An AGV robot operation fault information feedback method, device and system

Abstract

The application relates to the technical field of digital information transmission, in particular to an AGV robot operation fault information feedback method, device and system. The method comprises the following steps: when a certain AGV robot (hereinafter referred to as "target robot") is detected to be interrupted in communication with a platform, a normally-operating AGV robot around the target robot is dispatched as a "mobile monitoring node" to cooperatively collect state information such as the position and speed of the target robot and upload the state information to the cloud platform; the platform dynamically optimizes a monitoring strategy, restores the motion trajectory of the target robot with high precision and finally intelligently judges the fault type of the target robot based on the discrete cooperative monitoring data, a preset route and an intelligent algorithm, so that reliable feedback of fault information of the target robot is realized without depending on the target robot itself, and the fault checking and disposal efficiency is improved since manual on-site inspection is not needed.

Description

Technical Field

[0001] This application belongs to the field of digital information transmission technology, specifically relating to a method, device and system for transmitting AGV robot operation fault information. Background Technology

[0002] In continuous operation scenarios such as intelligent warehousing and production lines, the stable operation of AGV robot clusters is crucial. If an AGV robot malfunctions and its fault information cannot be reported to the central management platform in a timely and accurate manner, it can easily lead to a chain reaction of production line congestion and task chain disruptions, causing significant production losses. Therefore, real-time and reliable fault information feedback is key to ensuring rapid system response and recovery.

[0003] Currently, the mainstream AGV fault information feedback mechanism relies on the faulty robot's own communication module (such as a 5G or Wi-Fi module) actively sending status data to the cloud platform. However, this method has inherent drawbacks: if the fault happens to occur in the communication module itself, or if a serious fault (such as a power supply or main control board failure) renders the communication module inoperable, the faulty robot will be "out of contact" and unable to report any information autonomously. In this situation, the management platform cannot detect the fault in a timely manner, nor can it obtain the real-time location and status of the faulty robot, resulting in a serious delay in fault diagnosis and handling.

[0004] Therefore, there is an urgent need for a redundant information feedback mechanism that does not rely on the robot's own communication capabilities, so as to ensure that the platform can still obtain its key fault information through other means when the robot is "out of contact". Summary of the Invention

[0005] To address the aforementioned issues, this application provides a method, device, and system for transmitting AGV robot operation fault information.

[0006] According to a first aspect of the embodiments of this application, a method for transmitting AGV robot operation fault information is provided, the method comprising: When the first target robot fails to communicate with the cloud platform multiple times in a row, a second target robot that is working normally around the first target robot is scheduled to collect and report the status information of the first target robot. The status information includes the information collection timestamp. Based on the information collection timestamp, the dynamic attention level of the first target robot is obtained; The preset basic monitoring frequency is optimized based on the dynamic attention level to determine the theoretical required monitoring frequency; Based on the discrete position coordinate sequence of the first target robot obtained from the monitoring frequency of the theoretical requirement, an interpolation algorithm is used to obtain the continuous motion trajectory of the first target robot during the communication interruption. The continuous motion trajectory is compared with the preset path of the first target robot to obtain the overall trajectory deviation. The fault information of the first target robot is determined based on the overall trajectory deviation, and the fault information is transmitted back to the cloud platform.

[0007] In one implementation, obtaining the dynamic attention level of the first target robot based on the information collection timestamp includes: Based on the information collection timestamp, obtain the time interval between the (n-1)th monitoring and the nth monitoring; Obtain the historical average value of the previous n-1 monitoring time intervals; Obtain the cumulative duration from the start of the communication interruption to the nth monitoring session; The dynamic attention level of the first target robot is obtained based on the time interval, the historical average value, and the cumulative duration.

[0008] In one implementation, optimizing the preset basic monitoring frequency based on the dynamic attention level to determine the theoretically required monitoring frequency includes: Based on the dynamic attention level and the basic monitoring frequency, a preliminary optimization frequency is obtained; Obtain the importance coefficient of the preset path; The upper limit of the monitoring frequency is obtained based on the system's currently available communication bandwidth, total communication bandwidth, and the importance coefficient of the preset path; The theoretical required monitoring frequency is determined based on the smaller of the preliminary optimized frequency and the upper limit of the monitoring frequency.

[0009] In one implementation, obtaining the importance coefficient of the preset path includes: Obtain the length of the preset path itself; Obtain the number of intersections between the preset path and other robot-planned paths; The basic value of the preset path is determined based on its own length and the number of intersections. Obtain the mean of the base values ​​for all planned paths; The importance coefficient of the preset path is obtained based on the base value of the preset path and the average of the base values.

[0010] In one implementation, the discrete position coordinate sequence of the first target robot obtained based on the theoretical requirement monitoring frequency is used to obtain the continuous motion trajectory of the first target robot during the communication interruption using an interpolation algorithm, including: Obtain the degree of speed anomaly of the second target robot; The degree of speed anomaly is normalized into a correlation index; The smoothing factor of the B-spline interpolation algorithm is dynamically adjusted based on the correlation index. Based on the discrete position coordinate sequence, B-spline interpolation is performed using a dynamically adjusted smoothing factor to obtain the continuous motion trajectory of the first target robot during the communication interruption.

[0011] In one implementation, obtaining the degree of speed anomaly of the second target robot includes: Obtain the number of the second target robots; Obtain the real-time velocity of each second target robot at the position where it passes the first target robot; Obtain the global planning average velocity at the location of the first target robot; The degree of speed anomaly of the second target robot is obtained based on the number of the second target robots, the real-time speed, and the global planning average speed.

[0012] In one implementation, comparing the continuous motion trajectory with a preset path of the first target robot to obtain the overall trajectory deviation includes: Obtain the deviation value of the first target robot relative to the preset path at each time point; Obtain the weighted sum of the deviation values; Obtain the sum of the weighted coefficients; The overall trajectory deviation is obtained based on the weighted sum of the deviation values ​​and the sum of the weighting coefficients.

[0013] In one implementation, before using an interpolation algorithm to obtain the continuous motion trajectory of the first target robot during the communication interruption from the discrete position coordinate sequence of the first target robot obtained based on the theoretical requirement monitoring frequency, the method further includes: Obtain the total monitoring frequency that all robots currently in operation can provide; If the theoretical required monitoring frequency is greater than the total monitoring frequency, then the number of robots that need to be additionally scheduled is calculated, and the number of robots that need to be additionally scheduled are scheduled from the robots that are not in operation to join the monitoring work, so as to make up for the monitoring capacity gap.

[0014] According to a second aspect of the embodiments of this application, an AGV robot operation fault information feedback device is provided, the AGV robot operation fault information feedback device being deployed on an edge computing node, the device comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method of any one of the first aspects.

[0015] According to a third aspect of the embodiments of this application, an AGV robot operation fault information feedback system is provided. The system includes a cloud platform, the AGV robot operation fault information feedback device described in the second aspect, and multiple robots. Each robot is configured with a first communication module that communicates with the AGV robot operation fault information feedback device, and a second communication module for sensing and data interaction with adjacent robots. After detecting that a first target robot has failed to communicate with the cloud platform multiple times consecutively, the AGV robot operation fault information feedback device sends a monitoring command to a second target robot through the first communication module. The second target robot senses the status information of the first target robot through the second communication module and reports the status information to the AGV robot operation fault information feedback device through the first communication module.

[0016] The embodiments of this application have the following beneficial effects: The embodiments of this application propose a redundant backhaul scheme based on cluster collaborative perception and intelligent trajectory analysis. Its core is that when an AGV robot (hereinafter referred to as "target robot") is detected as having interrupted communication with the platform, the surrounding normally operating AGV robots are scheduled as "mobile monitoring nodes" to collaboratively collect the target robot's position, speed and other status information and upload it to the cloud platform. Based on these discrete collaborative monitoring data, the platform combines preset routes and intelligent algorithms to dynamically optimize the monitoring strategy, accurately reconstruct the target robot's motion trajectory, and finally intelligently determine its fault type, thereby achieving reliable backhaul of fault information without relying on the faulty robot itself. Since no manual on-site inspection is required, the efficiency of fault diagnosis and handling is improved.

[0017] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0018] To more clearly illustrate the implementation schemes of this application, the accompanying drawings used in the implementation schemes will be briefly introduced below. It should be understood that the accompanying drawings only show some implementation schemes of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from the accompanying drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an AGV robot operation fault information feedback method according to an exemplary embodiment.

[0020] Figure 2This is a flowchart illustrating a method for obtaining dynamic attention of a first target robot according to an exemplary embodiment.

[0021] Figure 3 This is a flowchart illustrating a method for determining the monitoring frequency of theoretical requirements according to an exemplary embodiment.

[0022] Figure 4 This is a flowchart illustrating a method for obtaining the importance coefficient of a preset path according to an exemplary embodiment.

[0023] Figure 5 This is a flowchart illustrating a method for acquiring the continuous motion trajectory of a first target robot during a communication interruption, according to an exemplary embodiment.

[0024] Figure 6 This is a flowchart illustrating a method for obtaining the degree of speed anomaly of a second target robot according to an exemplary embodiment.

[0025] Figure 7 This is a flowchart illustrating a method for obtaining overall trajectory deviation according to an exemplary embodiment.

[0026] Figure 8 This is a flowchart illustrating yet another method for transmitting AGV robot operation fault information according to an exemplary embodiment.

[0027] Figure 9 This is a block diagram illustrating an AGV robot operation fault information feedback device according to an exemplary embodiment.

[0028] Figure 10 This is a block diagram illustrating an AGV robot operation fault information feedback system according to an exemplary embodiment. Detailed Implementation

[0029] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0030] It should be understood that the term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description.

[0031] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules, or units, and are not used to limit the order of functions performed by these devices, modules, or units or their interdependencies. The modifications of "one" and "multiple" mentioned in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless explicitly stated in the context, they should be understood as "one or more". In the description of this application, unless otherwise stated, "multiple" means two or more, and other quantifiers are similar; "at least one item", "one item or multiple items", or similar expressions refer to any combination of these items, including any combination of single or multiple items.

[0032] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of this application, this should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of this application, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.

[0033] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information. It is understood that before using the technical solutions of the various embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be disclosed to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means.

[0034] First, the application scenario of this application is described. In the collaborative operation scenario of AGV robots in an intelligent warehousing center, all robots have their routes planned and tasks scheduled uniformly by the cloud platform, and each robot has a unique identification code. The communication module of each robot includes two types of communication: communication with the cloud platform, edge computing nodes, and communication between the robot and itself. The robot only establishes communication with the adjacent tracks of its current track to avoid communication congestion and wasted computing power. The two modules are independent of each other and do not interfere with each other. They can collect the position coordinates and running status data such as travel speed of the robot and the adjacent track robots according to a unified coordinate system. When managing the robot's behavior route, the cloud platform can make real-time adjustments according to the specific scenario. For example, if robot A is stationary in channel A, and the adjacent robot was originally going to go through channel A, after discovering an obstacle, the edge computing node scheduling system will temporarily allocate a backup channel next to channel A. There are gaps between the robot's running tracks in the scenario. The width of the gap needs to ensure that at least one robot can pass normally, ensuring that subsequent robots can detour in case of congestion or other emergencies. The following describes this application with reference to specific embodiments.

[0035] Figure 1 This is a flowchart illustrating a method for transmitting operational fault information from an AGV robot, according to an exemplary embodiment. Figure 1 As shown in the figure, this application embodiment provides a method for transmitting AGV robot operation fault information, which may include the following steps: In step S10, when it is detected that the first target robot has failed to communicate with the cloud platform multiple times in a row, a second target robot that is working normally around the first target robot is scheduled to collect and report the status information of the first target robot. The status information includes the information collection timestamp.

[0036] In this step, when the first target robot fails to communicate with the cloud platform multiple times consecutively, a second target robot operating normally around the first target robot is scheduled to collect and report the status information of the first target robot. This status information includes the information collection timestamp. For example, the edge computing node continuously monitors the heartbeat signals or periodic status reports of all AGV robots. If no report information is received from the first target robot for a preset number of consecutive times (e.g., twice), it is determined that there is a risk of communication interruption, and it is immediately marked as a "suspected fault" state, triggering the collaborative monitoring process. The node broadcasts instructions to the AGV robots (second target robots) operating normally in the area where the first target robot is located and in adjacent areas, requiring them to collect and report information about the first target robot, including its coordinates (including the information collection timestamp) and its own speed, when passing near the first target robot. "Passing by" can be defined as entering a circular area centered on the first target robot with a certain radius (e.g., the vehicle's dimensions plus the average width of the track), and the distance is less than the maximum effective distance of the near-field obstacle avoidance sensor.

[0037] In step S20, the dynamic attention level of the first target robot is obtained based on the information collection timestamp.

[0038] In this step, the dynamic attention level of the first target robot is obtained based on the information collection timestamp. For example, the time interval from the (n-1)th monitoring to the nth monitoring can be obtained first based on the information collection timestamp, then the historical average of the time intervals of the previous n-1 monitoring can be obtained, then the cumulative duration from the start of the communication interruption to the nth monitoring can be obtained, and finally the dynamic attention level of the first target robot is obtained based on the time interval, the historical average, and the cumulative duration.

[0039] In step S30, the preset basic monitoring frequency is optimized based on the dynamic attention level to determine the theoretical required monitoring frequency.

[0040] In this step, the preset basic monitoring frequency is optimized based on dynamic attention to determine the theoretical required monitoring frequency. For example, a preliminary optimized frequency can be obtained first based on dynamic attention and the basic monitoring frequency, then the importance coefficient of the preset path can be obtained, then the upper limit of the monitoring frequency can be obtained based on the system's current available communication bandwidth, total communication bandwidth, and the importance coefficient of the preset path, and finally the theoretical required monitoring frequency can be determined based on the smaller value between the preliminary optimized frequency and the upper limit of the monitoring frequency.

[0041] In step S40, based on the discrete position coordinate sequence of the first target robot obtained from the monitoring frequency of the theoretical requirement, an interpolation algorithm is used to obtain the continuous motion trajectory of the first target robot during the communication interruption.

[0042] In this step, based on the discrete position coordinate sequence of the first target robot obtained from the monitoring frequency required by theoretical needs, an interpolation algorithm is used to obtain the continuous motion trajectory of the first target robot during the communication interruption. For example, the speed anomaly degree of the second target robot can be obtained first, then the speed anomaly degree can be normalized into a correlation index. Then, based on the correlation index, the smoothing factor of the B-spline interpolation algorithm can be dynamically adjusted. Finally, based on the discrete position coordinate sequence, B-spline interpolation is performed using the dynamically adjusted smoothing factor to obtain the continuous motion trajectory of the first target robot during the communication interruption.

[0043] In step S50, the continuous motion trajectory is compared with the preset path of the first target robot to obtain the overall trajectory deviation.

[0044] In this step, the continuous motion trajectory is compared with the preset path of the first target robot to obtain the overall trajectory deviation. For example, the deviation value of the first target robot relative to the preset path at each moment can be obtained first, then the weighted sum of the deviation values ​​can be obtained, then the sum of the weighted coefficients can be obtained, and finally the overall trajectory deviation can be obtained based on the weighted sum of the deviation values ​​and the sum of the weighted coefficients.

[0045] In step S60, the fault information of the first target robot is determined based on the overall trajectory deviation, and the fault information is transmitted back to the cloud platform.

[0046] In this step, the fault information of the first target robot is determined based on the overall trajectory deviation, and the fault information is transmitted back to the cloud platform. For example, the overall trajectory deviation can be normalized (e.g., divided by its maximum theoretical value or normalized to the [0,1] interval based on historical data) to obtain a normalized deviation. The edge computing node judges the relationship between the normalized deviation and a preset decision threshold (e.g., 0.8) and generates a final fault report, which is then transmitted back to the cloud platform. If the normalized deviation is less than the preset judgment threshold, the first target robot is determined to have a communication module failure, but the driving control system is basically normal, and the returned report contains information such as the fault location and low deviation.

[0047] If the normalized deviation is greater than or equal to the preset decision threshold, the first target robot is determined to have a combined communication and driving control fault, and the returned report includes information such as fault location, high deviation, and possible motion pattern analysis.

[0048] The report, along with key information such as the ID of the first target robot, the time of the failure start, and the restored trajectory fragments, was pushed to the cloud platform's operation and maintenance management interface to guide on-site handling.

[0049] This solution eliminates the need to rely on the robot's own communication module, ensuring timely feedback of fault information, facilitating rapid fault identification and shortening repair time. Since no manual on-site inspection is required, it improves the efficiency of fault diagnosis and handling.

[0050] Figure 2 This is a flowchart illustrating a method for obtaining the dynamic attention level of a first target robot according to an exemplary embodiment. Figure 2 As shown, obtaining the dynamic attention level of the first target robot based on the information collection timestamp may include the following steps: In step S201, the time interval between the (n-1)th monitoring and the nth monitoring is obtained based on the information collection timestamp.

[0051] In this step, the time interval between the (n-1)th monitoring and the nth monitoring is obtained based on the information collection timestamp. .

[0052] In step S202, the historical average value of the previous n-1 monitoring time intervals is obtained.

[0053] In this step, the historical average value of the previous n-1 monitoring time intervals is obtained. .

[0054] In step S203, the cumulative duration from the start of the communication interruption to the nth monitoring is obtained.

[0055] In this step, the cumulative duration from the start of the communication interruption to the nth monitoring session is obtained. .

[0056] In step S204, the dynamic attention level of the first target robot is obtained based on the time interval, the historical average value, and the cumulative duration.

[0057] In this step, according to the time interval Historical average and cumulative duration To obtain the dynamic attention of the first target robot. For example, the dynamic attention level of the first target robot. It can be obtained from the following formula: Formula 1 in, To obtain the maximum value, The minimum effective statistical time can be 1 second. It should be noted that when n=1, the corresponding dynamic attention level is directly set to 1 to avoid the problem of not being able to calculate the corresponding dynamic attention level when n=1.

[0058] The first part of the formula It is the ratio of the current monitoring interval to the historical average interval, reflecting the degree of deviation of the current monitoring interval from the historical average interval; This reflects the multiple of the cumulative anomaly duration relative to historical normal intervals. The larger the multiple, the longer the anomaly disrupts the normal process and the higher its severity. Multiplying these two values ​​yields the dynamic attention level of the first target robot. It can characterize the degree of suspicion of the first target robot.

[0059] Figure 3 This is a flowchart illustrating a method for determining the monitoring frequency of theoretical requirements according to an exemplary embodiment. Figure 3 As shown, optimizing the preset basic monitoring frequency based on the dynamic attention level to determine the theoretically required monitoring frequency may include the following steps: In step S301, a preliminary optimization frequency is obtained based on the dynamic attention level and the basic monitoring frequency.

[0060] In this step, based on dynamic attention and basic monitoring frequency Obtain the initial optimization frequency For example, initial frequency optimization. It can be obtained from the following formula: Formula 2 Basic monitoring frequency This is the routine monitoring schedule when there are no abnormalities, and the dynamic level of attention. The abnormal state of the disconnected robot (the first target robot) was quantified, and the two were multiplied to achieve dynamic optimization of the monitoring frequency according to the abnormal state of the first target robot.

[0061] In step S302, the importance coefficient of the preset path is obtained.

[0062] In this step, the importance coefficient of the preset path is obtained. For example, the length of the preset path itself can be obtained first, then the number of intersections between the preset path and other robot-planned paths can be obtained, then the base value of the preset path can be determined based on the preset path's own length and the number of intersections, then the average of the base values ​​of all planned paths can be obtained, and finally the importance coefficient of the preset path can be obtained based on the preset path's base value and the average of the base values.

[0063] In step S303, the upper limit of the monitoring frequency is obtained based on the system's currently available communication bandwidth, total communication bandwidth, and the importance coefficient of the preset path.

[0064] In this step, based on the system's currently available communication bandwidth... Total communication bandwidth And the importance coefficient I of the preset path, to obtain the upper limit of the monitoring frequency. For example, the upper limit of monitoring frequency. It can be obtained from the following formula: Formula 3 Where exp is an exponential function with the natural constant e as its base.

[0065] This is an upper limit constraint on system security. The more important the route and the more redundant the system communication bandwidth, the higher the upper limit of the monitoring frequency can be. The importance of a route is correlated with the base monitoring frequency. The larger the value of I, the higher the monitoring frequency should be, prioritizing the monitoring needs of critical operation paths. The denominator reflects the system's capacity constraints; the less remaining bandwidth, the larger the denominator, and the lower the upper limit monitoring frequency. This helps prevent monitoring tasks from consuming excessive resources.

[0066] In step S304, the theoretical required monitoring frequency is determined based on the smaller of the preliminary optimized frequency and the upper limit of the monitoring frequency.

[0067] In this step, based on the initial optimized frequency and monitoring frequency limit The smaller value in the range determines the theoretical required monitoring frequency. For example, the theoretical requirement monitoring frequency. It can be obtained from the following formula: Formula 4 in, To obtain the minimum value.

[0068] In this way, the monitoring frequency can be adapted to the abnormal state of the first target robot, while avoiding exceeding the system's carrying capacity, thus achieving a balance between monitoring efficiency and system stability.

[0069] Figure 4 This is a flowchart illustrating a method for obtaining the importance coefficient of a preset path according to an exemplary embodiment. Figure 4 As shown, obtaining the importance coefficient of the preset path may include the following steps: In step S3021, the length of the preset path itself is obtained.

[0070] In this step, the length L of the preset path is obtained.

[0071] In step S3022, the number of intersections between the preset path and other robot-planned paths is obtained.

[0072] In this step, the number of intersections JD between the preset path and other robot-planned paths is obtained.

[0073] In step S3023, the basic value of the preset path is determined based on the length of the preset path itself and the number of intersections.

[0074] In this step, the base value JC of the preset path is determined based on its length L and the number of intersections JD. For example, the base value JC of the preset path can be obtained using the following formula: JC=L JD Formula 5 It should be noted that, in order to avoid dimensional issues, both L and JD are divided by a unit quantity with a value of 1 and corresponding dimensions when participating in the calculation, thereby converting them into dimensionless data before participating in the calculation.

[0075] In step S3024, the mean of the base values ​​for all planned paths is obtained.

[0076] In this step, the mean of the base values ​​for all planned paths is obtained. .

[0077] In step S3025, the importance coefficient of the preset path is obtained based on the base value of the preset path and the average of the base values.

[0078] In this step, the base value JC of the preset path is compared with the average base value of all planned paths. Obtain the importance coefficient I of the preset path. For example, the importance coefficient I can be obtained by the following formula: I=JC / ( ) Formula 6 The embodiments of this application accurately quantify the importance of the path, making the subsequent monitoring frequency upper limit setting more reasonable and ensuring the monitoring priority of critical paths.

[0079] Figure 5This is a flowchart illustrating a method for acquiring the continuous motion trajectory of a first target robot during a communication interruption, according to an exemplary embodiment. Figure 5 As shown, the process of obtaining the continuous motion trajectory of the first target robot during the communication interruption by using an interpolation algorithm based on the discrete position coordinate sequence of the first target robot obtained from the monitoring frequency of the theoretical requirement may include the following steps: In step S401, the degree of speed anomaly of the second target robot is obtained.

[0080] In this step, the degree of speed anomaly of the second target robot is obtained. For example, the number of second target robots can be obtained first, then the real-time speed of each second target robot when it passes the position of the first target robot can be obtained, then the global planning average speed at the position of the first target robot can be obtained, and finally the degree of speed anomaly of the second target robot can be obtained based on the number of second target robots, their real-time speeds, and the global planning average speed.

[0081] In step S402, the degree of speed anomaly is normalized into a correlation index.

[0082] In this step, the degree of speed anomaly will be determined. Normalized to correlation index , making The value range is limited to 0 to 1. For example, maximum and minimum value normalization can be used to determine the degree of speed anomaly. Normalized to correlation index .

[0083] Correlation Index express The degree to which the second target robot is affected by the first target robot as it passes by.

[0084] In step S403, the smoothing factor of the B-spline interpolation algorithm is dynamically adjusted according to the correlation index.

[0085] In this step, based on the correlation index Dynamically adjust the smoothing factor of the B-spline interpolation algorithm. For example, a smoothing factor. It can be obtained from the following formula: Formula 7 in, for The smoothing factor is dynamically optimized at all times, determining The tightness of the time trajectory curve, Based on the smoothing factor, the inherent parameters of the robot trajectory fitting, such as a navigation accuracy of 0.05m, then , for The correlation index at any given moment reflects The real-time impact intensity of the primary target robot on its surroundings.

[0086] B-spline interpolation, by fitting a continuous smooth curve, avoids the stiffness and distortion caused by linearization in basic interpolation. It also eliminates the need to filter trajectory points, preserving the original information of discrete monitoring data. Its core parameter is the smoothing factor, used to control the tightness of the curve. However, a fixed smoothing factor lacks adaptability; setting it too high leads to overly smoothed curves, masking abnormal motion characteristics of the disconnected robot (the first target robot); setting it too low amplifies errors, causing meaningless trajectory swaying and failing to adapt to the dynamically changing motion state of the disconnected robot. Therefore, a correlation index is introduced to dynamically adjust the smoothing factor. The formula is as follows: Basic smoothing factor; The higher the value, the more necessary it is to reduce the smoothing factor to make the curve closely follow the discrete points. Therefore, with... As an adjustment factor.

[0087] In step S404, based on the discrete position coordinate sequence, B-spline interpolation is performed using a dynamically adjusted smoothing factor to obtain the continuous motion trajectory of the first target robot during the communication interruption.

[0088] In this step, the discrete position coordinate sequence of the first target robot, obtained based on the monitoring frequency of theoretical requirements, is interpolated using B-spline with a dynamically adjusted smoothing factor to obtain the continuous motion trajectory of the first target robot during the communication interruption.

[0089] Dynamically adjusting the smoothing factor makes the interpolation results more closely match the actual motion state of the first target robot, effectively improving the accuracy of restoring continuous motion trajectories during communication interruptions.

[0090] Figure 6 This is a flowchart illustrating a method for obtaining the degree of velocity anomaly of a second target robot according to an exemplary embodiment. Figure 6 As shown, obtaining the degree of speed anomaly of the second target robot may include the following steps: In step S4011, the number of the second target robots is obtained.

[0091] In this step, the total number M of the second target robots is obtained.

[0092] In step S4012, the real-time speed of each second target robot at the position where it passes the first target robot is obtained.

[0093] In this step, the position of the second target robot i at time t when it passes the first target robot is obtained. Real-time speed at the location .

[0094] In step S4013, the global planning average velocity at the position of the first target robot is obtained.

[0095] In this step, the position of the first target robot at time t is obtained. Global planning mean speed at the location For example, global planning mean speed. This can be used when there are no global exceptions. The average speed at that location.

[0096] In step S4014, the degree of speed anomaly of the second target robot is obtained based on the number of the second target robots, the real-time speed, and the global planning average speed.

[0097] In this step, based on the number M of the second target robots and their real-time speed... and global planning mean speed To obtain the second target robot in Speed ​​anomaly at any moment For example, the degree of speed anomaly. It can be obtained from the following formula: Formula 8 Where M represents the total number of the second target robots.

[0098] The formula takes the dynamic coordinates of the disconnected robot as its core, in At that time, using normal speed as the benchmark, the speed deviation is converted into a 0-1 range by 1 - real-time speed / normal speed; default time =0.1, which neither ignores the scenario where the disconnected robot has not been passed but still exists, nor exaggerates the impact, ensuring that the formula is continuously usable.

[0099] Figure 7 This is a flowchart illustrating a method for obtaining overall trajectory deviation according to an exemplary embodiment. Figure 7 As shown, comparing the continuous motion trajectory with the preset path of the first target robot to obtain the overall trajectory deviation may include the following steps: In step S501, the deviation value of the first target robot relative to the preset path is obtained at each time step.

[0100] In this step, the deviation value of the first target robot relative to the preset path is obtained at each moment. For example, the restored continuous motion trajectory of the first target robot during the communication interruption can be compared with a preset path, and for each aligned time point... Calculate the Euclidean distance between corresponding points on the two trajectories to obtain the deviation value. .

[0101] In step S502, the weighted sum of the deviation values ​​is obtained.

[0102] In this step, the correlation index is used. As a weighting factor, obtain the deviation value. The weighted sum value. For example, deviations from the numerical value. The weighted sum can be expressed as , where N is the total number of moments during which the first target robot was interrupted.

[0103] In step S503, the sum of the weighting coefficients is obtained.

[0104] In this step, the weighting coefficients are obtained. The sum of Where N is the total number of moments during which the first target robot experiences a communication interruption.

[0105] In step S504, the overall trajectory deviation is obtained based on the weighted sum of the deviation values ​​and the sum of the weighting coefficients.

[0106] In this step, the weighted sum of the deviations from the numerical values ​​is used. and the sum of the weighting coefficients. Obtain the overall trajectory deviation For example, the overall trajectory deviation. It can be obtained from the following formula: Formula 9 The overall trajectory deviation quantifies the severity of the first target robot's deviation from the preset path, can define the anomaly level, and provide data support for subsequent fault tracing.

[0107] Figure 8 This is a flowchart illustrating yet another method for transmitting AGV robot operational fault information, according to an exemplary embodiment. For example... Figure 8 As shown, before using an interpolation algorithm to obtain the continuous motion trajectory of the first target robot during the communication interruption from the discrete position coordinate sequence of the first target robot obtained based on the monitoring frequency of the theoretical requirement, the method may further include the following steps: In step S31, the total monitoring frequency that all robots currently in operation can provide is obtained.

[0108] In this step, the total monitoring frequency that all the second target robots currently in operation can provide is obtained. For example, the total monitoring frequency It can be obtained from the following formula: Formula 10 in, The average monitoring frequency that each robot can provide is non-zero, and M is the total number of the second target robots.

[0109] In step S32, if the theoretical required monitoring frequency is greater than the total monitoring frequency, the number of robots that need to be additionally scheduled is calculated, and the number of robots that need to be additionally scheduled are scheduled from the robots that are not in operation to join the monitoring work in order to make up for the monitoring capacity gap.

[0110] In this step, if the theoretical required monitoring frequency Greater than the total monitoring frequency Then calculate the number of robots that need to be rescheduled. The system then allocates additional robots from its non-working state to supplement the monitoring efforts, thereby filling the monitoring capacity gap. For example, the number of robots requiring additional allocation... It can be obtained from the following formula: Formula 11 in, To obtain the maximum value, This is for rounding up.

[0111] The embodiments of this application ensure that the theoretical requirements for monitoring frequency can be realized, avoid inadequate monitoring due to insufficient number of robots, and ensure the integrity of fault information collection.

[0112] This application also provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the AGV robot operation fault information feedback method provided in this application.

[0113] Figure 9 This is a block diagram illustrating an AGV robot operation fault information feedback device according to an exemplary embodiment. For example, the AGV robot operation fault information feedback device 900 can be deployed on an edge computing node. (Refer to...) Figure 9The AGV robot operation fault information feedback device 900 includes a processor 922, which further includes one or more processors, and memory resources represented by a memory 932 for storing instructions executable by the processor 922, such as application programs. The application programs stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 922 is configured to execute instructions to perform the aforementioned AGV robot operation fault information feedback method.

[0114] The AGV robot operation fault information feedback device 900 may further include a power supply component 926 configured to perform power management of the AGV robot operation fault information feedback device 900, a communication component 950 configured to connect the AGV robot operation fault information feedback device 900 to a network, and an input / output interface 958. The AGV robot operation fault information feedback device 900 can operate on an operating system stored in memory 932.

[0115] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a programmable electronic device, the computer program having a code portion for performing the above-described AGV robot operation fault information feedback method when executed by the programmable electronic device.

[0116] Figure 10 This is a block diagram illustrating an AGV robot operation fault information feedback system according to an exemplary embodiment. Figure 10 As shown in the figure, this application embodiment provides an AGV robot operation fault information feedback system 1000, including a cloud platform 1010, an AGV robot operation fault information feedback device 1020, and multiple robots 1030. Each robot is equipped with a first communication module for communicating with the AGV robot operation fault information feedback device 1020, and a second communication module for sensing and data interaction with adjacent robots. After detecting that a first target robot has failed to communicate with the cloud platform 1010 multiple times, the AGV robot operation fault information feedback device 1020 sends a monitoring command to a second target robot through the first communication module. The second target robot senses the status information of the first target robot through the second communication module and reports the status information to the AGV robot operation fault information feedback device 1020 through the first communication module.

[0117] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for transmitting AGV robot operation fault information, characterized in that, The method includes: When the first target robot fails to communicate with the cloud platform multiple times in a row, a second target robot that is working normally around the first target robot is scheduled to collect and report the status information of the first target robot. The status information includes the information collection timestamp. Based on the information collection timestamp, the dynamic attention level of the first target robot is obtained; The preset basic monitoring frequency is optimized based on the dynamic attention level to determine the theoretical required monitoring frequency; Based on the discrete position coordinate sequence of the first target robot obtained from the monitoring frequency of the theoretical requirement, an interpolation algorithm is used to obtain the continuous motion trajectory of the first target robot during the communication interruption. The continuous motion trajectory is compared with the preset path of the first target robot to obtain the overall trajectory deviation. The fault information of the first target robot is determined based on the overall trajectory deviation, and the fault information is transmitted back to the cloud platform; The step of comparing the continuous motion trajectory with the preset path of the first target robot to obtain the overall trajectory deviation includes: Obtain the deviation value of the first target robot relative to the preset path at each time point; Obtain the weighted sum of the deviation values; Obtain the sum of the weighted coefficients; The overall trajectory deviation is obtained based on the weighted sum of the deviation values ​​and the sum of the weighting coefficients.

2. The method for transmitting AGV robot operation fault information according to claim 1, characterized in that, The step of obtaining the dynamic attention level of the first target robot based on the information collection timestamp includes: Based on the information collection timestamp, obtain the time interval between the (n-1)th monitoring and the nth monitoring; Obtain the historical average value of the previous n-1 monitoring time intervals; Obtain the cumulative duration from the start of the communication interruption to the nth monitoring session; The dynamic attention level of the first target robot is obtained based on the time interval between the (n-1)th and nth monitoring, the historical average value, and the cumulative duration.

3. The method for transmitting AGV robot operation fault information according to claim 1, characterized in that, The step of optimizing the preset basic monitoring frequency based on the dynamic attention level to determine the theoretically required monitoring frequency includes: Based on the dynamic attention level and the basic monitoring frequency, a preliminary optimization frequency is obtained; Obtain the importance coefficient of the preset path; The upper limit of the monitoring frequency is obtained based on the system's currently available communication bandwidth, total communication bandwidth, and the importance coefficient of the preset path; The theoretical required monitoring frequency is determined based on the smaller of the preliminary optimized frequency and the upper limit of the monitoring frequency.

4. The method for transmitting AGV robot operation fault information according to claim 3, characterized in that, Obtaining the importance coefficient of the preset path includes: Obtain the length of the preset path itself; Obtain the number of intersections between the preset path and other robot-planned paths; The basic value of the preset path is determined based on its own length and the number of intersections. Obtain the mean of the base values ​​for all planned paths; The importance coefficient of the preset path is obtained based on the base value of the preset path and the average of the base values ​​of all planned paths.

5. The method for transmitting AGV robot operation fault information according to claim 1, characterized in that, The discrete position coordinate sequence of the first target robot, obtained based on the theoretical requirement monitoring frequency, is used to obtain the continuous motion trajectory of the first target robot during the communication interruption using an interpolation algorithm, including: Obtain the degree of speed anomaly of the second target robot; The degree of speed anomaly is normalized into a correlation index; The smoothing factor of the B-spline interpolation algorithm is dynamically adjusted based on the correlation index. Based on the discrete position coordinate sequence, B-spline interpolation is performed using a dynamically adjusted smoothing factor to obtain the continuous motion trajectory of the first target robot during the communication interruption.

6. The method for transmitting AGV robot operation fault information according to claim 5, characterized in that, The process of obtaining the degree of speed anomaly of the second target robot includes: Obtain the number of the second target robots; Obtain the real-time velocity of each second target robot at the position where it passes the first target robot; Obtain the global planning average speed at the location of the first target robot; the global planning average speed is the average speed at the location when there are no global anomalies; The degree of speed anomaly of the second target robot is obtained based on the number of the second target robots, the real-time speed, and the global planning average speed.

7. The method for transmitting AGV robot operation fault information according to claim 1, characterized in that, Before using an interpolation algorithm to obtain the continuous motion trajectory of the first target robot during the communication interruption, based on the discrete position coordinate sequence of the first target robot obtained from the monitoring frequency of the theoretical requirement, the method further includes: Obtain the total monitoring frequency that all robots currently in operation can provide; If the theoretical required monitoring frequency is greater than the total monitoring frequency, then the number of robots that need to be additionally scheduled is calculated, and the number of robots that need to be additionally scheduled are scheduled from the robots that are not in operation to join the monitoring work, so as to make up for the monitoring capacity gap.

8. An AGV robot operation fault information feedback device, characterized in that, The AGV robot operation fault information feedback device is deployed on an edge computing node, and the device includes: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-7.

9. An AGV robot operation fault information feedback system, characterized in that, The system includes a cloud platform, the AGV robot operation fault information feedback device as described in claim 8, and multiple robots. Each robot is equipped with a first communication module that communicates with the AGV robot operation fault information feedback device, and a second communication module for sensing and data interaction with adjacent robots. After detecting that the first target robot has failed to communicate with the cloud platform multiple times consecutively, the AGV robot operation fault information feedback device sends a monitoring command to the second target robot through the first communication module. The second target robot senses the status information of the first target robot through the second communication module and reports the status information to the AGV robot operation fault information feedback device through the first communication module.

Images