ROS robot intelligent teaching auxiliary system, method and device based on large model, medium and product

Abstract

The invention discloses an ROS robot intelligent teaching assistance system, method, device, medium and product based on a large model, and relates to the field of teaching assistance, the system comprises a user input module used for selecting an input mode according to a user usage scene, and inputting text information according to the input mode; the large model module is used for generating text information required by a user according to the text information and large model prompt information on the basis of a large model; the keyword matching module is used for carrying out keyword matching according to the text information required by the user and determining keyword matching information; the ROS robot module is used for controlling an ROS robot to execute a corresponding ROS program according to the keyword matching information, dynamically modifying parameter information of the ROS program and assisting in completing teaching tasks, and teachers can be comprehensively assisted in guiding students to quickly understand teaching content.

Description

Technical Field

[0001] The present application relates to the field of teaching assistance, and in particular to a large-model-based ROS robot intelligent teaching assistance system, method, equipment, medium and product. Background Art

[0002] In recent years, with the rise of big models, big model technology with natural language generation as its core can directly generate knowledge questions and answers, design models, code snippets and other results through the needs described in natural language to meet the needs of various industries.

[0003] In the field of teaching, the current teaching method is essentially to carry out in-class teaching and extracurricular practice with teachers teaching and students listening to lectures. The teaching effectiveness is limited by many factors such as the teaching input, professional level, course design, and software resources of the teachers. Before the theoretical teaching stage, the design of teaching cases takes up a lot of teachers' time; during theoretical teaching, the content of teachers' lectures is limited to the content designed in advance, including PPT, exercises, etc.; at the same time, the static display of teaching content is difficult to vividly explain the theory and laws of robot engineering algorithms. It can be seen that the current teaching method has low efficiency in teacher preparation and poor interest and practicality of classroom teaching, and cannot fully assist teachers in guiding students to quickly understand the teaching content. Summary of the invention

[0004] The purpose of this application is to provide a ROS robot intelligent teaching assistance system, method, equipment, medium and product based on a large model to solve the problem of being unable to fully assist teachers in guiding students to quickly understand the teaching content.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a ROS robot intelligent teaching assistance system based on a large model, comprising:

[0007] A user input module, used to select an input mode according to a user usage scenario and input text information according to the input mode; the input mode includes a voice recognition text input mode and a keyboard input mode;

[0008] A large model module, for generating text information required by the user based on the large model, according to the text information and large model prompt information; the large model prompt information includes the theme, target, background information, role setting, input and output prompts, and restrictions and taboos;

[0009] A keyword matching module, used to perform keyword matching according to the text information required by the user and determine keyword matching information;

[0010] The ROS robot module is used to control the ROS robot to execute the corresponding ROS program according to the keyword matching information, and dynamically modify the parameter information of the ROS program to assist in completing the teaching task.

[0011] In the second aspect, the present application provides a ROS robot intelligent teaching auxiliary method based on a large model, comprising:

[0012] Select an input mode according to the user's usage scenario, and input text information according to the input mode; the input mode includes a voice recognition text input mode and a keyboard input mode;

[0013] Based on the big model, the text information required by the user is generated according to the text information and the big model prompt information; the big model prompt information includes the theme, goal, background information, role setting, input and output prompts, and restrictions and taboos;

[0014] Perform keyword matching according to the text information required by the user to determine keyword matching information;

[0015] The ROS robot is controlled to execute the corresponding ROS program according to the keyword matching information, and the parameter information of the ROS program is dynamically modified to assist in completing the teaching task.

[0016] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned large-model-based ROS robot intelligent teaching assistance method.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned large-model-based ROS robot intelligent teaching assistance method.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned large-model-based ROS robot intelligent teaching assistance method.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0020] The present application generates the text information required by the user according to the input text information and the large model prompt information through the user input module, the large model module, the keyword matching module and the robot operating system (ROS) robot module, determines the keyword matching information, controls the ROS robot to execute the corresponding ROS program, and dynamically modifies the parameter information of the ROS program. Since the teacher knows that the current ROS program is developed based on the proportional-integral-differential controller (PID) algorithm principle, the system dynamically modifies the values ​​of the parameter information P, I, and D of the ROS program to dynamically teach students how these three parameters affect the control effect of the robot. The teacher does not need to spend time inputting execution commands through the keyboard or manually searching and modifying parameters in the file system of the ROS robot, which greatly saves the teacher's time. It can be seen that the system shown in the present application can enrich the teacher's teaching content, guide students to understand the physical meaning of the parameters and the engineering effects brought about by modifying the parameters through on-site teaching, improve the fun and practicality of classroom teaching, and assist teachers in guiding students to quickly understand the teaching content in all aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a ROS robot intelligent teaching assistance system based on a large model provided in one embodiment of the present application;

[0023] Figure 2 A schematic diagram of an application scenario of a ROS robot intelligent teaching assistance system based on a large model provided in one embodiment of the present application;

[0024] Figure 3 A flow chart of a ROS robot intelligent teaching assistance method based on a large model provided in one embodiment of the present application;

[0025] Figure 4 A schematic diagram of a computer device provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] like Figure 1 As shown, the embodiment of the present application provides a ROS robot intelligent teaching auxiliary system based on a large model, including:

[0029] The user input module is used to select an input mode according to the user usage scenario and input text information according to the input mode; the input mode includes a voice recognition text input mode and a keyboard input mode.

[0030] The big model module is used to generate the text information required by the user based on the big model, according to the text information and the big model prompt information; the big model prompt information includes the theme, goal, background information, role setting, input and output prompts, and restrictions and taboos. The big model can be ChatGPT.

[0031] The keyword matching module is used to perform keyword matching according to the text information required by the user and determine keyword matching information.

[0032] The ROS robot module is used to control the ROS robot to execute the corresponding ROS program according to the keyword matching information, and dynamically modify the parameter information of the ROS program to assist in completing the teaching task.

[0033] In an exemplary embodiment, the user input module specifically includes:

[0034] The keyboard input unit is used to input text information using a keyboard input mode.

[0035] The speech recognition unit is used to recognize speech information and convert the speech information into text information by adopting the speech recognition text input mode.

[0036] The text information modification unit is used to modify the text information converted by the speech recognition unit by using a keyboard input mode.

[0037] Furthermore, when the system is used for teacher teaching case design, keyboard input fits the current scenario; when the system is used for teacher teaching, the teacher may be at any position in the classroom, so the teacher can obtain the teacher's voice through the microphone, and then convert the teacher's voice information into text information through the voice recognition unit. Since the classroom environment may be noisy, this method also supports keyboard modification of voice recognition information results.

[0038] In an exemplary embodiment, the large model module specifically includes:

[0039] A matching unit is used to dynamically match the large model prompt information according to the text information.

[0040] The user required text information generating unit is used to input the large model prompt information into the large model to generate the user required text information.

[0041] Furthermore, the big model has randomness in the generated question and answer results. In order to make the big model output fit the current usage scenario, it is first necessary to construct big model prompt information for different scenarios. For example, in the lesson plan design stage, you can tell the big model that the lesson plan prompt information is: "You are a teacher and now you need to write a teaching case. A complete teaching case includes: teaching objectives, teaching key points and difficulties, and basic teaching content. The basic teaching content includes theoretical knowledge points, homework exercises, and teaching case analysis. The theoretical knowledge points must be explained thoroughly, and the core concepts must be described; homework exercises should give 3-5 homework exercises of different difficulty levels; the teaching case analysis design needs to be explained thoroughly, and several relevant code cases of different difficulty levels can be given, and the program principles involved are explained in detail to achieve the teaching objectives."

[0042] If during the teaching session, the large model is required to start the corresponding ROS program based on user input, the large model prompts: "You now have a ROS robot (i.e., role positioning), which can drive in all directions and can use a robotic arm to grab objects (i.e., background information).

[0043] There are several programs inside the existing robot: When performing the corresponding task, you need to execute the corresponding program (i.e. subject and target) according to the ROS mechanism.

[0044] Specific input and output prompts include moving command tasks, visual gripping tasks, and killing the current program tasks or stopping tasks:

[0045] For a moving command task, you need to issue the command to execute bashmove.sh; for a line patrol and obstacle removal task, you need to issue the command to execute bash lidar.sh.

[0046] For visual gripping tasks, you need to issue and execute the bash color_track.sh command.

[0047] To kill the current program task or stop the task, you need to issue and execute the bashkill.sh command.

[0048] When you are asked to perform a task, you only need to give instructions. For example, when you request a move task, you only need to issue bash move.sh, without any additional content (i.e. restrictions and taboos). "Depending on the current usage scenario, the corresponding prompt information is instilled into the large model during the question-and-answer session.

[0049] In an exemplary embodiment, the keyword matching module specifically includes:

[0050] The keyword matching unit is used to perform keyword matching on the text information required by the user by using a combination of regular expressions, deep learning algorithms and string search (Boyer-Moore, BM) algorithms to determine keyword matching information.

[0051] Furthermore, multiple algorithms are used redundantly to achieve Chinese and English keyword matching, including a combination of regular expression-based, deep learning method-based, and BM algorithm-based methods, which can realize the extraction of Chinese and English keywords, thereby realizing the automatic running of the corresponding ROS program according to the output results of the large model.

[0052] In an exemplary embodiment, the ROS robot module specifically includes:

[0053] The API interface information matching unit is used to match the API interface information of the ROS robot according to the keyword matching information.

[0054] The ROS program execution unit is used to control the ROS robot to execute the corresponding ROS program according to the API interface information.

[0055] The dynamic modification unit is used to dynamically modify the parameter information of the ROS program through the ROS startup script to assist in completing the teaching task.

[0056] In an exemplary embodiment, the large model-based ROS robot intelligent teaching auxiliary system and the ROS robot are in the same local area network and are connected via a secure shell protocol (SSH).

[0057] Furthermore, the ROS robot provides an internal API interface based on the internal program algorithm, through which different ROS programs can be started; the teaching system matches information based on keywords and obtains the corresponding API interface information, so that the system can start or stop the currently executing ROS program; the ROS startup script can also be used to dynamically modify the parameter information of the ROS program, enriching the teacher's teaching content and guiding students to understand the physical meaning of the parameters and the engineering effects they bring through on-site teaching.

[0058] Scenario 1: How to use the system to teach PID control in a classroom.

[0059] Prompt message: "You now have a ROS robot that can drive in all directions and use a robotic arm to grab objects. The existing robot has the following programs inside: When executing the corresponding tasks, you need to execute the corresponding programs according to the ROS mechanism. For the mobile command task, you need to issue the command to execute bash move.sh; for the line patrol and obstacle removal task, you need to issue the command to execute bash lidar.sh; for the visual gripping task, you need to issue the command to execute bash color_track.sh; to kill the current program task or stop the task, you need to issue the command to execute bash kill.sh; when requesting to execute a task, you only need to give the command, for example, when requesting a mobile task, you only need to issue bash move.sh, no additional content is required."

[0060] User requirement 1: Please perform the mobile task.

[0061] Large model output: bashmove.sh.

[0062] Keyword matching: According to the output result "bashmove.sh" of the large model, the probability of matching the program that starts the robot movement in the ROS robot is the highest and reaches the threshold, so the corresponding program command is automatically issued.

[0063] At this point, after the program starts normally, it will be fed back to the system to inform the ROS robot of the program it is running.

[0064] At this point, the teacher knows that the current ROS movement program is developed based on the PID algorithm principle, so he can dynamically teach students how these three parameters affect the control effect of the robot by modifying the values ​​of program parameters P, I, and D. Therefore, after the movement task is executed once, the corresponding parameters can be modified through the system.

[0065] User requirement 2: Modify the parameter list in the mobile task and change the parameter p to 0.5.

[0066] Large model output: sed -i 's / ^p=.* / p=0.5 / 'pid.txt.

[0067] Keyword matching: According to the big model results, if the system preset command is matched with the big model results with a high similarity and exceeds the threshold, the corresponding command is executed to modify the configuration file and feedback is given to the system to inform it that the modification is successful.

[0068] In this application, the user first obtains voice through a microphone and converts the voice into text information through voice recognition technology. The user can also input or change the text information after voice recognition through the keyboard, and then transmit the text information to the big model to obtain the question and answer results. The question and answer results are matched with keywords, and then the corresponding ROS program API is obtained through the matching results. Finally, the ROS robot is connected through the local area network, and the corresponding program is executed to control the robot, thereby realizing the comprehensive integrated teaching of theory + code + engineering deployment + robot running test, such as Figure 2 shown.

[0069] The above process saves teachers’ time by eliminating the need to input execution commands through the keyboard or manually search and modify parameters in the ROS robot’s file system. This greatly saves teachers’ time. This system can enrich teachers’ teaching content and guide students to understand the physical meaning of parameters and the engineering effects of parameter modification through on-site teaching.

[0070] This application improves the efficiency of teacher preparation by combining large models with traditional teaching; increases the fun and practicality of classroom teaching, and can quickly realize the comprehensive integrated teaching of theory + code + engineering deployment + robot running and testing, and comprehensively assist teachers to guide students to quickly understand the teaching content, thereby making up for the empty limitations of traditional teaching in engineering practice.

[0071] The embodiment of the present application provides a ROS robot intelligent teaching assistance method based on a large model, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, Figure 3 As shown, the method includes the following steps.

[0072] S1: Select an input mode according to the user's usage scenario, and input text information according to the input mode; the input mode includes a voice recognition text input mode and a keyboard input mode.

[0073] S2: Based on the big model, generate the text information required by the user according to the text information and the big model prompt information; the big model prompt information includes the theme, goal, background information, role setting, input and output prompts, and restrictions and taboos.

[0074] S3: Perform keyword matching according to the text information required by the user to determine keyword matching information.

[0075] S4: Control the ROS robot to execute the corresponding ROS program according to the keyword matching information, and dynamically modify the parameter information of the ROS program to assist in completing the teaching task.

[0076] In an exemplary embodiment, a computer device is provided. Figure 4 As shown, the computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store ROS robot intelligent teaching auxiliary data based on a large model. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a ROS robot intelligent teaching auxiliary method based on a large model is implemented.

[0077] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0078] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0079] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0080] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0081] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0082] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0083] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A ROS robot intelligent teaching auxiliary system based on a large model, characterized in that: include: A user input module, used to select an input mode according to a user usage scenario and input text information according to the input mode; the input mode includes a voice recognition text input mode and a keyboard input mode; A large model module, for generating text information required by the user based on the large model, according to the text information and large model prompt information; the large model prompt information includes the theme, target, background information, role setting, input and output prompts, and restrictions and taboos; A keyword matching module, used to perform keyword matching according to the text information required by the user and determine keyword matching information; The ROS robot module is used to control the ROS robot to execute the corresponding ROS program according to the keyword matching information, and dynamically modify the parameter information of the ROS program to assist in completing the teaching task.

2. The ROS robot intelligent teaching auxiliary system based on a large model according to claim 1 is characterized in that: The user input module specifically includes: A keyboard input unit, used to input text information using a keyboard input mode; A speech recognition unit, used to recognize speech information and convert the speech information into text information using the speech recognition text input mode; The text information modification unit is used to modify the text information converted by the speech recognition unit by using a keyboard input mode.

3. The ROS robot intelligent teaching auxiliary system based on a large model according to claim 1 is characterized in that: The large model module specifically includes: A matching unit, used for dynamically matching large model prompt information according to the text information; The user required text information generating unit is used to input the large model prompt information into the large model to generate the user required text information.

4. The ROS robot intelligent teaching auxiliary system based on a large model according to claim 1 is characterized in that: The keyword matching module specifically includes: The keyword matching unit is used to perform keyword matching on the text information required by the user by using a combination of regular expressions, deep learning algorithms and BM algorithms to determine keyword matching information.

5. The ROS robot intelligent teaching auxiliary system based on a large model according to claim 1 is characterized in that: The ROS robot module specifically includes: An API interface information matching unit, used to match the API interface information of the ROS robot according to the keyword matching information; A ROS program execution unit, used to control the ROS robot to execute a corresponding ROS program according to the API interface information; The dynamic modification unit is used to dynamically modify the parameter information of the ROS program through the ROS startup script to assist in completing the teaching task.

6. The ROS robot intelligent teaching auxiliary system based on a large model according to claim 1 is characterized in that: The large-model-based ROS robot intelligent teaching auxiliary system and the ROS robot are in the same local area network and are connected via SSH.

7. A ROS robot intelligent teaching auxiliary method based on a large model, characterized in that: The ROS robot intelligent teaching auxiliary method based on the large model includes: Select an input mode according to the user's usage scenario, and input text information according to the input mode; the input mode includes a voice recognition text input mode and a keyboard input mode; Based on the big model, the text information required by the user is generated according to the text information and the big model prompt information; the big model prompt information includes the theme, goal, background information, role setting, input and output prompts, and restrictions and taboos; Perform keyword matching according to the text information required by the user to determine keyword matching information; The ROS robot is controlled to execute the corresponding ROS program according to the keyword matching information, and the parameter information of the ROS program is dynamically modified to assist in completing the teaching task.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the large model-based ROS robot intelligent teaching assistance method according to claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the ROS robot intelligent teaching auxiliary method based on a large model as described in claim 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the ROS robot intelligent teaching auxiliary method based on a large model as described in claim 7 is implemented.

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