A method, system and medium for dynamic selection of optimal center point of a robot cluster

Abstract

The application provides a robot cluster optimal center point dynamic selection method, system and medium, and belongs to the technical field of mobile cooperative networking, and comprises the following steps: constructing an initial optimal node list according to the weight information configured by each node in a robot cluster network, and determining the optimal node in the list as a master node; sorting each sub-node according to position information, xRSSI information, load information and residual power of each sub-node in a preset period through the master node, and updating and publishing the optimal node list of the sub-node; when the optimal node list update and publication of the master node are not received within a preset time, the original master node is out of connection, the optimal sub-node surviving in the current optimal node list is taken as an updated master node, master node broadcast / event information is sent, and a receiving sub-node is accessed; when the master node is out of connection or at the edge of the network, the optimal sub-node is upgraded to the master node to take over the whole network, so that the situation that the mobile cooperative networking whole network is disconnected or the communication quality is poor is avoided.

Description

Technical Field

[0001] This invention relates to the field of mobile collaborative networking technology, specifically to a method, system, and medium for dynamically selecting the optimal center point of a robot swarm. Background Technology

[0002] Robot swarm mobile networking typically employs a centralized master-slave architecture, where one node is selected as the master node, and the remaining child nodes maintain communication with the master node. The master node is responsible for global task scheduling, topology management, and data aggregation and distribution. In static or low-speed scenarios, the master node can stably cover the entire swarm. However, the nodes in a robot swarm often move unpredictably. For example, when performing search, rescue, or collaborative exploration tasks, nodes may be dispersed over a wide area, and the master node may move closer to the network edge. When the master node moves to an edge location, the quality of the wireless link between it and more remote child nodes in the swarm will significantly degrade, or even be completely interrupted, causing some child nodes to be unable to receive heartbeats or control commands in a timely manner. On the other hand, the master node may completely lose connection due to power depletion, hardware failure, or entering an area with no signal.

[0003] All of the above situations weaken or even paralyze the overall cluster's collaborative capabilities. To address this issue, a common approach is to introduce a backup node mechanism. This involves pre-electing several high-performing nodes from among the child nodes as candidate master nodes. When the master node fails, the backup nodes take over its work. Due to the presence of backup nodes, the master node needs to synchronize complete network status information (such as the location of all nodes, topology, task allocation, etc.) to each backup node in real-time or periodically. The more backup nodes there are, the larger the amount of synchronization data the master node needs to send. Therefore, maintaining backup nodes requires additional signaling overhead, which consumes valuable wireless channel resources. Furthermore, as the number of backup nodes increases, when re-determining a new master node, the backup nodes need to exchange information multiple times through election algorithms (such as the Bully algorithm, ring algorithm, priority-based election, etc.), significantly increasing network complexity and load. In addition, there are scenarios where backup nodes lose connection simultaneously with the master node. Summary of the Invention

[0004] This invention addresses the technical problems in existing technologies by providing a dynamic method for selecting the optimal center point of a robot swarm. When the master node is disconnected or located at the network edge, this method promotes a preferred child node to the master node to take over the entire network, preventing network-wide outages or poor communication quality in mobile collaborative networking. The method includes the following steps: The master node in the robot swarm network sorts each child node according to its location information, xRSSI information, load information, and remaining power at a preset period, and updates and publishes the optimal node list of the child nodes; the xRSSI information is the average RSSI of the child nodes from the network edge to the child nodes closer to the network center. When the master node loses contact or is determined to be an edge node of the robot cluster network based on the location information of each node in the current robot cluster network, the master node degenerates into a child node and stops broadcasting. If a child node does not receive an updated optimal node list from the master node within a preset time, the best surviving child node in the current optimal node list will be used as the updated master node. The updated master node sends master node broadcast / event information and receives child node access.

[0005] Preferably, it further includes: If the current optimal node list is not updated, the best surviving child node in the initial optimal node list will be used as the updated master node; the initial optimal node list is determined by the weight information randomly configured for each node, and the best node in the list will be used as the initial master node.

[0006] Preferably, updating the optimal node list includes the following steps: The master node selects the multiple child nodes closest to the network center as the optimal node list based on the network node distribution map. The master node selects multiple child nodes at the network edge as the worst node list based on the network node distribution map; the master node publishes the best node list and the worst node list. The child nodes in the optimal node list calculate the mean of the RSSI from the worst node to themselves to determine the xRSSI information; the child nodes in the optimal node list send their location information, xRSSI information, load information, and remaining power to the master node; the master node performs a weighted calculation based on the location information, xRSSI information, load information, and remaining power of the optimal child nodes, sorts the optimal node list, and publishes the sorted optimal node list.

[0007] Preferably, the master node receives the location information of the child nodes and constructs a network node distribution map based on the location information of all child nodes.

[0008] Preferably, the load information includes CPU utilization, memory utilization, and transmission resource utilization.

[0009] Preferably, it also includes receiving broadcast / event information from the new master node multiple times within a preset time after the original lost master node is restored, and then degenerating into a child node.

[0010] Preferably, the best nodes in the current best node list are synchronized sequentially until synchronization is successful and other child nodes can receive the broadcast / event information of the best node within a preset time. Then, the best node is designated as the updated master node.

[0011] This invention also proposes a dynamic selection system for the optimal center point of a robot swarm, the system comprising: The node list construction module is used to sort the sub-nodes according to their location information, xRSSI information, load information and remaining power at a preset period, and update and publish the optimal node list of sub-nodes; the xRSSI information is the average RSSI of sub-nodes at the network edge to sub-nodes closer to the network center. The master node update module is used to degenerate into a child node and stop broadcasting when the master node loses contact or is determined to be an edge node of the robot cluster network based on the location information of each node in the current robot cluster network; if the child node does not receive the updated optimal node list published by the master node within a preset time, the best surviving child node in the current optimal node list will be used as the updated master node.

[0012] The present invention also proposes a computer-readable storage medium storing a data processing program, which, when executed by a processor, implements the dynamic selection method for the optimal center point of the robot cluster.

[0013] The present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic selection method for the optimal center point of the robot cluster.

[0014] The beneficial effects of this invention are: This invention proposes a dynamic selection method for the optimal center point of a robot swarm. This method constructs an initial optimal node list based on node weight information and dynamically elects a master node. This avoids the significant signaling overhead caused by all backup nodes simultaneously maintaining state synchronization in traditional multi-backup schemes, significantly reducing control message congestion and energy consumption in wireless mesh networks. Secondly, the master node periodically reorders child nodes and updates the optimal node list based on location, xRSSI, load, and remaining battery power. This ensures the list always reflects the dynamic changes in the current network topology and node performance. The xRSSI information effectively characterizes the communication quality from edge nodes to the central area, thus guaranteeing the rationality and real-time nature of the optimal node list. When the master node loses connection due to undirected movement, the system does not need a global re-election. Instead, it directly selects the highest-priority surviving child node from the published latest optimal node list as the new master node and immediately broadcasts the master node change event. This mechanism avoids the risk of the master node and all backup nodes losing connection simultaneously. Because the candidate nodes in the optimal node list are scattered in different locations and ordered according to comprehensive performance, even if the original master node loses connection, a usable node can be quickly found from the list to take over, achieving fault recovery. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is a distribution diagram of cluster network nodes according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a master node disconnection scenario according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a scenario where the master node is located at the network edge according to an embodiment of the present invention; Figure 5 This is a flowchart of the master node update method after the master node is located at the network edge according to an embodiment of the present invention; Figure 6 This is a flowchart of the main node disconnection and reactivation degradation steps in an embodiment of the present invention; Figure 7 This is a flowchart illustrating the process of updating the master node and reconnecting other child nodes according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Example 1 This embodiment proposes a method for dynamically selecting the optimal center point of a robot swarm. The main steps are as follows: Figure 1 As shown, the method includes: S1: Based on the weight information configured for each node in the robot swarm network, construct an initial optimal node list and determine the optimal node in the list as the master node.

[0018] In the initial configuration phase, all nodes in the cluster network are configured with weight information manually or randomly. This weight information is the initial optimal list. At the beginning of the network, the TOP node is selected as the master node.

[0019] S2: The master node sorts the child nodes according to their location information, xRSSI information, load information and remaining power at a preset period, and updates and publishes the optimal node list of child nodes; xRSSI information is the average RSSI of child nodes from the network edge to the child nodes closer to the network center.

[0020] Specifically: S2.1: The master node selects the multiple child nodes closest to the network center as the optimal node list based on the network node distribution diagram. The network node distribution diagram is as follows: Figure 2 As shown, the master node receives the location information of the child nodes and constructs a system based on the location information of all child nodes.

[0021] S2.2: The master node selects multiple child nodes at the network edge as the worst node list based on the network node distribution map; the master node publishes the best node list and the worst node list.

[0022] S2.3: Child nodes in the optimal node list calculate the average RSSI from the worst node to themselves to determine xRSSI information; child nodes in the optimal node list send location information, xRSSI information, load information, and remaining power to the master node; the master node performs a weighted calculation based on the location information, xRSSI information, load information, and remaining power of the optimal child nodes, sorts the optimal node list, and publishes the sorted optimal node list. Load information includes CPU utilization, memory utilization, and transmission resource utilization.

[0023] S3: If the optimal node list update is not received from the master node within a preset time, the original master node becomes disconnected. A diagram illustrating the disconnection in the cluster network is shown below. Figure 3 As shown, the blue master node G is currently disconnected. At this point, the best surviving child node T from the current best node list is designated as the updated master node, and master node broadcast / event information is sent to receive child node connections. If the current best node list has not been updated, the best surviving child node from the initial best node list is designated as the updated master node.

[0024] Furthermore, this invention also addresses the method for updating the master node after it becomes an edge node due to dynamic movement, such as... Figure 4 As shown, Figure 4This is a schematic diagram of a scenario where the master node G is located at the network edge. In this case, the master node G may move to the network edge, resulting in weak communication quality or interruption of communication with the more remote child node T in the cluster.

[0025] Specific steps, such as Figure 5 As shown, it includes: S4.1: Based on the location information of each node in the current robot cluster network, determine whether the current master node is an edge node of the robot cluster network.

[0026] S4.2: When the master node is an edge node, stop broadcasting, degenerate into a child node, take the best surviving child node in the current best node list as the updated master node, send master node broadcast / event information, and receive child node access.

[0027] Furthermore, for the scenario of a master node losing contact and then reviving: (1) After all child nodes lose contact with the master node for time T1 and a new master node has been established, the original master node will no longer be regarded as the master node if the new master node is not invalid; (2) If the original master node receives broadcast / event information sent by the new master node multiple times within time T2 after reviving, it will enter the degenerate child node process, and the specific degenerate steps are as follows. Figure 6 As shown, it includes: S5.1: When the original disconnected master node receives broadcast / event information from the new master node multiple times within a preset time, it degenerates into a child node.

[0028] S5.2: Select the best node from the current list of best nodes and synchronize them sequentially until synchronization is successful and other child nodes can receive the broadcast / event information of the best node within a preset time. Then, the best node will be the updated master node.

[0029] To avoid situations where other child nodes lose synchronization and fail to become the master node, such as... Figure 7 The diagram shows the process of updating the master node and reconnecting other child nodes. At this point, it is necessary to determine the survival status of each node based on the optimal node list.

[0030] In addition, if the optimal node list has not been updated based on information such as location and RSSI, and the master node has lost contact, the surviving TOP node is selected as the current master node based on the initial optimal list.

[0031] Calculation diagram of optimal node S: ; In the formula, This represents the distance from the node to the center of the robot cluster network coverage graph. The network coverage graph is constructed using the least squares method. This represents the remaining power of the device at this node. This provides information on the node's load, including CPU utilization, memory usage, and transmission resource (bandwidth) utilization. This is the mean RSSI from this node to the worst node.

[0032] The above is one embodiment of the method for dynamically selecting the optimal center point of a robot swarm provided in this example. Based on the same idea, this example also provides a corresponding system for dynamically selecting the optimal center point of a robot swarm. The node list construction module is used to sort the sub-nodes according to their location information, xRSSI information, load information and remaining power at a preset period, and update and publish the optimal node list of sub-nodes; the xRSSI information is the average RSSI of sub-nodes at the network edge to sub-nodes closer to the network center. The master node update module is used to degenerate into a child node and stop broadcasting when the master node loses contact or is determined to be an edge node of the robot cluster network based on the location information of each node in the current robot cluster network; if the child node does not receive the updated optimal node list published by the master node within a preset time, the best surviving child node in the current optimal node list will be used as the updated master node.

[0033] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A method for dynamically selecting the optimal center point of a robot cluster is provided.

[0034] Those skilled in the art will understand that implementing all or part of the processes in the methods of the above embodiments can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamically selecting the optimal center point of a robot swarm, characterized in that, Includes the following steps: The master node in the robot swarm network sorts each child node according to its location information, xRSSI information, load information, and remaining power at a preset period, and updates and publishes the optimal node list of the child nodes; the xRSSI information is the average RSSI of the child nodes from the network edge to the child nodes closer to the network center. When the master node loses contact or is determined to be an edge node of the robot cluster network based on the location information of each node in the current robot cluster network, it degenerates into a child node and stops broadcasting; If a child node does not receive an updated optimal node list from the master node within a preset time, the best surviving child node in the current optimal node list will be used as the updated master node. The updated master node sends master node broadcast / event information and receives child node access.

2. The method for dynamic selection of the optimal center point of a robot swarm according to claim 1, characterized in that, It also includes the following: when the current optimal node list is not updated, the best surviving child node in the initial optimal node list is used as the updated master node; the initial optimal node list is determined by the weight information randomly configured for each node, and the best node in the list is used as the initial master node.

3. The method for dynamic selection of the optimal center point of a robot swarm according to claim 1, characterized in that, The update of the optimal node list includes the following steps: The master node selects the multiple child nodes closest to the network center as the optimal node list based on the network node distribution map. The master node selects multiple child nodes at the network edge as the worst node list based on the network node distribution map; the master node publishes the best node list and the worst node list. The child nodes in the optimal node list calculate the mean of the RSSI from the worst node to themselves to determine the xRSSI information; the child nodes in the optimal node list send their location information, xRSSI information, load information, and remaining power to the master node; the master node performs a weighted calculation based on the location information, xRSSI information, load information, and remaining power of the optimal child nodes, sorts the optimal node list, and updates and publishes the sorted optimal node list.

4. The method for dynamic selection of the optimal center point of a robot swarm according to claim 3, characterized in that, The master node receives the location information of the child nodes and constructs a network node distribution map based on the location information of all child nodes.

5. The method for dynamic selection of the optimal center point of a robot swarm according to claim 3, characterized in that, The load information includes CPU utilization, memory usage, and transmission resource utilization.

6. The method for dynamic selection of the optimal center point of a robot swarm according to claim 1, characterized in that, It also includes the ability to receive broadcast / event information from the new master node multiple times within a preset time after the original lost master node is restored, thus degenerating into a child node.

7. The method for dynamic selection of the optimal center point of a robot swarm according to claim 1, characterized in that, The best nodes in the current best node list are synchronized sequentially until synchronization is successful and other child nodes can receive the broadcast / event information of the best node within a preset time. Then, the best node becomes the updated master node.

8. A dynamic selection system for the optimal center point of a robot swarm, characterized in that, The system includes: The node list construction module is used to sort the sub-nodes according to their location information, xRSSI information, load information and remaining power at a preset period, and update and publish the optimal node list of sub-nodes; the xRSSI information is the average RSSI of sub-nodes at the network edge to sub-nodes closer to the network center. The master node update module is used to degenerate into a child node and stop broadcasting when the master node loses contact or is determined to be an edge node of the robot cluster network based on the location information of each node in the current robot cluster network; if the child node does not receive the updated optimal node list published by the master node within a preset time, the best surviving child node in the current optimal node list will be used as the updated master node.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the dynamic selection method for the optimal center point of a robot swarm as described in any one of claims 1 to 7.

10. A computer device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the dynamic selection method for the optimal center point of a robot swarm, as described in any one of claims 1 to 7.

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