information, power information, and load status of each transport robot uploaded through the mesh network, and selects the optimal data transmission path based on the location information, power information, load status, and dynamic neighbor list using the multi-hop routing algorithm. The data to be transmitted is transmitted between the transport robots through the optimal data transmission path.

[0006] In one embodiment, controlling each of the transport robots to broadcast, determining the distance between nodes in the mesh network, and constructing a dynamic neighbor list includes: Select any one of the multiple transport robots as the sending node, and use the other transport robots as other nodes; The sending node broadcasts a beacon frame containing its own ID, location coordinates, and current task status information to the surrounding area through the mesh communication module. After other nodes receive the beacon frame, they calculate the first distance between themselves and the sending node based on the signal strength of the beacon frame. When the first distance is less than a preset threshold, the corresponding other nodes are taken as neighbor nodes, and the neighbor list of the sending node is updated according to the ID, location coordinates, task status information and the first distance. After completing one broadcast and neighbor list update, the next transport robot is selected as the new sending node, and the broadcast and distance calculation process is repeated until all transport robots have completed one round of broadcast and neighbor list update as sending nodes, generating a complete dynamic neighbor list.

[0007] In one embodiment, acquiring the handling task involves decomposing the handling task into multiple task instructions, and distributing the multiple task instructions to the corresponding in-field handling robots through the mesh network, so that each in-field handling robot performs handling operations according to the received task instructions, including: Obtain the transportation task and determine the transportation information, wherein the transportation information includes the starting position, the target position, and the information of the transported item; The transport information is analyzed to determine the transport steps required to complete the transport task, and each transport step is converted into a corresponding task instruction. Based on the location information of each transport robot and the requirements of the task instructions, the task instructions are sent to the corresponding transport robots through the mesh network, so that each transport robot can perform transport operations according to the received task instructions.

[0008] In one embodiment, after the step of sending each task instruction to the corresponding handling robot through the mesh network according to the location information of each handling robot and the requirements of the task instruction, so that each handling robot performs the handling operation according to the received task instruction, the method further includes: The real-time location information, moving speed, and task execution progress of each of the aforementioned handling robots are obtained during the handling operation. Based on the real-time location information, moving speed, and task execution progress, the position status of each handling robot at a future time point is estimated. Determine whether there is a conflict between the handling robots based on their position status at the future time point; In the event of a conflict, the conflict type is determined based on the positional status at the future time point, wherein the conflict type includes chasing conflict, intersection conflict, face-to-face conflict, and failure conflict. Generate a corresponding conflict resolution strategy based on the conflict type, wherein the conflict resolution strategy includes at least adjusting movement speed, changing movement path, pausing task execution, or reassigning task instructions; The conflict resolution strategy is distributed to the handling robots involved in the conflict through the mesh network, so that each handling robot can adjust its handling operation according to the received conflict resolution strategy; Monitor the execution of conflict resolution strategies in real time and make dynamic adjustments based on the results until the conflict is completely resolved.

[0009] In one embodiment, when data needs to be transmitted during the handling operation of the handling robot, determining the transmission priority based on the data type of the data to be transmitted, and invoking the corresponding multi-hop routing algorithm according to the transmission priority, includes: When the handling robot needs to transmit data during the handling operation, the data to be transmitted by the handling robot is acquired, and the data type of the data to be transmitted is determined. The data type includes one or more of real-time control instructions, task scheduling information, and environmental perception data. The transmission delay tolerance is determined according to the data type of the data to be transmitted, wherein the transmission delay tolerance of the real-time control command 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 that of the real-time control command and the environmental perception data. Transmission priority is determined based on the transmission delay tolerance, wherein the transmission delay tolerance is inversely proportional to the transmission priority; According to the transmission priority, the corresponding multi-hop routing algorithm is called from the preset multi-hop routing algorithm library. The multi-hop routing algorithm library stores multi-hop routing algorithms corresponding to different transmission priorities. High priority corresponds to a greedy routing algorithm based on link quality, secondary priority corresponds to a hybrid routing algorithm based on distance and remaining energy, and low priority corresponds to a load balancing routing algorithm.

[0010] In one embodiment, the step of acquiring the location information, power information, and load status uploaded by each of the transport robots through the mesh network, and selecting the optimal data transmission path using the multi-hop routing algorithm based on the location information, power information, load status, and dynamic neighbor list, includes: Obtain the location information, power information, and load status uploaded by each of the transport robots through the mesh network; An initial data transmission path is generated using the multi-hop routing algorithm based on the location information, power information, load status, and dynamic neighbor list. The initial data transmission path is optimized based on the improved discrete whale algorithm to generate the optimal data transmission path.

[0011] In one embodiment, generating an initial data transmission path using the multi-hop routing algorithm based on the location information, the power information, the load status, and the dynamic neighbor list includes: When the multi-hop routing algorithm is a greedy routing algorithm based on link quality, the robot closest to the data sender is selected as the initial transmission node according to the location information of each handling robot. The link quality information between the initial transmission node and its surrounding neighboring nodes is obtained through the mesh communication module of the initial transmission node. The link quality information includes signal strength, packet loss rate, and transmission delay. Based on the link quality information, the node with the best link quality is selected from the neighboring nodes as the next-hop transmission node; Repeat the process of selecting the next hop transmission node until the data is transmitted to the target handling robot, thus generating the initial data transmission path.

[0012] In one embodiment, the step of generating an initial data transmission path using the multi-hop routing algorithm based on the location information, the power information, the load status, and the dynamic neighbor list further includes: When the multi-hop routing algorithm is a hybrid routing algorithm based on distance and remaining energy, the second distance from the data sender to all potential next-hop nodes is calculated according to the position information of each transport robot; Obtain the remaining battery power and current load status of each potential next-hop node; Based on the second distance, remaining power, and load status, each potential next-hop node is comprehensively evaluated to determine a comprehensive score for each potential next-hop node; Select the potential next-hop node with the highest overall 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 handling robot, thus generating the initial data transmission path.

[0013] In one embodiment, optimizing the initial data transmission path based on the improved discrete whale algorithm to generate the optimal data transmission path includes: The initial data transmission path is encoded as the location of an individual whale, and a whale population is initialized based on the location of the individual whale using a genetic algorithm. Each location of an individual whale in the whale population contains multiple dimensions, and each dimension value represents a node number on the data transmission path. The fitness value of each individual whale in the whale population is calculated based on the total transmission delay of the path, the total packet loss rate, and the power consumption and load status of the handling robot. The optimal whale individual in the whale population is selected as the leader based on the fitness value, and the position of the whale population is updated to obtain the updated whale population. The position update process includes leader-guided update and random walk update. Leader-guided update means that other whale individuals learn from the leader and calculate a new position based on the leader's position and their own current position. Random walk update means that whale individuals randomly select a direction to move in the search space. During the position update process, if the path node corresponding to the new position 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 recalculated, and it is determined whether the preset optimization termination condition is met. If the preset optimization termination condition is not met, the steps of calculating fitness value, selecting the best whale individual, updating the position of the whale population, and recalculating fitness value continue to be executed iteratively until the preset optimization termination condition is met. The preset optimization termination condition includes reaching the preset maximum number of iterations, or the change in fitness value of all whale individuals in the whale population is less than the preset threshold in several consecutive iterations. If the preset optimization termination condition is met, the location of the whale individual with the highest fitness value will be decoded as the optimal data transmission path.

[0014] Furthermore, to achieve the above objectives, this application also proposes a mesh communication-based communication device for an in-field transport robot, which includes: The building module is used to construct a mesh network based on multiple handling robots in the field work area. The mesh network consists of multiple handling robots connected to each other as communication nodes, and each handling robot is equipped with an independent mesh communication module. The construction module is also used to control each of the transport robots to broadcast, determine the distance between each node in the mesh network, and construct a dynamic neighbor list; The distribution module is used to acquire the handling task, decompose the handling task into multiple task instructions, and distribute the multiple task instructions to the corresponding handling robots through the mesh network, so that each handling robot can perform handling operations according to the received task instructions. The determination module is used to determine the transmission priority based on the data type of the data to be transmitted when the handling robot needs to transmit data during the handling operation, and to call the corresponding multi-hop routing algorithm according to the transmission priority. The selection module is used to obtain the location information, power information and load status of each of the transport robots uploaded through the mesh network, and select the optimal data transmission path according to the location information, power information, load status and dynamic neighbor list through the multi-hop routing algorithm; The transmission module is used to transmit the data to be transmitted between the transport robots through the optimal data transmission path.

[0015] This application proposes one or more technical solutions, which construct a mesh network based on multiple transport robots in a work area. The mesh network consists of multiple transport robots interconnected as communication nodes, each equipped with an independent mesh communication module. The system controls each transport robot to broadcast, determining the distance between nodes in the mesh network and constructing a dynamic neighbor list. It acquires transport tasks, decomposes these tasks into multiple task instructions, and distributes these instructions to the corresponding transport robots via the mesh network, enabling each robot to perform transport operations according to the received instructions. When data transmission is required during transport operations, the system determines the transmission priority based on the data type and invokes the corresponding multi-hop routing algorithm based on the priority. It acquires the location information, battery information, and load status uploaded by each transport robot through the mesh network and selects the optimal data transmission path using the multi-hop routing algorithm based on the location information, battery information, load status, and the dynamic neighbor list. Finally, it transmits the data between the transport robots via the optimal data transmission path. By introducing a self-organizing mesh network, the integrity of network coverage is improved. Data transmission path planning is performed by calling a multi-hop routing algorithm based on data transmission priority, which has strong fault tolerance and ensures continuous communication of the robot cluster in complex environments. This effectively improves the communication stability and fault tolerance of the in-field handling robots, while further ensuring the operation efficiency and reliability of the robot cluster. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the communication method for an in-field handling robot based on mesh communication in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the communication method for an in-field handling robot based on mesh communication provided in this application; Figure 3 This is a schematic diagram of the module structure of the communication device for an in-field handling robot based on mesh communication, as described in this application embodiment.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] This application provides a solution that improves network coverage integrity by introducing a self-organizing mesh network for robots. It uses a multi-hop routing algorithm to plan data transmission paths based on data transmission priority, resulting in strong fault tolerance and ensuring continuous communication of the robot cluster in complex environments. This effectively improves the communication stability and fault tolerance of in-field handling robots, while further ensuring the operational efficiency and reliability of the robot cluster.

[0023] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a mesh communication-based communication device for an in-field handling robot. The following description uses a mesh communication-based communication device for an in-field handling robot as an example to illustrate this embodiment and the subsequent embodiments.

[0024] Based on this, the embodiments of this application provide a communication method for an in-field handling robot based on mesh communication, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the on-site handling robot communication method based on mesh communication in this application.

[0025] In this embodiment, the communication method for the in-field handling robot based on mesh communication includes steps S10~S60: Step S10: Construct a mesh network based on multiple transport robots in the work area. The mesh network consists of multiple transport robots connected to each other as communication nodes, and each transport robot is equipped with an independent mesh communication module.

[0026] It should be noted that the on-site work area refers to a specific area in a factory, warehouse, or similar facility where material handling is required. Multiple handling robots are distributed within this area. These robots are equipped with independent mesh communication modules. Mesh communication modules are wireless communication units with self-organizing and multi-hop transmission capabilities. They support self-organizing network protocols, can automatically discover surrounding nodes, and can support the establishment of direct communication links between robots without relying on fixed infrastructure, thus forming a self-organizing mesh network.

[0027] Understandably, in a mesh network, each transport robot is not only an individual performing transport tasks but also a communication node capable of direct communication with other robots without relying on a central controller. This decentralized network structure greatly enhances the network's reliability and fault tolerance. Communication nodes are the basic units constituting a mesh network. Each communication node has data transmission, reception, and processing capabilities and can automatically adjust the communication path according to the network protocol, ensuring the stability and efficiency of data transmission.

[0028] In practical implementation, each in-field handling robot is equipped with a mesh communication module supporting IEEE 802.11s or LoRamesh protocols. This module integrates an antenna and a processor, enabling it to automatically scan the mesh modules of other robots in the vicinity and establish bidirectional connections through a handshake protocol, forming 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 to forward data packets from other nodes. This multi-hop transmission mechanism significantly expands network coverage, making it particularly suitable for long-distance communication needs in complex factory environments. When a node experiences signal attenuation due to obstacles or insufficient power, adjacent nodes can automatically relay the transmission, ensuring the continuity of the data link. For example, in a cross-workshop material transfer scenario, if handling robot A needs to transmit task instructions to robot D, but there is a signal blind spot between them, robots B and C can act as relay nodes, transmitting the data completely to the target node through multi-hop routing. This dynamic path adjustment capability allows the mesh network to maintain a stable data transmission success rate even in complex industrial environments.

[0029] Step S20: Control each of the transport robots to broadcast, determine the distance between each node in the mesh network, and construct a dynamic neighbor list.

[0030] It's important to note that each transport robot periodically broadcasts its presence and location information to its surroundings, while also receiving broadcasts from other robots. Using this information, the robot can calculate its distance to other nodes in the vicinity and build a dynamic neighbor list. This list is updated in real-time as the robot moves and the environment changes, ensuring that each robot accurately understands the location and status of other nodes in the surrounding network.

[0031] The construction of a dynamic neighbor list is a crucial step in enabling mesh networks to achieve self-organization and self-adaptation. It allows each robot to dynamically adjust its communication strategy based on the current network conditions, selecting the optimal communication path. For example, when a robot detects that its distance from a neighbor node exceeds a preset threshold, it will automatically remove that node from the neighbor list and attempt to establish a connection with a new, closer node. This dynamic adjustment mechanism ensures that the mesh network maintains efficient and stable communication even when robots move or the environment changes.

[0032] It's worth noting that during the construction of the mesh network, each transport robot broadcasts information through its own mesh communication module, detecting and recording the presence and distance information of other robots in the vicinity, thus forming a dynamic neighbor list. This dynamic neighbor list is updated in real-time by each transport robot in the mesh network based on the broadcast detection results. It records information about other robots that can directly communicate with the current robot, including robot ID, distance, and signal strength.

[0033] In one feasible implementation, step S20 may include: selecting any one of the multiple transport robots as a sending node, and designating other transport robots as other nodes; broadcasting a beacon frame containing its own ID, location coordinates, and current task status information to the surrounding area through the mesh communication module of the sending node; after other nodes receive the beacon frame, calculating a first distance between themselves and the sending node based on the signal strength of the beacon frame; when the first distance is less than a preset threshold, designating the corresponding other node as a neighbor node, and updating its own neighbor list according to the sending node's ID, location coordinates, task status information, and the first distance; after completing one broadcast and neighbor list update, selecting the next transport robot as a new sending node, repeating the broadcast and distance calculation process, until all transport robots have completed one round of broadcast and neighbor list update as sending nodes, generating a complete dynamic neighbor list.

[0034] It's important to note that during the generation of the complete dynamic neighbor list, each transport robot takes turns acting as a sending node, ensuring that distance information between all nodes in the network is accurately obtained. This polling-style broadcast mechanism not only improves the comprehensiveness and accuracy of the neighbor list but also enhances the network's robustness. Even if some nodes are unable to participate in broadcasting due to failure or signal interference, other nodes can still complete the neighbor list construction through the remaining nodes. Furthermore, the real-time updating characteristic of the dynamic neighbor list allows the transport robots to quickly adapt to changes in network topology, such as the addition of new robots or the departure of old ones, thus 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 robot swarms, especially in high-density, highly dynamic industrial environments.

[0035] Understandably, the self-ID is a unique identifier for each handling robot in the mesh network, used to distinguish different robot nodes. The location coordinates are precise location information obtained through GPS / QR code positioning or pre-set positioning base stations within the mesh, reflecting the robot's specific location within the work area. Current task status information includes states such as idle, loaded, and moving, which helps other robots understand the current robot's working status, thus enabling better task allocation and collaborative work. A beacon frame is a wireless signal frame containing specific information. In a mesh network, the sending node announces its presence, location, and task status by broadcasting beacon frames. The design of beacon frames needs to balance information integrity and transmission efficiency, containing enough information for other nodes to accurately identify and locate, while avoiding excessively long frames that could affect transmission efficiency. In this implementation, the beacon frame includes the sending node's self-ID, location coordinates, and current task status information. In practical applications, beacon frames typically employ concise encoding formats, such as using specific binary sequences to represent different task states and using compressed coordinate representation methods to reduce data volume.

[0036] The first distance is calculated based on the signal strength of the beacon frame. There is an inverse relationship between signal strength and distance; that is, the stronger the signal, the closer the distance. The formula for calculating the first distance is: in, It is the signal strength at a distance d. It is a known distance The signal strength is given by , where n is the path loss factor, determined by environmental factors, and d is the actual measured distance. This is a reference distance.

[0037] Understandably, when the first distance is less than a preset threshold, it indicates that the two transport robots are within effective communication range. At this point, other corresponding nodes are designated as neighbor nodes, and the robot updates its neighbor list based on the sending node's own ID, location coordinates, task status information, and the first distance. This process ensures that the neighbor list records information about robots that can currently communicate directly with the robot, providing a foundation for subsequent data transmission and task collaboration.

[0038] After completing one broadcast and neighbor list update, the next transport robot is selected as the new sending node, and the broadcast and distance calculation process is repeated. This polling mechanism ensures that all transport robots in the network can take turns as sending nodes, complete one round of broadcast and neighbor list updates, and ultimately generate a complete dynamic neighbor list. If a node does not send a Beacon frame for more than 30 seconds, it is automatically removed from the neighbor list to ensure the real-time nature of the list.

[0039] It's worth noting that each transport robot acts as a sending node during the generation of the complete dynamic neighbor list. This comprehensive polling method not only improves the comprehensiveness and accuracy of the neighbor list but also enhances the network's robustness. Even if some nodes are unable to participate in broadcasting due to failure or signal interference, other nodes can still complete the neighbor list construction through the remaining nodes, ensuring the continuous stability and efficiency of the mesh network.

[0040] Step S30: Obtain the handling task, decompose the handling task into multiple task instructions, and send the multiple task instructions to the corresponding handling robots through the mesh network, so that each handling robot can perform handling operations according to the received task instructions.

[0041] It should be noted that material handling tasks are typically generated by the factory's management system or scheduling center. These tasks may involve moving materials from a location in the warehouse to a designated workstation on the production line, or transferring materials between multiple workshops. When generating material handling tasks, the management system comprehensively considers factors such as production plans, material requirements, the current status of robots, and information about the factory environment to ensure the rationality and feasibility of the tasks.

[0042] After receiving a handling task, it is broken down into multiple specific task instructions. This breakdown is based on the functional characteristics of the handling robot, the layout of the work area, and the specific requirements of the task. For example, a handling task may be broken down into specific instructions such as "handle material X from warehouse area A to workstation B on the production line". Each task instruction will clearly specify key elements such as the handling robot number that will execute the instruction, the material information to be handled, the starting position, the target position, and the completion time.

[0043] The decomposed task instructions are distributed to the corresponding handling robots through the previously constructed mesh network. During transmission, the mesh network dynamically selects the optimal transmission path based on the current network conditions and the priority of the task instructions, ensuring that the task instructions are delivered to the target robot in a timely and accurate manner. This mesh network-based transmission method not only improves the reliability of data transmission but also enhances the network's fault tolerance; even if some nodes fail, data can still be transmitted smoothly through other nodes. After receiving the task instructions, each handling robot executes the handling operation according to the specific requirements of the instructions. For example, for the instruction "move material X from warehouse A to workstation B on the production line," the corresponding handling robot will first move to warehouse A, confirm the location of material X through its own identification system, then move it to workstation B on the production line, and send a task completion feedback to the management system upon arrival. During this process, the handling robot updates its own location and task status information in real time and feeds this information back to the management system through the mesh network, so that the management system can monitor and adjust the overall task progress.

[0044] During task execution, if an unexpected situation arises, such as an obstacle appearing in the transport path or the target material being taken by another robot, the transport robot will immediately pause its current task and send an anomaly report to the management system via the mesh network. Upon receiving the anomaly report, the management system will, according to preset emergency handling strategies, re-plan the task instructions or adjust the task assignments of other robots to ensure the smooth progress of the overall transport task. For example, if an obstacle appears in the transport path, the management system may plan a new path around the obstacle for the obstructed robot; if the target material is taken by another robot, the management system may assign the task instruction to another idle robot.

[0045] Furthermore, to further improve the efficiency and accuracy of material handling tasks, the management system can also utilize a mesh network to enable collaborative operations among the handling robots. For example, when multiple robots need to simultaneously move the same batch of materials to different workstations, the management system can send collaborative instructions to each robot through the mesh network, specifying the handling sequence and handover method for each robot, thereby avoiding confusion and delays in the material handling process.

[0046] Meanwhile, the mesh network also supports real-time data sharing and status monitoring among the handling robots. Each robot can upload its location, speed, battery level, and task progress in real time through the mesh network. The management system can then dynamically adjust and optimize the overall handling task based on this information. For example, when the management system detects that a robot's battery is about to run out, it can promptly assign a charging task to that robot or adjust the task schedules of other robots to ensure that the robot can be charged in time and continue to perform subsequent tasks.

[0047] In one feasible implementation, step S30 may include: acquiring a handling task and determining handling information, wherein the handling information includes a starting position, a target position, and information about the object being handled; analyzing the handling information to determine the handling steps required to complete the handling task, and converting each handling step into a corresponding task instruction; and, based on the position information of each handling robot and the requirements of the task instruction, sending each task instruction to the corresponding handling robot through the mesh network, so that each handling robot can perform handling operations according to the received task instruction.

[0048] It should be noted that after receiving a handling task, the handling information must be determined, including core elements such as the starting location, the destination location, and the information of the items to be handled. The starting location clarifies the initial storage point of the materials or goods, the destination location indicates where they should ultimately be located, and the information of the items to be handled includes the specific objects being handled, as well as key details such as the material type, weight, and dimensions.

[0049] Understandably, when analyzing material handling information, it's necessary to comprehensively consider factors such as the carrying capacity, moving speed, working radius, and layout of the handling robot to determine the specific handling steps required to complete the task. The overall handling task is broken down into multiple executable steps. Each step corresponds to a specific task instruction, which specifies the actions the handling robot should perform, the material to be handled, the starting and target locations, and the expected completion time. During the task instruction issuance phase, the management system accurately sends the instructions to the target robot via a mesh network based on the real-time location information of each handling robot and the specific requirements of the task instruction. During this process, the mesh network dynamically selects the optimal transmission path based on the current network conditions and the priority of the task instruction, ensuring that the instruction is delivered quickly and accurately. Upon receiving the task instruction, each handling robot immediately parses the instruction content and executes the corresponding handling operation according to the instruction requirements. During execution, the handling robot updates its own location and task status information in real time and feeds this key data back to the management system via the mesh network. The management system then continuously monitors and dynamically adjusts the overall handling task based on this feedback information to ensure that the task is completed smoothly according to the predetermined plan.

[0050] In one feasible implementation, after the step of sending task instructions to the corresponding transport robots through the mesh network based on the position information and task instructions of each transport robot, so that each transport robot can perform transport operations according to the received task instructions, the method further includes: acquiring the real-time position information, movement 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 based on the real-time position information, movement 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; if there is a conflict, determining the conflict type based on the position state at the future time point, wherein the conflict type includes chasing conflict, intersection point conflict, face-to-face conflict, and fault conflict; generating a corresponding conflict resolution strategy based on the conflict type, wherein the conflict resolution strategy includes at least adjusting movement speed, changing movement path, pausing task execution, or reallocating task instructions; sending the conflict resolution strategy to the transport robots involved in the conflict through the mesh network, so that each transport robot adjusts its transport operations according to the received conflict resolution strategy; and monitoring the execution of the conflict resolution strategy in real time and making dynamic adjustments based on the execution effect until the conflict is completely resolved.

[0051] It should be noted that various potential conflicts may occur during the handling operations of different handling robots. If these conflicts are not handled in a timely manner, they can easily lead to the interruption of the handling task or a reduction in efficiency.

[0052] Understandably, each transport robot continuously collects its real-time location information through built-in positioning systems such as GPS, LiDAR, or visual recognition technology. Simultaneously, it accurately measures its movement speed using devices such as wheel encoders or inertial measurement units. Regarding task execution, the robot records its current task stage, completed transport volume, and remaining workload as task progress according to preset task instructions. This real-time data is promptly uploaded to the mesh network.

[0053] Based on the acquired real-time location information, movement speed, and task execution progress, the management system uses a predictive algorithm to estimate the position of each handling robot at a future point in time. The predictive algorithm comprehensively considers factors such as the robot's current movement trajectory, speed change trends, and the layout of the environment, establishing a mathematical model to simulate the robot's subsequent movement, thereby accurately determining the robot's position at a specific future moment, as shown in the following formula: in, To predict the location of the transport robot at a future point in time, Let t be the position of the robot at the current time. Let t be the robot's velocity at the current time. Let be the robot's acceleration at the current time t. The predicted time interval is from the current moment to the target future moment. As a task progress factor, An acceleration adjustment function related to task progress. Environmental factors are modeled based on factors such as the distance between the robot and obstacles, and the curvature of the path.

[0054] Based on the predicted positional status at future time points, further analysis is conducted to determine whether conflicts exist between the transport robots. When the prediction results indicate conflicts between the transport robots, the specific type of conflict is determined based on the positional status at future time points. Chase-over conflicts typically occur when two robots are moving in the same direction, and the robot behind is moving faster and may catch up with the robot in front; intersection point conflicts refer to conflicts arising from the possibility that two robots may arrive at a certain intersection point simultaneously; face-to-face conflicts occur when two robots are moving towards each other and may collide on their paths; malfunction conflicts occur when a malfunction in one robot affects the normal task execution of other robots.

[0055] For different types of conflicts, the management system generates corresponding conflict resolution strategies. For chasing conflicts, the movement speed of the robot behind can be adjusted to maintain a safe distance from the robot in front; for intersection conflicts, the movement paths of one or two robots can be changed to avoid them arriving at the intersection point at the same time; for face-to-face conflicts, one robot can pause its task execution and wait for the other robot to pass before continuing; for fault conflicts, task instructions can be reallocated, and tasks affected by the fault can be assigned to other idle robots for execution.

[0056] After generating conflict resolution strategies, the management system distributes these strategies to the handling robots involved in the conflict via a mesh network. Upon receiving the strategy, each robot immediately adjusts its handling operations accordingly. Simultaneously, the management system monitors the execution of the conflict resolution strategies in real time, evaluating their effectiveness by collecting feedback from each robot. If the strategy's execution is found to be unsatisfactory or the conflict is not completely resolved, the management system will dynamically adjust and regenerate or optimize the conflict resolution strategy until the conflict is completely resolved, ensuring the smooth progress of the handling task.

[0057] Step S40: When data needs to be transmitted during the handling operation of the handling 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.

[0058] It should be noted that multi-hop routing algorithms are a key technology for achieving efficient data transmission in networks, especially suitable for mesh communication-based in-field transport robot systems. During the transport robot's operations, various types of data often need to be transmitted, including but not limited to real-time control commands, task scheduling information, and environmental perception data. In this embodiment, the multi-hop routing algorithm prioritizes nodes with fewer hops, higher signal strength, and lower load as relays. Low load means that the amount of data currently being transmitted by the node is less than a preset threshold.

[0059] Different types of data have varying degrees of importance for the successful execution of data transfer tasks; therefore, transmission priorities need to be determined based on data type. Different multi-hop routing algorithms are then applied according to data priority. For example, high-priority data uses a greedy routing algorithm based on link quality (LQ), prioritizing short paths with stable signals; low-priority data uses a load-balancing routing algorithm to avoid concentrating bandwidth on a single node.

[0060] Link quality-based (LQ) greedy routing algorithms consider link quality metrics such as signal strength and bit error rate during path selection, prioritizing paths with the most stable signal and fewest hops for data transmission. This algorithm ensures that high-priority data, such as real-time control commands and critical task scheduling information, experiences minimal packet loss and latency during transmission, guaranteeing that the handling robot can receive commands and execute corresponding operations promptly and accurately. For example, when the management system needs to send an emergency stop command to a handling robot, the link quality-based greedy routing algorithm can quickly find a path with the best signal quality, delivering the command rapidly and preventing safety incidents caused by signal problems preventing timely command execution.

[0061] Load balancing routing algorithms primarily focus on the load status of each node in the network. They monitor the data transmission volume of each node in real time, and when a node's load approaches or exceeds a preset threshold, they proactively avoid that node and select other nodes with lower loads as relays for data transmission. This effectively prevents a single node from experiencing performance degradation or even paralysis due to excessive data transmission, ensuring the overall data transmission efficiency and stability of the network. For low-priority data, such as non-critical environmental sensing data, load balancing routing algorithms can rationally allocate network resources without affecting high-priority data transmission, improving the overall network utilization. For example, when multiple handling robots are working simultaneously and generating a large amount of environmental sensing data, load balancing routing algorithms can evenly distribute the data across different paths based on the real-time load status of each node, preventing congestion at any single node from impacting the performance of the entire network.

[0062] In its implementation, after determining the transmission priority of the data to be transmitted, the management system selects the corresponding multi-hop routing algorithm to plan the data transmission path based on the transmission priority. The multi-hop routing algorithm selects suitable relay nodes in the network to achieve multi-hop transmission of data from the source node to the destination node. In this embodiment, the multi-hop routing algorithm comprehensively considers factors such as the number of hops, signal strength, and load of nodes, prioritizing nodes with fewer hops, higher signal strength, and lower load as relays to ensure that data can be transmitted along the optimal path.

[0063] When encountering network congestion or node failures during data transmission, the multi-hop routing algorithm can 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 the 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 mesh communication-based in-field handling robot systems.

[0064] In one feasible implementation, step S40 may include: when data needs to be transmitted during the handling operation of the handling robot, acquiring the data to be transmitted by the handling robot and determining the data type of the data to be transmitted, wherein the data type includes one or more of real-time control commands, task scheduling information, and environmental perception data; determining a transmission delay tolerance based on the data type of the data to be transmitted, wherein the transmission delay tolerance of the real-time control commands 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 that of the real-time control commands and the environmental perception data; determining a transmission priority based on 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 preset multi-hop routing algorithm library based on the transmission priority, wherein the multi-hop routing algorithm library stores multi-hop routing algorithms corresponding to different transmission priorities, with high priority corresponding to a greedy routing algorithm based on link quality, secondary priority corresponding to a hybrid routing algorithm based on distance and remaining energy, and low priority corresponding to a load-balancing routing algorithm.

[0065] It should be noted that the data generated by the robot to be transmitted can be divided into three categories, with different transmission delay tolerances for different data types: 1. Real-time control commands, such as obstacle avoidance interaction commands with other robots and emergency stop signals, have a transmission delay tolerance of less than 100ms; 2. Task scheduling information, such as task allocation and path adjustment commands issued by the management system or scheduling center, have a transmission delay tolerance of less than 500ms; 3. Environmental perception data, such as LiDAR scan maps and cargo recognition images, have a transmission delay tolerance that can be relaxed to 1-2s. Transmission delay tolerance is inversely proportional to transmission priority. Therefore, the transmission priority of real-time control commands is set to the highest level to ensure that control commands can be transmitted with the shortest delay and highest reliability, guaranteeing the real-time performance and accuracy of the handling robot operation; the transmission priority of task scheduling information is set to the second highest level to ensure that task allocation and adjustment information can be communicated in a timely manner, maintaining the coordination and efficiency of the overall handling task; and the transmission priority of 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.

[0066] Understandably, the multi-hop routing algorithm library pre-stores multi-hop routing algorithms for different transmission priorities. Once the management system determines the transmission priority based on transmission delay tolerance, it quickly calls the algorithm matching the priority from this library. For example, for high-priority data such as real-time control commands, the system uses a greedy routing algorithm based on link quality. This algorithm evaluates the link quality of each node in the network in real time, prioritizing the shortest path with stable signal and reliable transmission for data transmission, thus ensuring that control commands reach the target robot in the shortest time and with the highest accuracy. For secondary-priority data such as task scheduling information, the system uses a hybrid routing algorithm based on distance and remaining energy. This algorithm considers both the distance between nodes and the remaining energy of the nodes, selecting relay nodes that can guarantee transmission efficiency and extend the overall network lifespan for data transmission. For low-priority data such as environmental perception data, the system uses a load-balancing routing algorithm. This algorithm dynamically monitors the load of each node in the network, evenly distributing data transmission tasks to nodes with lower loads, avoiding concentrated bandwidth consumption on any one node, thereby improving the overall transmission efficiency and stability of the network. By flexibly invoking multi-hop routing algorithms based on data type and transmission priority, the system can ensure that all types of data can be transmitted with the optimal path and highest efficiency during the handling operation of the handling robot.

[0067] Step S50: Obtain the location information, power information, and load status uploaded by each of the transport robots through the mesh network, and select the optimal data transmission path using the multi-hop routing algorithm based on the location information, power information, load status, and dynamic neighbor list.

[0068] It's important to note that the transport robots upload their location information in real time via the mesh network. This location information pinpoints the robot's exact coordinates within the area, allowing the management system to accurately track its distribution and movement. Battery information reflects the robot's current energy reserves, crucial for efficient task allocation and preventing task interruptions due to power depletion. Load status refers to information such as the weight or volume of goods the robot is currently carrying, helping the management system understand the robot's workload for more rational task distribution. After acquiring this information, the management system combines it with a dynamic neighbor list and uses a multi-hop routing algorithm to select the optimal data transmission path. The dynamic neighbor list records information about each robot's currently connectable neighbor nodes, including neighbor IDs, signal strength, and load status. The multi-hop routing algorithm comprehensively considers these factors, as well as the priority and characteristics of the data to be transmitted, to plan an efficient and reliable transmission path.

[0069] 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.

[0070] 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.

[0071] Step S60: Transmit the data to be transmitted between each handling robot through the optimal data transmission path.

[0072] 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.

[0073] 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.

[0074] Carrier Sense Multiple Access (CSMA) allows low-priority data to compete for access by monitoring channel conditions in non-fixed time slots. When the channel is idle, data can be sent immediately; if the channel is occupied, it waits for a period of time before attempting again. This mechanism improves the utilization of network resources and is particularly suitable for transmitting non-critical data such as environmental sensing data. For example, when multiple handling robots simultaneously upload LiDAR scan images, CSMA allows each robot to dynamically adjust its transmission timing based on channel conditions, avoiding data conflicts caused by simultaneous transmissions. This ensures effective transmission of environmental sensing data without affecting the transmission of high-priority data.

[0075] When high-priority data needs to be transmitted, the combined TDMA and CSMA mechanism 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 commands) encounters a channel occupied by low-priority data while waiting to be transmitted under the CSMA mechanism, the high-priority data can immediately interrupt the transmission of the low-priority data and take priority to occupy the channel for transmission. This preemption mechanism ensures the real-time performance and reliability of high-priority data, guaranteeing the timely delivery of critical commands even under high network load.

[0076] By combining Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) into a media access control mechanism, this implementation method not only ensures the real-time performance 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.

[0077] Specifically, a hybrid TDMA and CSMA mechanism is adopted, dividing time into 10ms time slots. Fixed time slots are allocated for real-time control commands, such as one dedicated time slot for each robot, ensuring conflict-free transmission. Task scheduling information and environmental perception data compete for remaining time slots through the CSMA mechanism. Data is transmitted when an idle channel is detected, and if a collision is detected, it is randomly backed up and retransmitted. When high-priority data bursts, a time slot preemption mechanism can be triggered to suspend the transmission of low-priority data, prioritizing the transmission of critical commands. It's worth noting that a route reselection mechanism exists during data transmission. During mesh network operation, each node monitors link quality in real time, such as packet loss rate and latency. Route reselection is triggered when the following conditions occur: ① three consecutive transmission failures; ② a neighbor node's heartbeat signal is lost for more than 2 seconds; ③ the Link Quality Index (LQ) is below 60 (out of 100). During reselection, the node recalculates the optimal path to the target node based on the latest neighbor list and link quality data. The entire process is completed within 50ms, ensuring uninterrupted communication, strong fault tolerance, and continuous communication of the robot cluster in complex environments. The management system or dispatch center can access any node in the mesh network, such as a relay node fixed at the edge of the site, via wired or wireless means. This allows them to issue task instructions to all robots, such as "Robot A will move the goods from shelf 3 to sorting station 2". They can also receive data such as location, battery level, and load status uploaded by each robot. Based on this data, they can perform global path planning to avoid robot congestion. The adjusted path is then synchronized to the relevant robots in real time through the mesh network, enabling global monitoring and scheduling. This reduces reliance on fixed communication infrastructure, lowers on-site deployment costs, and is suitable for scenarios where robots move dynamically.

[0078] It's worth noting that all data transmitted through the mesh network is encrypted using the AES-128 encryption algorithm. Nodes must be authenticated with a preset key before joining the network to prevent unauthorized devices from accessing the network. Furthermore, the encryption key is periodically changed during data transmission to enhance security. The AES-128 encryption algorithm boasts high security and encryption efficiency, effectively protecting sensitive data in the handling robot system, such as real-time control commands, task scheduling information, and environmental perception data, preventing theft or tampering during transmission. The preset key authentication mechanism before nodes join the network further ensures that only authorized devices can access the mesh network, avoiding interference and security threats caused by unauthorized access. This combination of encryption and authentication mechanisms provides reliable security for mesh-based in-field handling robot systems. This embodiment provides a communication method for in-field transport robots based on mesh communication. A mesh network is constructed based on multiple transport robots in the working area. The mesh network consists of multiple transport robots interconnected as communication nodes, with each transport robot equipped with an independent mesh communication module. Each transport robot is controlled to broadcast, determining the distance between nodes in the mesh network and constructing a dynamic neighbor list. A transport task is acquired, decomposed into multiple task instructions, and distributed to the corresponding transport robots through the mesh network, enabling each transport robot to perform transport operations according to the received task instructions. When data transmission is required during transport operations, the transmission priority is determined based on the data type, and a corresponding multi-hop routing algorithm is invoked based on the transmission priority. Location information, power information, and load status uploaded by each transport robot through the mesh network are acquired, and the optimal data transmission path is selected using the multi-hop routing algorithm based on the location information, power information, load status, and the dynamic neighbor list. The data to be transmitted is then transmitted between the transport robots through the optimal data transmission path. By introducing a self-organizing mesh network, the integrity of network coverage is improved. Data transmission path planning is performed by calling a multi-hop routing algorithm based on data transmission priority, which has strong fault tolerance and ensures continuous communication of the robot cluster in complex environments. This effectively improves the communication stability and fault tolerance of the in-field handling robots, while further ensuring the operation efficiency and reliability of the robot cluster.

[0079] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S50 includes steps S501 to S503: Step S501: Obtain the location information, power information, and load status uploaded by each of the transport robots through the mesh network.

[0080] It should be noted that each handling robot uploads key data such as its location, battery level, and load status to the mesh network in real time via its built-in mesh communication module. The location information is obtained through high-precision positioning technology, accurately reflecting the robot's specific coordinates and movement trajectory within the area. Battery level information is monitored in real time by the robot's built-in power sensors, ensuring the management system can accurately grasp the robot's energy reserves. Load status information is obtained through the robot's onboard weighing sensors or volume measurement devices, helping the management system understand the robot's current workload. This information, after being uploaded to the mesh network, is received and processed by the management system.

[0081] Step S502: Generate an initial data transmission path using the multi-hop routing algorithm based on the location information, the power information, the load status, and the dynamic neighbor list.

[0082] It's important to note that multi-hop routing algorithms can be greedy routing algorithms based on link quality, hybrid routing algorithms based on distance and remaining energy, or load-balancing routing algorithms. These algorithms comprehensively consider the robot's location, battery level, load status, and dynamic neighbor list when generating the initial data transmission path. For example, a greedy routing algorithm based on link quality prioritizes the neighbor node with the best link quality for data transmission to ensure stability. A hybrid routing algorithm based on distance and remaining energy considers both the distance between nodes and the robot's remaining battery power to avoid data transmission interruptions due to insufficient power. A load-balancing routing algorithm rationally allocates data transmission tasks based on the load status of each node to prevent some nodes from being overloaded and affecting overall transmission efficiency. These multi-hop routing algorithms can generate efficient and reliable initial data transmission paths.

[0083] In one feasible implementation, step S502 may include: if the multi-hop routing algorithm is a link quality-based greedy routing algorithm, selecting the transport robot closest to the data sender as the initial transmission node based on the location information of each transport robot; obtaining the link quality information between the initial transmission node and its surrounding neighboring 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 neighboring nodes as the next-hop transmission node based on the link quality information; repeating the process of selecting the next-hop transmission node until the data is transmitted to the target transport robot, thereby generating the initial data transmission path.

[0084] It's important to note that when transmitting high-priority data such as real-time control commands, a greedy routing algorithm based on link quality (LQ) is employed to prioritize data transmission stability and real-time performance. This algorithm first selects the robot closest to the data sender as the initial transmission node based on the location information of each transport robot. Then, through the mesh communication module of this initial node, it collects link quality data between the robot and its neighbors. This data includes key indicators such as signal strength, packet loss rate, and transmission latency. Based on this link quality information, the algorithm selects the node with the best link quality from among the neighbors as the next-hop node for data transmission. This process continues, continuously selecting the next-hop transmission node until the data successfully reaches the target transport robot, thus constructing the initial data transmission path. This path selection method is particularly effective for high-priority data transmission, minimizing transmission latency and packet loss, and ensuring the timely and accurate delivery of critical commands.

[0085] Understandingly, selecting the node with the best link quality from among neighboring nodes as the next-hop transmission node involves: quantifying and evaluating the collected link quality information; weighting indicators such as signal strength, packet loss rate, and transmission delay according to preset weights to obtain a comprehensive link quality score for each neighboring node. For example, the weight of signal strength can be set to 0.4, packet loss rate to 0.3, and transmission delay to 0.3. Then, based on the actual signal strength, packet loss rate, and transmission delay values ​​of each neighboring node, the comprehensive score is calculated using the weighting formula. Next, the neighboring nodes are sorted from highest to lowest comprehensive link quality score, and the neighboring node with the highest score is selected as the next-hop transmission node. If multiple neighboring nodes have the same score, a secondary screening is performed based on other auxiliary indicators, such as the node's historical transmission success rate and remaining bandwidth, to ensure that the node with the best link quality is selected. In this way, the node with the best link quality from among the neighboring nodes can be selected more scientifically and accurately as the next-hop transmission node, thereby effectively improving the stability and reliability of data transmission.

[0086] In one feasible implementation, step S502 may further include: when the multi-hop routing algorithm is a hybrid routing algorithm based on distance and remaining energy, calculating the second distance from the data sender to all potential next-hop nodes according to the location information of each handling robot; obtaining the remaining power information and current load status of each potential next-hop node; comprehensively evaluating each potential next-hop node according to the second distance, remaining power, and load status 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 handling robot, thereby generating an initial data transmission path.

[0087] It should be noted that when the data to be transmitted is secondary priority data such as task scheduling information, a hybrid routing algorithm based on distance and remaining energy is adopted, which comprehensively considers the distance from the data sender to the potential next-hop node, the remaining power of each potential next-hop node, and the current load status. First, based on the location information of each handling robot, the second distance from the data sender to all potential next-hop nodes is accurately calculated. Simultaneously, the remaining power information of each potential next-hop node is obtained through the mesh communication module, reflecting the node's current energy reserves. Furthermore, the current load status of each node is also obtained to understand its workload. Each potential next-hop node is comprehensively evaluated based on the second distance, remaining power, and load status. During the evaluation process, different weights can be assigned to distance, remaining power, and load status to reflect their relative importance in data transmission. For example, nodes that are closer, have sufficient remaining power, and have a lighter load may receive a higher comprehensive score. This comprehensive evaluation method determines the comprehensive score of each potential next-hop node. Finally, the potential next-hop node with the highest comprehensive score is selected as the next-hop transmission node, and this selection process is repeated until the data is successfully transmitted to the target handling robot, thus generating the initial data transmission path. This hybrid routing algorithm, based on distance and remaining energy, can balance transmission efficiency and node resource utilization during secondary priority data transmission, ensuring the reliability and stability of data transmission.

[0088] In one feasible implementation, step S502 may further include: when the multi-hop routing algorithm is a load-balanced routing algorithm, based on the load status of each handling robot, prioritizing the handling robot with the lightest current load as the initial transmission node; obtaining the load status of neighboring nodes around the initial transmission node, and calculating the load balancing coefficient of each neighboring node in combination with the location information of each neighboring node and the initial transmission node; based on the load balancing coefficient, selecting the node with the optimal load balancing coefficient (i.e., the lightest load and the most suitable location) from the neighboring nodes as the next-hop transmission node; repeating the process of selecting the next-hop transmission node, continuously and dynamically evaluating the load status of each node during transmission, and adjusting the transmission path as needed, until the data is transmitted to the target handling robot, thus generating the initial data transmission path.

[0089] It should be noted that the load balancing coefficient is an important parameter that comprehensively considers the relationship between node load and location, as shown in the following formula: in, This is the load balancing coefficient. The load of neighbor node i represents the workload or load of its current task. For the initial transmission node load, Let be the distance between the initial transmitting node and its neighbor node i. For load factor, This is the distance coefficient.

[0090] Understandably, the load factor and distance factor can be set according to actual scenario requirements and experience. For example, the load factor can be set to 0.6 and the distance factor to 0.4. With such settings, when calculating the load balancing factor, both the load of the node and the distance between the node and the initial transmission node can be taken into account.

[0091] Based on the calculated load balancing coefficient, the node with the optimal load balancing coefficient—that is, the node with the lightest load and the most suitable location—is selected from the neighboring nodes as the next-hop transmission node. This process of selecting the next-hop transmission node is then repeated, with the load status of each node continuously and dynamically evaluated during transmission. Because the load status of each node may change as data transmission progresses, the transmission path needs to be adjusted in a timely manner until the data is successfully transmitted to the target handling robot, thus generating the initial data transmission path. This load-balancing routing algorithm effectively prevents some nodes from being overloaded, thus affecting the overall transmission efficiency and ensuring smooth data transmission.

[0092] Step S503: Optimize the initial data transmission path based on the improved discrete whale algorithm to generate the optimal data transmission path.

[0093] It should be noted that the improved discrete whale algorithm is a swarm intelligence-based optimization algorithm inspired by the predation behavior of whale pods. In the data transmission path optimization problem, this algorithm iteratively optimizes the initial data transmission path by simulating the search and encirclement mechanism of a whale pod. Specifically, the algorithm first encodes the initial data transmission path into the location information of individual whales, with each whale representing a possible data transmission path scheme. Then, the algorithm defines a fitness function based on indicators such as path transmission efficiency and stability to evaluate the merits of each individual whale. During the iteration process, the whale pod continuously updates its own location information, i.e., adjusts its data transmission path scheme, to gradually approach the optimal solution.

[0094] The improved Discrete Whale Algorithm incorporates several strategies during optimization to enhance convergence speed and global search capability. For example, it employs a discretized position update method to adapt to the discrete nature of data transmission paths. Simultaneously, it introduces inertial weights and an adaptive adjustment mechanism, enabling the algorithm to maintain a large search step size in the early stages of the search, quickly approaching the optimal solution region, while reducing the search step size in the later stages for a more refined search. Furthermore, the algorithm utilizes simulated annealing to avoid getting trapped in local optima, further improving its global search capability.

[0095] By optimizing the Discrete Whale Algorithm, redundant nodes and inefficient transmission segments in the initial data transmission path were effectively eliminated, generating the optimal data transmission path. This path achieves optimal performance in terms of transmission efficiency, stability, and fault tolerance, ensuring efficient and reliable communication for the in-field handling robots in complex environments. Furthermore, the generation of the optimal data transmission path provides strong support for the operational efficiency and reliability of the robot swarm.

[0096] In one feasible implementation, step S503 may include: encoding the initial data transmission path as whale individual positions, and initializing the whale population based on the whale individual positions using 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; calculating the fitness value of each whale individual in the whale population, the fitness value being calculated based on the total transmission delay of the path, the total packet loss rate, and the power consumption and load status of the handling robot; selecting the optimal whale individual in the whale population as the leader based on the fitness value, and updating the position of the whale population to obtain the updated whale population, wherein the position update process includes leader-guided update and random walk update, wherein leader-guided update refers to other whale individuals learning from the leader and calculating a new position based on the leader's position and their own current position, and random walk update is... This refers to a whale randomly selecting a direction to move within the search space. During the position update process, if the path node corresponding to the new position exceeds 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 recalculated, 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 recalculating the fitness value continue iteratively until the preset optimization termination condition is met. The preset optimization termination condition includes reaching a preset maximum number of iterations, or the change in the fitness value of all whale individuals in the whale population being less than a preset threshold in several 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 the optimal data transmission path.

[0097] It should be noted that the introduction of genetic algorithms provides more diverse and effective starting points for the initialization of whale populations. By encoding the initial data transmission path and generating individual whale locations through genetic algorithms, the whale population can cover a wider area within the search space, thereby increasing the likelihood of finding the global optimum.

[0098] When calculating the fitness value of an individual whale, multiple factors are comprehensively considered, including the total transmission delay of the path, the total packet loss rate, and the power consumption and load status of the transport robot. This setting allows the fitness value to more comprehensively reflect the quality of the data transmission path. The formula for calculating the fitness function is: in, , , , These are the weights for total transmission delay, total packet loss rate, power consumption, and load status, respectively. and This represents the number of consecutive nodes in the path. Represents the node To the node Transmission time, Represents the node To the node packet loss rate, Represents the node To the node Power consumption, Represents a node The load status.

[0099] The position update method, combining leader-guided updates and random walk updates, ensures the directionality of the whale pod during the search process while increasing the randomness and globality of the search. Leader-guided updates allow other whales to learn from leaders with higher fitness values, gradually approaching the optimal solution; while random walk updates allow whales to explore randomly within the search space, avoiding getting trapped in local optima. This hybrid update strategy effectively balances the algorithm's exploration and exploration capabilities, making the improved discrete whale algorithm perform exceptionally well in data transmission path optimization.

[0100] During the location update process, new locations that are outside the feasible range or violate the constraints of the dynamic neighbor list are regenerated, ensuring the feasibility and effectiveness of the path. This avoids the generation of invalid paths, thereby improving the overall transmission efficiency.

[0101] When the preset optimization termination conditions are met, such as reaching the maximum number of iterations or the change in fitness value being less than a preset threshold, the algorithm will stop iterating and decode the location of the whale individual with the highest current fitness value 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.

[0102] Furthermore, by introducing a genetic algorithm to initialize the whale population and by comprehensively considering multiple factors to calculate the fitness value of individual whales, the improved discrete whale algorithm demonstrates greater flexibility and adaptability in data transmission path optimization. It can adjust the algorithm parameters and fitness function according to different application scenarios and needs to find the most suitable data transmission path for the current environment.

[0103] In summary, the data transmission path optimization method based on the improved discrete whale algorithm, by simulating the foraging behavior of whale groups and combining multiple optimization strategies, effectively eliminates redundant nodes and inefficient transmission segments in the initial path, generating the optimal data transmission path. This method not only improves the efficiency and stability of data transmission but also provides a reliable communication solution for in-field handling robots in complex environments. 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. Then, the data transmission path optimization based on the improved discrete whale algorithm effectively eliminates redundant nodes and inefficient transmission segments in the initial path, generating the optimal data transmission path and effectively improving the efficiency and stability of data transmission.

[0104] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the communication method of the in-field handling robot based on mesh communication in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0105] This application also provides a communication device for an in-field handling robot based on mesh communication. Please refer to [link / reference]. Figure 3 The communication device for the in-field handling robot based on mesh communication includes: The construction module 10 is used to build a mesh network based on multiple transport robots in the field work area. The mesh network is composed of multiple transport robots as communication nodes connected to each other, and each transport robot is equipped with an independent mesh communication module.

[0106] The construction module 10 is also used to control each of the transport robots to broadcast, determine the distance between each node in the mesh network, and construct a dynamic neighbor list.

[0107] The distribution module 20 is used to acquire the handling task, decompose the handling task into multiple task instructions, and distribute the multiple task instructions to the corresponding handling robots through the mesh network, so that each handling robot can perform handling operations according to the received task instructions.

[0108] The determination module 30 is used to determine the transmission priority based on the data type of the data to be transmitted when the transport robot needs to transmit data during the transport operation, and to call the corresponding multi-hop routing algorithm according to the transmission priority.

[0109] Selection module 40 is used to obtain the location information, power information and load status uploaded by each of the transport robots through the mesh network, and select the optimal data transmission path according to the location information, power information, load status and dynamic neighbor list through the multi-hop routing algorithm.

[0110] The transmission module 50 is used to transmit the data to be transmitted between the transport robots through the optimal data transmission path.

[0111] The mesh-based communication device for in-field transport robots provided in this application employs the mesh-based communication method for in-field transport robots described in the above embodiments, which can solve the technical problems of poor stability and weak fault tolerance in existing in-field transport robot communication technologies. Compared with the prior art, the beneficial effects of the mesh-based communication device for in-field transport robots provided in this application are the same as those of the mesh-based communication method for in-field transport robots provided in the above embodiments, and other technical features in the mesh-based communication device for in-field transport robots are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0112] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A communication method for an in-field transport robot based on mesh communication, characterized in that, The method includes: A mesh network is constructed based on multiple handling robots in the on-site work area. The mesh network consists of multiple handling robots connected to each other as communication nodes, and each handling robot is equipped with an independent mesh communication module. Each of the transport robots is controlled to broadcast, the distance between nodes in the mesh network is determined, and a dynamic neighbor list is constructed. The system acquires a handling task, breaks it down into multiple task instructions, and sends these instructions to the corresponding handling robots via the mesh network, so that each handling robot can perform handling operations according to the received task instructions. When the handling robot needs to transmit data during the handling 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. The system acquires the location information, power information, and load status of each transport robot uploaded through the mesh network, and selects the optimal data transmission path based on the location information, power information, load status, and dynamic neighbor list using the multi-hop routing algorithm. The data to be transmitted is transmitted between the transport robots through the optimal data transmission path.

2. The method as described in claim 1, characterized in that, The control of each of the transport robots to broadcast, determine the distance between nodes in the mesh network, and construct a dynamic neighbor list includes: Select any one of the multiple transport robots as the sending node, and use the other transport robots as other nodes; The sending node broadcasts a beacon frame containing its own ID, location coordinates, and current task status information to the surrounding area through the mesh communication module. After other nodes receive the beacon frame, they calculate the first distance between themselves and the sending node based on the signal strength of the beacon frame. When the first distance is less than a preset threshold, the corresponding other nodes are taken as neighbor nodes, and the neighbor list of the sending node is updated according to the ID, location coordinates, task status information and the first distance. After completing one broadcast and neighbor list update, the next transport robot is selected as the new sending node, and the broadcast and distance calculation process is repeated until all transport robots have completed one round of broadcast and neighbor list update as sending nodes, generating a complete dynamic neighbor list.

3. The method as described in claim 1, characterized in that, The process of acquiring a handling task involves decomposing the task into multiple task instructions and distributing these instructions to corresponding in-field handling robots via the mesh network. This enables each in-field handling robot to perform handling operations based on the received task instructions, including: Obtain the transportation task and determine the transportation information, wherein the transportation information includes the starting position, the target position, and the information of the transported item; The transport information is analyzed to determine the transport steps required to complete the transport task, and each transport step is converted into a corresponding task instruction. Based on the location information of each transport robot and the requirements of the task instructions, the task instructions are sent to the corresponding transport robots through the mesh network, so that each transport robot can perform transport operations according to the received task instructions.

4. The method as described in claim 3, characterized in that, The step of sending task instructions to the corresponding handling robots via the mesh network, based on the location information of each handling robot and the requirements of the task instructions, so that each handling robot can perform handling operations according to the received task instructions, further includes: The real-time location information, moving speed, and task execution progress of each of the aforementioned handling robots are obtained during the handling operation. Based on the real-time location information, moving speed, and task execution progress, the position status of each handling robot at a future time point is estimated. Determine whether there is a conflict between the handling robots based on their position status at the future time point; In the event of a conflict, the conflict type is determined based on the positional status at the future time point, wherein the conflict type includes chasing conflict, intersection conflict, face-to-face conflict, and failure conflict. Generate a corresponding conflict resolution strategy based on the conflict type, wherein the conflict resolution strategy includes at least adjusting movement speed, changing movement path, pausing task execution, or reassigning task instructions; The conflict resolution strategy is distributed to the handling robots involved in the conflict through the mesh network, so that each handling robot can adjust its handling operation according to the received conflict resolution strategy; Monitor the execution of conflict resolution strategies in real time and make dynamic adjustments based on the results until the conflict is completely resolved.

5. The method as described in claim 1, characterized in that, When data needs to be transmitted during the handling operation of the handling 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 invoked according to the transmission priority, including: When the handling robot needs to transmit data during the handling operation, the data to be transmitted by the handling robot is acquired, and the data type of the data to be transmitted is determined. The data type includes one or more of real-time control instructions, task scheduling information, and environmental perception data. The transmission delay tolerance is determined based on the data type of the data to be transmitted, wherein the transmission delay tolerance of the real-time control command 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 that of the real-time control command and the environmental perception data. Transmission priority is determined based on the transmission delay tolerance, wherein the transmission delay tolerance is inversely proportional to the transmission priority; According to the transmission priority, the corresponding multi-hop routing algorithm is called from the preset multi-hop routing algorithm library. The multi-hop routing algorithm library stores multi-hop routing algorithms corresponding to different transmission priorities. High priority corresponds to a greedy routing algorithm based on link quality, secondary priority corresponds to a hybrid routing algorithm based on distance and remaining energy, and low priority corresponds to a load balancing routing algorithm.

6. The method as described in claim 1, characterized in that, The step of obtaining the location information, power information, and load status uploaded by each of the transport robots through the mesh network, and selecting the optimal data transmission path using the multi-hop routing algorithm based on the location information, power information, load status, and dynamic neighbor list, includes: Obtain the location information, power information, and load status uploaded by each of the transport robots through the mesh network; An initial data transmission path is generated using the multi-hop routing algorithm based on the location information, power information, load status, and dynamic neighbor list. The initial data transmission path is optimized based on the improved discrete whale algorithm to generate the optimal data transmission path.

7. The method as described in claim 6, characterized in that, The step of generating an initial data transmission path based on the location information, the power information, the load status, and the dynamic neighbor list using the multi-hop routing algorithm includes: When the multi-hop routing algorithm is a greedy routing algorithm based on link quality, the robot closest to the data sender is selected as the initial transmission node according to the location information of each handling robot. The link quality information between the initial transmission node and its surrounding neighboring nodes is obtained through the mesh communication module of the initial transmission node. The link quality information includes signal strength, packet loss rate, and transmission delay. Based on the link quality information, the node with the best link quality is selected from the neighboring nodes as the next-hop transmission node; Repeat the process of selecting the next hop transmission node until the data is transmitted to the target handling robot, thus generating the initial data transmission path.

8. The method as described in claim 6, characterized in that, The step of generating an initial data transmission path using the multi-hop routing algorithm based on the location information, the power information, the load status, and the dynamic neighbor list further includes: When the multi-hop routing algorithm is a hybrid routing algorithm based on distance and remaining energy, the second distance from the data sender to all potential next-hop nodes is calculated according to the position information of each handling robot; Obtain the remaining battery power and current load status of each potential next-hop node; Based on the second distance, remaining power, and load status, each potential next-hop node is comprehensively evaluated to determine a comprehensive score for each potential next-hop node; Select the potential next-hop node with the highest overall 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 handling robot, thus generating the initial data transmission path.

9. The method as described in claim 6, characterized in that, The optimization of the initial data transmission path based on the improved discrete whale algorithm to generate the optimal data transmission path includes: The initial data transmission path is encoded as the location of an individual whale, and a whale population is initialized based on the location of the individual whale using a genetic algorithm. Each location of an individual whale in the whale population contains multiple dimensions, and each dimension value represents a node number on the data transmission path. The fitness value of each individual whale in the whale population is calculated based on the total transmission delay of the path, the total packet loss rate, and the power consumption and load status of the handling robot. The optimal whale individual in the whale population is selected as the leader based on the fitness value, and the position of the whale population is updated to obtain the updated whale population. The position update process includes leader-guided update and random walk update. Leader-guided update means that other whale individuals learn from the leader and calculate a new position based on the leader's position and their own current position. Random walk update means that whale individuals randomly select a direction to move in the search space. During the position update process, if the path node corresponding to the new position 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 recalculated, and it is determined whether the preset optimization termination condition is met. If the preset optimization termination condition is not met, the steps of calculating fitness value, selecting the best whale individual, updating the position of the whale population, and recalculating fitness value continue to be executed iteratively until the preset optimization termination condition is met. The preset optimization termination condition includes reaching the preset maximum number of iterations, or the change in fitness value of all whale individuals in the whale population is less than the preset threshold in several consecutive iterations. If the preset optimization termination condition is met, the location of the whale individual with the highest fitness value will be decoded as the optimal data transmission path.

10. A communication device for an in-field transport robot based on mesh communication, characterized in that, The mesh-based communication device for the in-field handling robot includes: The building module is used to construct a mesh network based on multiple handling robots in the field work area. The mesh network consists of multiple handling robots connected to each other as communication nodes, and each handling robot is equipped with an independent mesh communication module. The construction module is also used to control each of the transport robots to broadcast, determine the distance between each node in the mesh network, and construct a dynamic neighbor list; The distribution module is used to acquire the handling task, decompose the handling task into multiple task instructions, and distribute the multiple task instructions to the corresponding handling robots through the mesh network, so that each handling robot can perform handling operations according to the received task instructions. The determination module is used to determine the transmission priority based on the data type of the data to be transmitted when the transport robot needs to transmit data during the transport operation, and to call the corresponding multi-hop routing algorithm according to the transmission priority. The selection module is used to obtain the location information, power information and load status of each of the transport robots uploaded through the mesh network, and select the optimal data transmission path according to the location information, power information, load status and dynamic neighbor list through the multi-hop routing algorithm; The transmission module is used to transmit the data to be transmitted between the transport robots through the optimal data transmission path.

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