LoRa-based low-delay high-reliability communication method and system for multi-robot cooperation

Abstract

The invention provides a LoRa-based low-delay and high-reliability communication method and system for multi-robot cooperation. The LoRa-based low-delay and high-reliability communication method and system are provided. The method belongs to the technical field of industrial wireless communication, multi-agent system and edge cooperative control crossing. The method comprises the following steps: carrying out decentralized ad hoc network topology construction on a multi-robot system, and generating direct communication link data between robots; a dynamic neighbor discovery protocol is deployed according to direct communication link data between robots, so that a decentralized LoRa network topology structure is constructed; by constructing the decentralized LoRa network, the direct communication capability between the robots is improved, the limitation of dependence on a central gateway traditionally is broken, the communication hop count and time delay are reduced, and the collaborative operation is more efficient. The microsecond-level time synchronization mechanism enhances the accuracy of multi-robot action coordination, avoids the problems of formation advancing deviation and the like caused by clock drift, and ensures smooth development of a coordination task.

Description

Technical Field

[0001] This invention proposes a low-latency, high-reliability communication method and system based on LoRa for multi-robot collaboration, belonging to the cross-technical fields of industrial wireless communication, multi-agent systems, and edge collaborative control. Background Technology

[0002] With the continuous development of industrial automation and intelligence, multi-robot collaborative operations are widely used in many fields due to their advantages such as high efficiency and flexibility. However, multi-robot collaborative communication faces many challenges, and traditional communication solutions are unable to meet its stringent requirements.

[0003] Current LoRa-based multi-robot communication solutions have significant drawbacks. Mainstream LoRaWAN uses a star topology, relying on a central gateway. All robots must pass through the gateway, preventing direct robot-to-robot (D2D) communication. This not only increases the number of communication hops but also leads to a significant increase in latency. Furthermore, the LoRa physical layer lacks a built-in time synchronization mechanism. During multi-robot coordinated actions, clock drift issues can easily cause deviations in tasks such as formation movement, severely impacting coordination. Additionally, under CSMA / CA or ALOHA mechanisms, concurrent transmissions from multiple nodes result in severe channel collisions, with the collision rate increasing quadratically with the number of nodes. Moreover, its retransmission strategy is coarse, employing a fixed number of blind retransmissions, wasting valuable air interface resources and failing to distinguish between critical and non-critical messages.

[0004] Existing publicly available information mostly focuses on LoRa's application in wide-area low-power scenarios, such as AGV positioning and remote monitoring, or simply mentions LoRaP2P. It fails to transform LoRa into a self-organizing collaborative network that supports time synchronization, dynamic TDMA, and hierarchical QoS guarantees, making it difficult to meet the stringent communication requirements of multi-robot collaboration. Therefore, a new communication method is urgently needed. Summary of the Invention

[0005] This invention provides a low-latency, high-reliability communication method and system based on LoRa for multi-robot collaboration, in order to solve the problems mentioned in the background art above:

[0006] This invention proposes a low-latency, high-reliability communication method based on LoRa for multi-robot collaborative surface-mount communication, the method comprising:

[0007] S1. Construct a decentralized self-organizing network topology for the multi-robot system and generate direct communication link data between robots; deploy a dynamic neighbor discovery protocol based on the direct communication link data between robots to construct a decentralized LoRa network topology.

[0008] S2. Perform microsecond-level time synchronization processing based on the decentralized LoRa network topology to generate multi-robot synchronization clock data; implement a dynamic TDMA channel allocation mechanism based on the multi-robot synchronization clock data to divide the channel resources for concurrent communication of multiple robots and obtain dynamic TDMA channel allocation data.

[0009] S3. Based on the dynamic TDMA channel allocation data, send multi-robot messages to generate original communication message data and message priority identification data; perform hierarchical reliable transmission processing on the original communication message data according to the message priority identification data, and use an adaptive retransmission strategy to prioritize the retransmission of key messages to generate hierarchical reliable transmission message data.

[0010] S4. Monitor link status by transmitting message data in a hierarchical manner and generate link quality assessment data; implement a link prediction mechanism based on the link quality assessment data to predict links with communication failures in advance and generate link prediction result data; adjust the communication links for rerouting based on the link prediction result data and generate optimized communication link data.

[0011] S5. Perform end-to-end latency and reliability statistics on the multi-robot system based on the optimized communication link data, and generate end-to-end latency data and communication reliability data; perform weighted comprehensive evaluation based on the end-to-end latency data and communication reliability data to generate a communication performance evaluation index; perform risk warning and optimization adjustment processing based on the communication performance evaluation index to generate multi-robot collaborative communication optimization strategy data.

[0012] This invention proposes a system for implementing a LoRa-based low-latency, high-reliability communication method for multi-robot collaboration as described above, the system comprising:

[0013] Network construction module: Constructs a decentralized self-organizing network topology for a multi-robot system, generating direct-to-deployment (D2D) communication link data between robots; deploys a dynamic neighbor discovery protocol based on the direct-to-deployment communication link data between robots, thereby constructing a decentralized LoRa network topology;

[0014] Resource allocation module: Performs microsecond-level time synchronization processing based on the decentralized LoRa network topology to generate multi-robot synchronization clock data; Implements a dynamic TDMA channel allocation mechanism based on the multi-robot synchronization clock data to allocate channel resources for concurrent communication of multiple robots and obtain dynamic TDMA channel allocation data;

[0015] Message retransmission module: Sends multi-robot messages based on dynamic TDMA channel allocation data, generating original communication message data and message priority identifier data; performs hierarchical reliable transmission processing on the original communication message data according to the message priority identifier data, and adopts an adaptive retransmission strategy to prioritize the retransmission of key messages, generating hierarchical reliable transmission message data.

[0016] Link optimization module: Monitors link status through hierarchical reliable transmission message data and generates link quality assessment data; implements a link prediction mechanism based on the link quality assessment data to predict links with communication failures in advance and generate link prediction result data; adjusts the communication links for rerouting based on the link prediction result data and generates optimized communication link data.

[0017] Strategy optimization module: Based on the optimized communication link data, end-to-end latency and reliability statistics of the multi-robot system are performed, generating end-to-end latency data and communication reliability data; a weighted comprehensive evaluation is performed based on the end-to-end latency data and communication reliability data to generate a communication performance evaluation index; risk warning and optimization adjustment are performed based on the communication performance evaluation index to generate multi-robot collaborative communication optimization strategy data.

[0018] The beneficial effects of this invention are as follows: By constructing a decentralized LoRa network, the direct communication capability between robots is improved, breaking the limitations of traditional reliance on a central gateway, reducing communication hop count and latency, and making collaborative operations more efficient. The microsecond-level time synchronization mechanism enhances the accuracy of multi-robot motion coordination, avoiding problems such as formation deviation caused by clock drift, and ensuring the smooth execution of collaborative tasks. Dynamic TDMA channel allocation and hierarchical reliable transmission strategies reduce channel conflicts and resource waste, improve the reliability of critical message transmission, and avoid collaborative errors caused by message loss or delay. Link prediction and fast rerouting mechanisms can both predict communication failures in advance and adjust communication links in a timely manner, reducing the risk of communication interruption and ensuring communication stability. This method not only meets the high real-time and high-reliability communication requirements of industrial multi-robot systems but also adapts to complex and ever-changing operating environments, providing strong communication support for multi-robot collaborative operations. Attached Figure Description

[0019] Figure 1 This is a diagram illustrating the steps of the method described in this invention;

[0020] Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] One embodiment of the present invention, such as Figure 1 As shown, a low-latency, high-reliability communication method based on LoRa for multi-robot collaboration is disclosed, the method comprising:

[0023] S1. Construct a decentralized self-organizing network topology for the multi-robot system and generate robot-to-robot (D2D) communication link data; deploy a dynamic neighbor discovery protocol based on the robot-to-robot direct communication link data to construct a decentralized LoRa network topology.

[0024] S2. Perform microsecond-level time synchronization processing based on the decentralized LoRa network topology to generate multi-robot synchronization clock data; implement a dynamic TDMA channel allocation mechanism based on the multi-robot synchronization clock data to divide the channel resources for concurrent communication of multiple robots and obtain dynamic TDMA channel allocation data.

[0025] S3. Based on the dynamic TDMA channel allocation data, send multi-robot messages to generate original communication message data and message priority identification data; perform hierarchical reliable transmission processing on the original communication message data according to the message priority identification data, and use an adaptive retransmission strategy to prioritize the retransmission of key messages to generate hierarchical reliable transmission message data.

[0026] S4. Monitor link status by transmitting message data in a hierarchical manner and generate link quality assessment data; implement a link prediction mechanism based on the link quality assessment data to predict links that may experience communication failures in advance and generate link prediction result data; perform rapid rerouting adjustment on communication links based on the link prediction result data to generate optimized communication link data.

[0027] S5. Perform end-to-end latency and reliability statistics on the multi-robot system based on the optimized communication link data, and generate end-to-end latency data and communication reliability data; perform weighted comprehensive evaluation based on the end-to-end latency data and communication reliability data to generate a communication performance evaluation index; perform risk warning and optimization adjustment processing based on the communication performance evaluation index to generate multi-robot collaborative communication optimization strategy data.

[0028] The working principle and effects of the above technical solution are as follows: This solution enhances the flexibility and anti-interference capabilities of multi-robot communication networking through decentralized self-organizing network topology construction, avoiding overall communication paralysis caused by single-point failures in centralized architectures. Microsecond-level time synchronization combined with dynamic TDMA channel allocation effectively reduces the probability of channel collisions and improves the efficiency of concurrent communication among multiple robots. Hierarchical reliable transmission and adaptive retransmission strategies strengthen the reliability of critical message transmission and reduce data packet loss. Link status monitoring and predictive rerouting mechanisms can proactively avoid collaborative interruptions caused by communication failures, reducing communication latency fluctuations. Through end-to-end performance evaluation and dynamic optimization, it can both guarantee low-latency communication requirements and continuously improve communication reliability, ensuring the stable and efficient operation of multi-robot collaborative work.

[0029] In one embodiment of the present invention, S1 includes:

[0030] S11. Collect the position coordinates, communication capabilities, and task requirements data of the multi-robot system, and generate a basic information set of robot nodes;

[0031] S12. Based on the robot node basic information set, a distributed topology negotiation algorithm is used to initialize and construct the decentralized self-organizing network topology, generating a candidate set of robot-to-robot (D2D) communication links.

[0032] S13. Filter the candidate set of D2D communication links by signal strength and interference threshold to generate valid D2D communication link data;

[0033] S14. Based on the valid D2D communication link data, deploy a distance-aware dynamic neighbor discovery protocol to update the robot node connection status in real time.

[0034] S15. Based on the node connection dynamic data fed back by the neighbor discovery protocol, construct a decentralized LoRa network topology with self-healing capabilities.

[0035] The working principle and effects of the above technical solution are as follows: By first collecting basic information about robot nodes and then performing topology initialization, the decentralized self-organizing network is built to better suit the actual communication capabilities and task requirements of multiple robots, avoiding link redundancy or weak connections caused by blind networking. Signal strength and interference filtering of the candidate communication links effectively eliminates inferior links, improves the communication stability of effective links, and reduces signal interference in subsequent communications. The dynamic neighbor discovery protocol updates node connection status in real time, enhancing the topology's adaptability to robot movement and preventing communication interruptions caused by lagging connection status after node position changes. The final self-healing topology further strengthens the LoRa network's fault tolerance, providing a stable infrastructure for multi-robot collaborative communication and ensuring the smooth operation of subsequent communication processes.

[0036] In one embodiment of the present invention, S2 includes:

[0037] S21. Obtain the node communication delay baseline data in the decentralized LoRa network topology and generate time synchronization preprocessing parameters;

[0038] S22. Based on the time synchronization preprocessing parameters, a combination of LoRa signal frame synchronization marking and crystal oscillator calibration is used to perform microsecond-level time synchronization initialization processing to generate multi-robot time synchronization reference values.

[0039] S23. Using the multi-robot time synchronization reference value as a reference, execute the distributed clock calibration algorithm to eliminate clock deviations between nodes and generate multi-robot synchronization clock data;

[0040] S24. Based on the multi-robot synchronization clock data, design a dynamic TDMA channel allocation framework and divide the communication time slots and channel resource pools.

[0041] S25. Based on the dynamic changes in robot task priority and communication load, the channel resource pool is dynamically adjusted and allocated to obtain dynamic TDMA channel allocation data.

[0042] The working principle and effects of the above technical solution are as follows: By first acquiring node communication delay reference data to generate time synchronization preprocessing parameters, and then combining LoRa signal frame synchronization markers and crystal oscillator calibration to achieve microsecond-level time synchronization, it can accurately eliminate clock deviations between multiple robot nodes, improve time synchronization accuracy, and avoid channel contention caused by clock asynchrony. A channel allocation framework is constructed based on the synchronized clock, and time slots are dynamically adjusted according to task priority and communication load, making channel resource allocation more aligned with actual communication needs, improving resource utilization, and reducing invalid resource occupation. Simultaneously, it can guarantee the supply of communication time slots for high-priority tasks, avoiding communication lag for critical tasks due to insufficient channel resources, building a stable channel foundation for concurrent communication of multiple robots, and ensuring the orderly and efficient transmission of subsequent data.

[0043] In one embodiment of the present invention, S25 includes:

[0044] S251. Based on the task load quantization dataset, analyze the differences in time slot requirements under different task scenarios and generate a time slot requirement matching table.

[0045] S252. Based on the time slot demand matching table, perform preliminary allocation of initial time slots in the channel resource pool to generate a preliminary time slot allocation scheme.

[0046] S253. Perform conflict detection and load balancing verification on the initial time slot allocation scheme, and generate time slot allocation optimization parameters;

[0047] S254. Based on the time slot allocation optimization parameters, adjust the time slot length and allocation order in the initial time slot allocation scheme to generate dynamic TDMA channel allocation data.

[0048] The working principle and effects of the above technical solution are as follows: By analyzing the differences in time slot requirements across different task scenarios, a matching table is generated, making the initial time slot allocation more closely match the actual task load and avoiding resource waste or insufficient time slot supply for critical tasks caused by blind allocation. Conflict detection and load balancing verification of the initial allocation scheme can accurately identify potential time slot overlap issues, reduce channel conflicts during communication, and balance the load pressure of each link, avoiding communication congestion caused by excessive local load. Adjusting the time slot length and allocation order based on optimized parameters makes channel resource allocation more dynamically adaptable, flexibly adjusting to changes in task load and further improving channel resource utilization. The overall process ensures both the rationality of time slot allocation and the stability of channel usage, providing strong support for the orderly conduct of concurrent communication among multiple robots.

[0049] In one embodiment of the present invention, S254 includes:

[0050] Based on the time slot adjustment reference data, the time slot length adaptation deviation in the initial time slot allocation scheme is analyzed, and the length adjustment difference is generated.

[0051] Based on the length adjustment difference, the time slot length corresponding to each task is finely corrected one by one to generate a time slot length adjustment sub-scheme;

[0052] By combining the time slot adjustment baseline data and the time slot length adjustment sub-scheme, the load superposition nodes in the time slot allocation sequence are identified, and the sequence optimization direction is generated.

[0053] Following the optimization sequence, the allocation order of time slots for each node is rearranged to generate a time slot order adjustment sub-scheme; the time slot length adjustment sub-scheme and the time slot order adjustment sub-scheme are merged to form the adjusted time slot allocation scheme.

[0054] The adjusted time slot allocation scheme is subjected to load verification and secondary conflict investigation to generate scheme verification results. Based on the scheme verification results, the residual deviations in the adjusted time slot allocation scheme are corrected to generate dynamic TDMA channel allocation data.

[0055] The working principle and effects of the above technical solution are as follows: By analyzing the length adaptation deviation of the initial time slot allocation scheme to generate a length adjustment difference, the mismatch between time slots and task requirements can be accurately located, avoiding resource waste caused by excessively long time slots or incomplete data transmission caused by excessively short time slots. Each time slot length is refined to improve the adaptability between time slots and the transmission requirements of each task, making resource allocation more accurate. By sorting out the load superposition nodes, determining the order optimization direction, and reordering them, the load concentration caused by overlapping time slot allocation sequences can be reduced, lowering the probability of communication congestion. After integrating the two adjustment sub-schemes, load verification and secondary conflict investigation are carried out to avoid communication risks caused by residual deviations after adjustment, ensuring the reliability of the solution. Finally, residual deviations are corrected to generate allocation data, further improving channel resource utilization efficiency, making TDMA channel allocation more closely aligned with dynamic load changes, and providing accurate time slot guarantees for stable and efficient communication among multiple robots.

[0056] In one embodiment of the present invention, S3 includes:

[0057] S31. Based on the dynamic TDMA channel allocation data, determine the communication time slot window and transmission power parameters of each robot node, and generate message sending configuration instructions;

[0058] S32. Based on the message sending configuration instructions, multiple robot nodes perform task-related data collection and message encapsulation to generate raw communication message data;

[0059] S33. Based on the urgency of the task and the importance of the data corresponding to the message, a hierarchical priority evaluation model is introduced to add message priority identification data (high / medium / low levels) to the original communication message data.

[0060] S34. Based on the message priority identifier data, implement a hierarchical transmission strategy for the original communication message data: high-priority messages are transmitted using redundant encoding, medium-priority messages are transmitted using conventional verification, and low-priority messages are transmitted using compression, generating hierarchical preprocessed message data.

[0061] S35. Real-time transmission status monitoring is performed on the hierarchical preprocessed message data. An adaptive retransmission strategy based on link quality is adopted to prioritize the retransmission of failed frames of high-priority critical messages, and finally generate hierarchical reliable transmission message data.

[0062] The working principle and effects of the above technical solution are as follows: Communication parameters are determined based on dynamic TDMA channel allocation data to generate transmission configuration instructions, making message sending by each robot node more targeted and avoiding transmission disorder or power waste caused by parameter mismatch. Adding priority identifiers to messages clearly distinguishes the importance of different task messages, preventing critical task messages from being delayed due to resource contention caused by low-priority messages. Implementing a hierarchical transmission strategy, high-priority messages use redundant coding to enhance reliability, while low-priority messages are compressed for transmission to save resources, ensuring the transmission security of core task data and improving overall channel resource utilization. Real-time monitoring of transmission status and priority retransmission of high-priority failed frames reduce packet loss of critical messages, preventing multi-robot collaboration interruptions due to core data transmission failures, ultimately improving the overall reliability of communication data transmission and providing stable data support for collaborative operations.

[0063] In one embodiment of the present invention, S35 includes:

[0064] Noise filtering and data standardization are performed on the raw transmission status monitoring data to generate transmission status assessment results; link packet loss rate, transmission delay and signal stability parameters are extracted from the transmission status assessment results to generate link quality characteristic data.

[0065] By combining link quality characteristic data, a retransmission trigger threshold and a retransmission count limit are set to generate adaptive retransmission control parameters; based on the transmission status assessment results, transmission failure frames in high-priority messages are filtered to generate a high-priority failure frame dataset.

[0066] According to the adaptive retransmission control parameters, perform priority retransmission operation on the high-priority failed frame dataset to generate a retransmission data transmission stream; monitor the reception status of the retransmission data transmission stream and generate retransmission result feedback data.

[0067] Based on the retransmission result feedback data, integrate the hierarchical preprocessed message data of successful transmission with the retransmission success data to generate a complete transmission dataset;

[0068] The system performs consistency checks on the transmitted complete dataset, corrects deviations during data transmission, and generates hierarchical reliable transmission message data.

[0069] The working principle and effects of the above technical solution are as follows: Noise filtering and standardization of the raw transmission status monitoring data effectively eliminates interfering data, improves the accuracy of transmission status assessment results, and avoids link quality judgment deviations caused by noise. Key link quality parameters are extracted from the assessment results, allowing retransmission control parameters to be set more closely to the actual link conditions, preventing resource waste or insufficient retransmission of critical messages due to unreasonable retransmission trigger thresholds or number of retransmissions. Prioritizing retransmission of high-priority failed frames ensures the integrity of core task data transmission and prevents multi-robot collaboration disruptions caused by critical message loss. Real-time monitoring of retransmission status and integration of successful data reduce data transmission incompleteness and ensure the effectiveness of transmitting complete datasets. Finally, consistency checks correct deviations, further improving the accuracy of hierarchically reliable transmission of message data, preventing erroneous data from entering subsequent communication processes, and providing stable, high-quality data transmission guarantees for multi-robot collaborative operations.

[0070] In one embodiment of the present invention, step S4 includes:

[0071] S41. Based on the received feedback information of hierarchical reliable transmission message data, extract key indicators, including key indicators such as link packet loss rate, signal-to-noise ratio (SNR) and transmission delay, and generate raw link status monitoring data.

[0072] S42. Perform sliding window filtering and outlier removal on the raw link status monitoring data to generate standardized link quality assessment data;

[0073] S43. Based on standardized link quality assessment data, an LSTM time-series prediction model is introduced to construct a link status prediction mechanism to predict the link performance change trend within a preset time window in the future.

[0074] S44. Based on the output of the LSTM model and combined with the fault threshold judgment rules, identify the risk links that may cause faults. The risk links include communication interruption, sudden delay, etc., and generate link prediction result data (normal link / risk link / fault warning link).

[0075] S45. For the risky links and fault warning links in the link prediction results data, start the distributed fast rerouting algorithm, search for the optimal backup communication link, complete the link switching and resource reallocation, and generate optimized communication link data.

[0076] The working principle and effects of the above technical solution are as follows: Key link indicators are extracted from the received feedback of hierarchical reliable transmission messages to generate raw monitoring data. Then, through sliding window filtering and outlier removal, data interference is effectively purified, improving the accuracy of link quality assessment and avoiding misjudgments of link status caused by noisy data. The introduction of an LSTM time-series prediction model to predict link performance change trends breaks the traditional post-fault handling mode, enabling early perception of link risks and avoiding communication interruptions caused by the inability to respond promptly to sudden link failures. Accurate identification of risky links using fault thresholds makes link optimization more targeted and reduces resource consumption from ineffective troubleshooting. For risky links, a fast rerouting algorithm is initiated to switch to the optimal backup link and reallocate resources, quickly restoring communication, reducing communication latency caused by link failures, and enhancing the network's fault tolerance. The overall process improves the accuracy of link status monitoring and ensures communication continuity, providing a solid communication guarantee for the stable operation of multi-robot collaborative work.

[0077] In one embodiment of the present invention, S45 includes:

[0078] Based on the detailed risk link data, the information of adjacent nodes in the decentralized LoRa network topology is traversed to generate a candidate set of backup nodes; the link connectivity detection and signal strength test are performed on the candidate set of backup nodes to generate backup node availability status data.

[0079] Filter the available status data of backup nodes to construct a set of candidate backup links and generate a candidate link list; calculate the expected values ​​of transmission delay, packet loss rate and resource utilization of each link in the candidate link list to generate candidate link performance evaluation data.

[0080] The candidate link performance evaluation data are comprehensively sorted, and the link with the best performance is selected as the target backup link to generate the optimal backup link scheme. Based on the optimal backup link scheme, the original link disconnection command and the new link connection configuration parameters are generated to form a link switching command set.

[0081] Execute the link switching instruction set to complete the switching between the risky link and the target backup link, and generate link switching status feedback data; based on the link switching status feedback data, reallocate the communication resources of the link after the switch, adjust the time slot allocation and transmission power, and generate a resource reallocation scheme.

[0082] Integrate the link switching results and resource reallocation scheme, verify link connectivity and resource adaptability, and generate a verification pass signal; based on the verification pass signal, summarize the connection information and resource configuration data of all links after the switch, and generate optimized communication link data.

[0083] The working principle and effects of the above technical solution are as follows: By traversing adjacent nodes in the network topology to generate a candidate set of backup nodes, the selection range of backup links is enriched, avoiding the problem of having no suitable alternative links when risky links occur. Connectivity detection and signal strength testing of backup nodes can accurately screen effective nodes, improve the reliability of candidate backup links, and prevent the selection of inferior links from causing a decline in communication quality after switching. The transmission delay, packet loss rate, and resource utilization rate of each candidate link are calculated and comprehensively ranked to ensure that the selected target backup link has optimal performance, reducing the probability of communication failure after switching. Rapid link switching and resource reallocation reduce communication interruption time and prevent multi-robot collaborative operations from stalling due to link failures. Finally, connectivity and resource adaptability verification eliminates potential hidden dangers after switching, ensuring that the optimized links can stably adapt to communication requirements, thus enhancing the network's fault resistance and ensuring the continuity and stability of multi-robot collaborative communication.

[0084] In one embodiment of the present invention, step S5 includes:

[0085] S51. Based on the optimized communication link data, deploy end-to-end performance monitoring probes to collect data on transmission latency, data throughput, and message delivery rate in real time during the multi-robot collaborative communication process, and generate a raw performance statistics set.

[0086] S52. Perform data cleaning and normalization on the original performance statistics data set to separate standardized end-to-end latency data and communication reliability data (message delivery rate, bit error rate).

[0087] S53. Based on the requirements of multi-robot collaborative tasks for latency and reliability, set dynamic weighting coefficients, perform weighted calculations on standardized end-to-end latency data and communication reliability data, and generate a communication performance evaluation index.

[0088] S54. Compare the communication performance evaluation index with the preset performance thresholds (minimum acceptable threshold / optimal threshold) to identify performance shortcomings and potential risks, and generate risk warning information (latency exceeding the standard warning / insufficient reliability warning / normal).

[0089] S55. Based on risk warning information and communication performance evaluation index, adjust LoRa communication parameters and topology optimization strategies in a targeted manner. The communication parameters include transmission power, encoding method and time slot length, and generate multi-robot collaborative communication optimization strategy data.

[0090] The working principle and effects of the above technical solution are as follows: Based on optimized link data, an end-to-end performance monitoring probe is deployed, enabling real-time and comprehensive collection of key communication performance data, avoiding performance evaluation bias caused by incomplete data collection. The raw data is cleaned and normalized to improve accuracy and standardization, making subsequent performance analysis more reliable and preventing dirty data from interfering with evaluation results. An evaluation index is calculated by setting dynamic weighting coefficients based on the collaborative task requirements, making performance evaluation more aligned with actual task scenarios and avoiding the inadequacy of adaptability issues caused by uniform evaluation standards. Comparing the evaluation index with preset thresholds identifies performance shortcomings, providing early warning of potential risks and preventing reactive measures only after performance degradation impacts operations. Targeted adjustments to communication parameters and topology ensure that optimization measures precisely address performance defects, effectively improving communication latency and reliability while reducing resource waste caused by blind adjustments. The final generated collaborative communication optimization strategy data forms a performance optimization closed loop, enhancing the network's adaptability and ensuring long-term stable and efficient multi-robot collaborative communication.

[0091] In one embodiment of the present invention, S55 includes:

[0092] Based on the detailed data of adjustment requirements, the two directions of communication parameter adjustment and topology optimization are separated to generate a two-dimensional adjustment framework. For the communication parameter direction in the two-dimensional adjustment framework, the gap between the current configuration of transmission power, encoding method, and time slot length and the performance requirements is analyzed to generate parameter adjustment difference values.

[0093] Based on the parameter adjustment difference, the transmission power is gradient corrected one by one, the encoding method is adapted and switched, and the time slot length is precisely fine-tuned to generate communication parameter adjustment sub-schemes;

[0094] In response to the topology structure direction in the dual-dimensional adjustment framework, identify nodes and links in the current topology that are overloaded or have weak connections, and generate topology optimization targets; around the topology optimization targets, plan node connection reconstruction paths and redundant link supplementation schemes, and generate topology structure optimization sub-schemes;

[0095] The communication parameter adjustment sub-scheme and the topology optimization sub-scheme are integrated to form a preliminary cooperative communication optimization strategy; the performance of the preliminary cooperative communication optimization strategy is simulated and deduced to simulate the adjusted latency and reliability data and generate strategy simulation results.

[0096] By comparing the simulation results of the strategy with the preset performance target, the deviation terms in the preliminary cooperative communication optimization strategy are corrected, and the final cooperative communication optimization strategy is generated. All adjustment parameters and topology configuration information in the final cooperative communication optimization strategy are summarized to generate multi-robot cooperative communication optimization strategy data.

[0097] The working principle and effects of the above technical solution are as follows: A dual-dimensional adjustment framework, splitting communication parameters and topology, comprehensively covers the core directions of performance optimization, avoiding incomplete optimization caused by single-dimensional adjustments. Analyzing the gap between the current parameter configuration and requirements generates scores, allowing for more precise adjustments to transmission power, encoding methods, and time slot lengths, reducing resource consumption from blind corrections. Identifying overloaded or weak nodes in the topology as optimization targets allows for targeted planning of connection reconstruction and redundancy supplementation, enhancing network anti-congestion and anti-fragmentation capabilities, and preventing the spread of local problems from affecting overall communication. After integrating sub-solutions, performance simulations are performed to proactively identify optimization deviations and prevent latency rebound or reliability degradation after strategy implementation. Finally, deviations are corrected to generate optimized data, making the collaborative communication strategy more aligned with actual needs. This not only continuously improves communication performance but also strengthens long-term network stability, while avoiding a disconnect between optimization measures and actual scenarios, providing accurate and sustainable communication assurance for multi-robot collaborative operations.

[0098] One embodiment of the present invention, such as Figure 2 As shown, a system for implementing a LoRa-based low-latency, high-reliability communication method for multi-robot collaboration as described above is provided, the system comprising:

[0099] Network construction module: Constructs a decentralized self-organizing network topology for a multi-robot system, generating direct-to-deployment (D2D) communication link data between robots; deploys a dynamic neighbor discovery protocol based on the direct-to-deployment communication link data between robots, thereby constructing a decentralized LoRa network topology;

[0100] Resource allocation module: Performs microsecond-level time synchronization processing based on the decentralized LoRa network topology to generate multi-robot synchronization clock data; Implements a dynamic TDMA channel allocation mechanism based on the multi-robot synchronization clock data to allocate channel resources for concurrent communication of multiple robots and obtain dynamic TDMA channel allocation data;

[0101] Message retransmission module: Sends multi-robot messages based on dynamic TDMA channel allocation data, generating original communication message data and message priority identifier data; performs hierarchical reliable transmission processing on the original communication message data according to the message priority identifier data, and adopts an adaptive retransmission strategy to prioritize the retransmission of key messages, generating hierarchical reliable transmission message data.

[0102] Link optimization module: Monitors link status through hierarchical reliable transmission message data and generates link quality assessment data; implements a link prediction mechanism based on the link quality assessment data to predict links that may experience communication failures in advance and generates link prediction result data; performs rapid rerouting adjustment on communication links based on the link prediction result data and generates optimized communication link data.

[0103] Strategy optimization module: Based on the optimized communication link data, end-to-end latency and reliability statistics of the multi-robot system are performed, generating end-to-end latency data and communication reliability data; a weighted comprehensive evaluation is performed based on the end-to-end latency data and communication reliability data to generate a communication performance evaluation index; risk warning and optimization adjustment are performed based on the communication performance evaluation index to generate multi-robot collaborative communication optimization strategy data.

[0104] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A low-latency, high-reliability communication method based on LoRa for multi-robot collaborative surface communication, characterized in that, The method includes: S1. Construct a decentralized self-organizing network topology for the multi-robot system and generate direct communication link data between robots; deploy a dynamic neighbor discovery protocol based on the direct communication link data between robots to construct a decentralized LoRa network topology. S2. Perform microsecond-level time synchronization processing based on the decentralized LoRa network topology to generate multi-robot synchronization clock data; implement a dynamic TDMA channel allocation mechanism based on the multi-robot synchronization clock data to divide the channel resources for concurrent communication of multiple robots and obtain dynamic TDMA channel allocation data. S3. Based on the dynamic TDMA channel allocation data, send multi-robot messages to generate original communication message data and message priority identification data; perform hierarchical reliable transmission processing on the original communication message data according to the message priority identification data, and use an adaptive retransmission strategy to prioritize the retransmission of key messages to generate hierarchical reliable transmission message data. S4. Monitor link status by transmitting message data in a hierarchical manner and generate link quality assessment data; implement a link prediction mechanism based on the link quality assessment data to predict links with communication failures in advance and generate link prediction result data; adjust the communication links for rerouting based on the link prediction result data and generate optimized communication link data. S5. Based on the optimized communication link data, perform end-to-end latency and reliability statistics on the multi-robot system, generate end-to-end latency data and communication reliability data; perform weighted comprehensive evaluation, generate communication performance evaluation index, perform risk warning and optimization adjustment processing, and generate multi-robot collaborative communication optimization strategy data.

2. The low-latency, high-reliability communication method based on LoRa for multi-robot collaboration in a planar manner according to claim 1, characterized in that, S1 includes: S11. Collect the position coordinates, communication capabilities, and task requirements data of the multi-robot system, and generate a basic information set of robot nodes; S12. Based on the robot node basic information set, a distributed topology negotiation algorithm is used to initialize and construct the decentralized self-organizing network topology, generating a candidate set of direct communication links between robots. S13. Filter the candidate set of D2D communication links by signal strength and interference threshold to generate valid D2D communication link data; S14. Based on the valid D2D communication link data, deploy a distance-aware dynamic neighbor discovery protocol to update the robot node connection status in real time. S15. Based on the node connection dynamic data fed back by the neighbor discovery protocol, construct a decentralized LoRa network topology with self-healing capabilities.

3. The low-latency, high-reliability communication method based on LoRa for multi-robot collaboration according to claim 1, characterized in that, The S2 includes: S21. Obtain the node communication delay baseline data in the decentralized LoRa network topology and generate time synchronization preprocessing parameters; S22. Based on the time synchronization preprocessing parameters, a combination of LoRa signal frame synchronization marking and crystal oscillator calibration is used to perform microsecond-level time synchronization initialization processing to generate multi-robot time synchronization reference values. S23. Using the multi-robot time synchronization reference value as a reference, execute the distributed clock calibration algorithm to eliminate clock deviations between nodes and generate multi-robot synchronization clock data; S24. Based on the multi-robot synchronization clock data, design a dynamic TDMA channel allocation framework and divide the communication time slots and channel resource pools. S25. Based on the dynamic changes in robot task priority and communication load, the channel resource pool is dynamically adjusted and allocated to obtain dynamic TDMA channel allocation data.

4. The low-latency, high-reliability communication method based on LoRa for multi-robot collaboration in a planar manner according to claim 3, characterized in that, The S25 includes: S251. Based on the task load quantization dataset, analyze the differences in time slot requirements under different task scenarios and generate a time slot requirement matching table. S252. Based on the time slot demand matching table, perform preliminary allocation of initial time slots in the channel resource pool to generate a preliminary time slot allocation scheme. S253. Perform conflict detection and load balancing verification on the initial time slot allocation scheme, and generate time slot allocation optimization parameters; S254. Based on the time slot allocation optimization parameters, adjust the time slot length and allocation order in the initial time slot allocation scheme to generate dynamic TDMA channel allocation data.

5. The low-latency, high-reliability communication method based on LoRa for multi-robot collaboration in a planar manner according to claim 4, characterized in that, S254 includes: Based on the time slot adjustment reference data, the time slot length adaptation deviation in the initial time slot allocation scheme is analyzed, and the length adjustment difference is generated. Based on the length adjustment difference, the time slot length corresponding to each task is finely corrected one by one to generate a time slot length adjustment sub-scheme; By combining the time slot adjustment baseline data and the time slot length adjustment sub-scheme, the load superposition nodes in the time slot allocation sequence are identified, and the sequence optimization direction is generated. Following the optimization sequence, the allocation order of time slots for each node is rearranged to generate a time slot order adjustment sub-scheme; the time slot length adjustment sub-scheme and the time slot order adjustment sub-scheme are merged to form the adjusted time slot allocation scheme. The adjusted time slot allocation scheme is subjected to load verification and secondary conflict investigation to generate scheme verification results. Based on the scheme verification results, the residual deviations in the adjusted time slot allocation scheme are corrected to generate dynamic TDMA channel allocation data.

6. The LoRa-based low-latency, high-reliability communication method for multi-robot collaboration in a planar manner according to claim 1, characterized in that, The S3 includes: S31. Based on the dynamic TDMA channel allocation data, determine the communication time slot window and transmission power parameters of each robot node, and generate message sending configuration instructions; S32. Based on the message sending configuration instructions, multiple robot nodes perform task-related data collection and message encapsulation to generate raw communication message data; S33. Based on the urgency of the task and the importance of the data corresponding to the message, a hierarchical priority evaluation model is introduced to add message priority identifier data to the original communication message data; S34. Based on the message priority identifier data, implement a hierarchical transmission strategy for the original communication message data: high-priority messages are transmitted using redundant encoding, medium-priority messages are transmitted using conventional verification, and low-priority messages are transmitted using compression, generating hierarchical preprocessed message data. S35. Real-time transmission status monitoring is performed on the hierarchical preprocessed message data. An adaptive retransmission strategy based on link quality is adopted to prioritize the retransmission of failed frames of high-priority critical messages, and finally generate hierarchical reliable transmission message data.

7. The low-latency, high-reliability communication method based on LoRa for multi-robot collaboration in a planar manner according to claim 1, characterized in that, The S4 includes: S41. Based on the received feedback information of hierarchical reliable transmission message data, extract key indicators and generate raw link status monitoring data; S42. Perform sliding window filtering and outlier removal on the raw link status monitoring data to generate standardized link quality assessment data. S43. Based on standardized link quality assessment data, an LSTM time-series prediction model is introduced to construct a link status prediction mechanism to predict the link performance change trend within a preset time window in the future. S44. Based on the output of the LSTM model and the fault threshold judgment rule, identify the risk links that have failed and generate link prediction result data. S45. For the risky links and fault warning links in the link prediction results data, start the distributed fast rerouting algorithm, search for the optimal backup communication link, complete the link switching and resource reallocation, and generate optimized communication link data.

8. The LoRa-based low-latency, high-reliability communication method for multi-robot collaboration in a planar manner according to claim 7, characterized in that, The S45 includes: Based on the detailed risk link data, the information of adjacent nodes in the decentralized LoRa network topology is traversed to generate a candidate set of backup nodes; the link connectivity detection and signal strength test are performed on the candidate set of backup nodes to generate backup node availability status data. Filter the available status data of backup nodes to construct a set of candidate backup links and generate a candidate link list; calculate the expected values ​​of transmission delay, packet loss rate and resource utilization of each link in the candidate link list to generate candidate link performance evaluation data. The candidate link performance evaluation data are comprehensively sorted, and the link with the best performance is selected as the target backup link to generate the optimal backup link scheme. Based on the optimal backup link scheme, the original link disconnection command and the new link connection configuration parameters are generated to form a link switching command set. Execute the link switching instruction set to complete the switching between the risky link and the target backup link, and generate link switching status feedback data; based on the link switching status feedback data, reallocate the communication resources of the link after the switch, adjust the time slot allocation and transmission power, and generate a resource reallocation scheme. Integrate the link switching results and resource reallocation scheme, verify link connectivity and resource adaptability, and generate a verification pass signal; based on the verification pass signal, summarize the connection information and resource configuration data of all links after the switch, and generate optimized communication link data.

9. The low-latency, high-reliability communication method based on LoRa for multi-robot collaboration in a planar manner according to claim 1, characterized in that, The S5 includes: S51. Based on the optimized communication link data, deploy end-to-end performance monitoring probes to collect data on transmission latency, data throughput, and message delivery rate in real time during the multi-robot collaborative communication process, and generate a raw performance statistics set. S52. Perform data cleaning and normalization on the original performance statistics data set to separate standardized end-to-end latency data and communication reliability data. S53. Based on the requirements of multi-robot collaborative tasks for latency and reliability, set dynamic weighting coefficients, perform weighted calculations on standardized end-to-end latency data and communication reliability data, and generate a communication performance evaluation index. S54. Compare the communication performance evaluation index with the preset performance threshold to identify performance shortcomings and potential risks, and generate risk warning information. S55. Based on risk warning information and communication performance evaluation index, adjust LoRa communication parameters and topology optimization strategies in a targeted manner to generate multi-robot collaborative communication optimization strategy data.

10. A system for implementing the LoRa-based low-latency, high-reliability communication method for multi-robot collaboration as described in claim 1, characterized in that, The system includes: Network construction module: Constructs a decentralized self-organizing network topology for the multi-robot system, generates direct communication link data between robots, and deploys a dynamic neighbor discovery protocol based on the direct communication link data between robots, thereby constructing a decentralized LoRa network topology; Resource allocation module: Performs microsecond-level time synchronization processing based on the decentralized LoRa network topology to generate multi-robot synchronization clock data; Implements a dynamic TDMA channel allocation mechanism based on the multi-robot synchronization clock data to allocate channel resources for concurrent communication of multiple robots and obtain dynamic TDMA channel allocation data; Message retransmission module: Sends multi-robot messages based on dynamic TDMA channel allocation data, generating original communication message data and message priority identifier data; performs hierarchical reliable transmission processing on the original communication message data according to the message priority identifier data, and adopts an adaptive retransmission strategy to prioritize the retransmission of key messages, generating hierarchical reliable transmission message data. Link optimization module: Monitors link status through hierarchical reliable transmission message data and generates link quality assessment data; implements a link prediction mechanism based on the link quality assessment data to predict links with communication failures in advance and generate link prediction result data; adjusts the communication links for rerouting based on the link prediction result data and generates optimized communication link data. Strategy optimization module: Based on the optimized communication link data, end-to-end latency and reliability statistics of the multi-robot system are performed, generating end-to-end latency data and communication reliability data; a weighted comprehensive evaluation is performed based on the end-to-end latency data and communication reliability data to generate a communication performance evaluation index; risk warning and optimization adjustment are performed based on the communication performance evaluation index to generate multi-robot collaborative communication optimization strategy data.

Images