A method, device, and readable storage medium for monitoring robot trajectory.

Abstract

This application provides a method, device, and readable storage medium for monitoring robot running trajectory. The method includes: acquiring 4G location information reported by the robot; performing trajectory fitting based on the 4G location information to obtain the robot's running trajectory; matching the robot's running trajectory with a preset trajectory through trajectory segmentation to determine whether the robot has deviated; and outputting deviation warning information if the robot has deviated. This application can monitor robot running trajectory information in real time and promptly detect trajectory deviation problems. Furthermore, this method does not require the installation of hardware resources such as magnetic strips or QR codes, and does not require regular maintenance, greatly saving enterprise costs and achieving the goal of cost reduction and efficiency improvement.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, device and readable storage medium for monitoring robot trajectory. Background Technology

[0002] By monitoring the operation trajectory of robots in the factory area, the operation status of the robots in the factory area can be effectively grasped. Generally, after the operation trajectory of the robots in the factory area is preset in advance, the robots will operate according to the set trajectory. However, due to the relocation of some equipment and the accumulation of goods in the factory area, the robot's operation route will be affected. Although the robot will automatically plan the path and start running again, if the trajectory deviates for a long time, it will lead to problems such as the intersection of trajectories between robots and the excessively long operation trajectory. Such problems seriously affect the production efficiency of enterprises.

[0003] Currently, the main solution to this problem is to install magnetic strips and QR codes, but this method has drawbacks such as long setup time, low accuracy, the need for regular maintenance, and high hardware costs, which are not conducive to the long-term development of enterprises.

[0004] Therefore, how to achieve robot trajectory monitoring at a lower cost has become a problem that needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by this application is to address the above-mentioned shortcomings of the prior art by providing a robot trajectory monitoring method, device, and readable storage medium to solve the problems existing in the prior art.

[0006] In a first aspect, this application provides a method for monitoring the trajectory of a robot, wherein the robot-side 5G signal measurement gap (GAP) is configured with 4G signal measurement, and the method includes:

[0007] S1. Obtain the 4G location information reported by the robot;

[0008] S2. Perform trajectory fitting based on the 4G location information to obtain the robot's running trajectory;

[0009] S3. Match the robot's running trajectory with a preset trajectory by trajectory segmentation to determine whether the robot has deviated from its course;

[0010] S4. If the robot deviates from its intended path, output a deviation warning message.

[0011] In some embodiments, S1 includes:

[0012] Obtain the 4G network information reported by the robot, and extract the robot's ID, location, and timestamp from the 4G network information.

[0013] In some embodiments, S2 includes:

[0014] Based on the 4G network information corresponding to each ID, the data is sorted according to the timestamp, and the trajectory is spliced ​​based on the starting and ending location information to obtain the robot's running trajectory corresponding to each ID.

[0015] In some embodiments, S3 includes:

[0016] The robot's running trajectory and the preset trajectory are divided according to a preset length to obtain multiple running trajectory segments and preset trajectory segments;

[0017] The running trajectory segments are matched with the corresponding preset trajectory segments to obtain the matching results;

[0018] Based on the matching results of all running trajectory segments and corresponding preset trajectory segments, it is determined whether the robot has deviated.

[0019] In some embodiments, the running trajectory segments are matched with corresponding preset trajectory segments to obtain matching results, including:

[0020] Calculate the lateral offset distance between the running trajectory segment and the corresponding preset trajectory segment;

[0021] If the lateral offset distance between the running trajectory segment and the corresponding preset trajectory segment exceeds the preset distance threshold, it is determined that the running trajectory segment has deviated from the corresponding preset trajectory segment.

[0022] In some embodiments, determining whether the robot has deviated based on the matching results of all running trajectory segments and corresponding preset trajectory segments includes:

[0023] If the lateral offset distance of N consecutive running trajectory segments exceeds the preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

[0024] In some embodiments, determining whether the robot has deviated based on the matching results of all running trajectory segments and corresponding preset trajectory segments includes:

[0025] If the average lateral offset distance of N consecutive trajectory segments exceeds a preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

[0026] Secondly, this application provides a robot trajectory monitoring device, the device comprising:

[0027] The location acquisition module is configured to acquire the 4G location information reported by the robot.

[0028] The trajectory fitting module is configured to perform trajectory fitting based on the 4G location information to obtain the robot's running trajectory;

[0029] The trajectory matching module is configured to match the robot's running trajectory with a preset trajectory through trajectory segmentation to determine whether the robot has deviated from its course.

[0030] The deviation warning module is configured to output deviation warning information if the robot deviates from its intended path.

[0031] Thirdly, this application provides a robot trajectory monitoring device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement the robot trajectory monitoring method described in the first aspect above.

[0032] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the robot trajectory monitoring method described in the first aspect.

[0033] This application provides a robot trajectory monitoring method, device, and readable storage medium. The method includes: acquiring 4G location information reported by the robot; performing trajectory fitting based on the 4G location information to obtain the robot's trajectory; matching the robot's trajectory with a preset trajectory through trajectory segmentation to determine whether the robot has deviated; and outputting deviation warning information if the robot deviates. This application proposes a method for measuring robot position based on a 5G signal GAP. The main idea is to configure 4G signal measurement at the robot's 5G signal GAP using a 5G base station in the factory area. After enabling 4G measurement, the robot will report 4G location information. The backend receives the 4G location information and fits the robot trajectory to obtain the robot's running trajectory, thus monitoring whether the robot's trajectory has deviated. Furthermore, the signal transmission speed is fast, allowing for real-time monitoring of the robot's trajectory information and timely detection of trajectory deviation problems. This method also eliminates the need for installing magnetic strips, QR codes, or other hardware resources and requires no regular maintenance, significantly saving enterprise costs and achieving cost reduction and efficiency improvement. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0035] Figure 1 A flowchart illustrating a robot trajectory monitoring method provided in this application embodiment;

[0036] Figure 2A flowchart illustrating yet another robot trajectory monitoring method provided in this application embodiment;

[0037] Figure 3 A schematic diagram illustrating the matching of the running trajectory with the preset trajectory provided in this application embodiment.

[0038] Figure 4 This is a schematic diagram of the structure of a robot trajectory monitoring device provided in an embodiment of this application;

[0039] Figure 5 This is a schematic diagram of another robot trajectory monitoring device provided in an embodiment of this application.

[0040] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0042] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0043] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0044] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0045] It is understood that each unit or module involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0046] It is understood that the terms "first," "second," etc., used in the embodiments of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0047] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0048] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

[0049] It is understood that the units and modules involved in the embodiments of this application can be implemented by software or by hardware. For example, the units and modules can be located in the processor.

[0050] In the context of 5G fully connected factory applications, this application connects robots to the 5G network. During the 5G signal gap (GAP), 4G measurement signals are activated. The server receives the 4G signals reported by the robot, including its position information. By fitting the robot's point-like position to generate trajectory information, the robot's running trajectory can be monitored, allowing for timely detection of deviations from the robot's trajectory caused by equipment relocation or goods accumulation in the factory area. The GAP (measurement gap) is the period in 5G where the terminal (UE) does not actively communicate with the wireless network but performs measurements.

[0051] This application proposes a method for measuring robot position based on a 5G signal GAP. The main idea is to configure 4G signal measurement at the robot's 5G signal GAP using a 5G base station in the factory area. After 4G measurement is enabled, the robot will report its 4G position information. The backend receives the 4G position information and fits the robot trajectory to obtain the robot's running trajectory. This allows for monitoring whether the robot's trajectory has deviated. Moreover, the signal transmission speed is fast, allowing for real-time monitoring of the robot's running trajectory information and timely detection of trajectory deviation problems. At the same time, this method does not require the installation of hardware resources such as magnetic strips or QR codes, and does not require regular maintenance, which greatly saves enterprise costs and achieves the goal of cost reduction and efficiency improvement.

[0052] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0053] This application provides a method for monitoring the trajectory of a robot. The workflow of this method can be implemented by electronic devices, such as computers and handheld smart terminals. For ease of explanation, the implementation of the method is described in various embodiments of this application with the server as the main body.

[0054] Figure 1 This is a schematic diagram of the robot trajectory monitoring method provided in the embodiments of this application. Figure 2 Another schematic diagram of the robot trajectory monitoring method provided in the embodiments of this application is shown below. Figure 1 as well as Figure 2 As shown, this application provides a method for monitoring the trajectory of a robot. The robot end is configured with 4G signal measurement during the 5G signal measurement gap. The method includes S1-S4, as follows:

[0055] S1. Obtain the 4G location information reported by the robot;

[0056] Specifically, in 5G networks, to improve energy efficiency and network performance, terminal devices (in this scenario, robots) are allowed to disable 5G signal reception and transmission for certain specific time intervals, known as "measurement gaps" (GAPs). During these GAPs, the robot can be configured to perform 4G signal measurements. During the GAP, the robot initiates 4G signal measurements according to a preset strategy, including measuring 4G network signal strength, quality, neighbor cell information, and location information. The robot then sends the 4G signal measurement information to the 5G base station, which can then retrieve this information from the robot.

[0057] In some embodiments, S1 includes:

[0058] Obtain the 4G network information reported by the robot, and extract the robot's ID, location, and timestamp from the 4G network information.

[0059] Specifically, the system collects 4G network information reported by the robot in real time and performs data cleaning. It extracts information such as ID, location, and timestamp from the information, removes incomplete records such as those lacking latitude and longitude or timestamps, and imports the standardized data into the Hive data warehouse.

[0060] Hive is a data warehouse tool based on Hadoop. It can map structured data files to a database table and provide simple SQL query functionality, allowing users to query large-scale datasets stored in the Hadoop file system using a SQL-like language. Hive is suitable for data warehouse applications, especially those involving batch processing.

[0061] S2. Perform trajectory fitting based on the 4G location information to obtain the robot's running trajectory;

[0062] In some embodiments, S2 includes:

[0063] Based on the 4G network information corresponding to each ID, the data is sorted according to the timestamp, and the trajectory is spliced ​​based on the starting and ending location information to obtain the robot's running trajectory corresponding to each ID.

[0064] Specifically, the cleaned robot data is grouped according to robot ID, the operational data of each robot is identified, and then trajectory fitting is performed. The steps are as follows:

[0065] (1) First, group by robot ID, where one ID represents the running data of one robot;

[0066] (2) Sort the grouped data according to the timestamp and divide the data by the start and end point position information. The divided data is a running trajectory. Connect the point data of the running trajectory to convert it into line data, and take the timestamp of the first point as the timestamp of the trajectory. Output a trajectory information, which includes ID, timestamp, and line data, and store it in the robot trajectory table.

[0067] S3. Match the robot's running trajectory with a preset trajectory by trajectory segmentation to determine whether the robot has deviated from its course;

[0068] In some embodiments, S3 includes:

[0069] The robot's running trajectory and the preset trajectory are divided according to a preset length to obtain multiple running trajectory segments and preset trajectory segments;

[0070] The running trajectory segments are matched with the corresponding preset trajectory segments to obtain the matching results;

[0071] Based on the matching results of all running trajectory segments and corresponding preset trajectory segments, it is determined whether the robot has deviated.

[0072] Specifically, through trajectory segmentation technology, we can subdivide the robot's actual running trajectory into multiple small segments and precisely match each segment with the corresponding part of a preset trajectory. By segmenting and matching the trajectory, we can more accurately identify minute deviations in the robot's operation, thereby improving the robot's positioning accuracy.

[0073] In some embodiments, the running trajectory segments are matched with corresponding preset trajectory segments to obtain matching results, including:

[0074] Calculate the lateral offset distance between the running trajectory segment and the corresponding preset trajectory segment;

[0075] If the lateral offset distance between the running trajectory segment and the corresponding preset trajectory segment exceeds the preset distance threshold, it is determined that the running trajectory segment has deviated from the corresponding preset trajectory segment.

[0076] In some embodiments, determining whether the robot has deviated based on the matching results of all running trajectory segments and corresponding preset trajectory segments includes:

[0077] If the lateral offset distance of N consecutive running trajectory segments exceeds the preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

[0078] Specifically, during robot operation, the lateral offset distance of each trajectory segment is monitored in real time. Once the offset of N consecutive segments exceeds the threshold, it is immediately determined that the robot has deviated, thereby enhancing the real-time performance of deviation detection.

[0079] The value of N can be dynamically adjusted to change the number of trajectory segments N for continuous deviations based on the robot's operating environment. In complex environments, it may be necessary to decrease the value of N to improve the sensitivity of deviation detection; in simple environments, the value of N can be increased to reduce false alarms.

[0080] The above-mentioned solution can improve the safety of robot operation. By detecting deviations in a timely manner, it can prevent the robot from entering dangerous areas or performing incorrect tasks, thereby improving operational safety. Furthermore, it can reduce maintenance costs; timely detection of deviations can prevent equipment damage caused by prolonged deviations from the trajectory, thus reducing maintenance costs.

[0081] In some embodiments, determining whether the robot has deviated based on the matching results of all running trajectory segments and corresponding preset trajectory segments includes:

[0082] If the average lateral offset distance of N consecutive trajectory segments exceeds a preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

[0083] In this embodiment, the concept of a time window is introduced when calculating the average lateral offset distance. Only the trajectory segments within a specific time window are considered, which allows for more flexible handling of deviations at different speeds.

[0084] Optionally, a weighted average offset distance can be used: different weights are assigned to the offset distance of each segment based on the importance of each trajectory segment or the complexity of the environment, thereby calculating the weighted average offset distance.

[0085] In addition, adaptive threshold adjustment can be performed: the preset distance threshold is automatically adjusted based on historical data and the current operating environment to adapt to different operating conditions.

[0086] The above-described scheme reduces false alarms caused by random factors by calculating the average offset distance instead of the offset of individual segments, thereby improving detection accuracy and lowering the false alarm rate. Furthermore, it enhances adaptability; the method for calculating the average offset distance is more flexible and can adapt to changes in robot speed and environment, thus improving the overall adaptability of the system.

[0087] For example, Figure 3 This is a schematic diagram illustrating the matching of the running trajectory with the preset trajectory provided in the embodiments of this application, as shown below. Figure 3 As shown, the running trajectory and the preset trajectory are divided into segments of 10 meters each. A trajectory may be divided into multiple segments. Each segment is formed into a matching pair (r, m). A running trajectory and the preset trajectory will form multiple sets of matching pairs. Then, the lateral distance between each set of matching pairs is calculated. If the offset of a certain set exceeds the threshold of 2 meters for 5 consecutive times, or if the average offset of a certain set exceeds the threshold of 2 meters for 5 consecutive times, then the robot trajectory is determined to have deviated at that segment.

[0088] The average offset can be calculated using the following formula:

[0089]

[0090] Where l is the average offset, and l1 to l5 are the offsets of 5 consecutive times.

[0091] S4. If the robot deviates from its intended path, output a deviation warning message.

[0092] Specifically, if the robot deviates from its designated path, the system will output information confirming the deviation and notify monitoring personnel to inspect equipment relocation or cargo accumulation on-site. This information will be addressed promptly to ensure the robot's trajectory remains within the normal range and improve factory production efficiency.

[0093] This application proposes a method for measuring robot position based on a 5G signal GAP. The main idea is to configure 4G signal measurement at the robot's 5G signal GAP using a 5G base station in the factory area. After 4G measurement is enabled, the robot will report its 4G position information. The backend receives the 4G position information and fits the robot trajectory to obtain the robot's running trajectory. This allows for monitoring whether the robot's trajectory has deviated. Moreover, the signal transmission speed is fast, allowing for real-time monitoring of the robot's running trajectory information and timely detection of trajectory deviation problems. At the same time, this method does not require the installation of hardware resources such as magnetic strips or QR codes, and does not require regular maintenance, which greatly saves enterprise costs and achieves the goal of cost reduction and efficiency improvement.

[0094] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0095] Figure 4 This is a schematic diagram of the robot trajectory monitoring device provided in the embodiments of this application, as shown below. Figure 4 As shown, this application provides a robot trajectory monitoring device, the device comprising:

[0096] Location acquisition module 11 is configured to acquire 4G location information reported by the robot;

[0097] The trajectory fitting module 12 is configured to perform trajectory fitting based on the 4G location information to obtain the robot's running trajectory;

[0098] The trajectory matching module 13 is configured to match the robot's running trajectory with a preset trajectory through trajectory segmentation to determine whether the robot has deviated from its course.

[0099] Deviation warning module 14 is configured to output deviation warning information if the robot deviates from its intended path.

[0100] Regarding the limitations on the robot trajectory monitoring device, please refer to the limitations on the robot trajectory monitoring method in the above embodiments of this application, which will not be repeated here.

[0101] Figure 5 Another schematic diagram of the robot trajectory monitoring device provided in the embodiments of this application is shown below. Figure 5 As shown, in some embodiments, this application provides a robot trajectory monitoring device, including a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to execute the robot trajectory monitoring method in the above embodiments of this application.

[0102] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0103] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the robot trajectory monitoring method in the above embodiments of this application.

[0104] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0105] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A method for monitoring the trajectory of a robot, characterized in that, The robot-side 5G signal measurement gap (GAP) is configured with 4G signal measurement, and the method includes: S1. Obtain the 4G location information reported by the robot; S2. Perform trajectory fitting based on the 4G location information to obtain the robot's running trajectory; S3. Match the robot's running trajectory with a preset trajectory by trajectory segmentation to determine whether the robot has deviated from its course; S4. If the robot deviates from its intended path, output a deviation warning message. Wherein, S1 includes: Obtain the 4G network information reported by the robot, and extract the robot's ID, location, and timestamp from the 4G network information; S3 includes: The robot's running trajectory and the preset trajectory are divided according to a preset length to obtain multiple running trajectory segments and preset trajectory segments; The running trajectory segments are matched with the corresponding preset trajectory segments to obtain the matching results; Based on the matching results of all running trajectory segments and corresponding preset trajectory segments, it is determined whether the robot has deviated. Among them, determining whether the robot has deviated based on the matching results of all running trajectory segments and corresponding preset trajectory segments includes: If the lateral offset distance of N consecutive running trajectory segments exceeds the preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

2. The robot trajectory monitoring method according to claim 1, characterized in that, S2 includes: Based on the 4G network information corresponding to each ID, the data is sorted according to the timestamp, and the trajectory is spliced ​​based on the starting and ending location information to obtain the robot's running trajectory corresponding to each ID.

3. The robot trajectory monitoring method according to claim 1, characterized in that, The running trajectory segments are matched with corresponding preset trajectory segments to obtain matching results, including: Calculate the lateral offset distance between the running trajectory segment and the corresponding preset trajectory segment; If the lateral offset distance between the running trajectory segment and the corresponding preset trajectory segment exceeds the preset distance threshold, it is determined that the running trajectory segment has deviated from the corresponding preset trajectory segment.

4. The robot trajectory monitoring method according to claim 1, characterized in that, Based on the matching results of all running trajectory segments and corresponding preset trajectory segments, it is determined whether the robot has deviated, including: If the average lateral offset distance of N consecutive trajectory segments exceeds a preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

5. A robot trajectory monitoring device, characterized in that, The robot-end 5G signal measurement gap (GAP) is configured with 4G signal measurement, and the device includes: The location acquisition module is configured to acquire the 4G location information reported by the robot. The trajectory fitting module is configured to perform trajectory fitting based on the 4G location information to obtain the robot's running trajectory; The trajectory matching module is configured to match the robot's running trajectory with a preset trajectory through trajectory segmentation to determine whether the robot has deviated from its course. The deviation warning module is configured to output deviation warning information if the robot deviates from its intended path. This includes obtaining the 4G location information reported by the robot, including: Obtain the 4G network information reported by the robot, and extract the robot's ID, location, and timestamp from the 4G network information; The process of matching the robot's trajectory with a preset trajectory through trajectory segmentation to determine whether the robot has deviated includes: The robot's running trajectory and the preset trajectory are divided according to a preset length to obtain multiple running trajectory segments and preset trajectory segments; The running trajectory segments are matched with the corresponding preset trajectory segments to obtain the matching results; Based on the matching results of all running trajectory segments and corresponding preset trajectory segments, it is determined whether the robot has deviated. Among them, determining whether the robot has deviated based on the matching results of all running trajectory segments and corresponding preset trajectory segments includes: If the lateral offset distance of N consecutive running trajectory segments exceeds the preset distance threshold, it is determined that the robot has deviated, where N is a positive integer greater than 2.

6. A robot trajectory monitoring device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the robot trajectory monitoring method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the robot trajectory monitoring method as described in any one of claims 1-4.

Images