Autonomous Underwater Vehicle (AUV) motion control method and system based on LLM human-computer interaction framework

Abstract

The invention belongs to the technical field of motion control of robots, and relates to an LLM human-computer interaction framework-based AUV motion control method and system, and the method comprises the steps: obtaining a target AUV, and constructing a target AUV simulation platform based on ROS2; the LLM and the NMPC are integrated in the ROS2 to form an LLM man-machine interaction framework; acquiring a user instruction, converting the user instruction into a control instruction by the LLM man-machine interaction framework, and transmitting the control instruction to the target AUV simulation platform; the AUV in the AUV simulation platform executes a control instruction, generates trajectory tracking and displays the control instruction, a large language model is applied to the field of underwater robots, a man-machine interaction framework is combined with a traditional NMPC control method, control over a bottom-layer NMPC is achieved by means of high-level decision from LLM, and through an interactive display interface of Rviz2, the AUV simulation platform can be applied to the field of underwater robots. And the control flexibility of the AUV in a complex experiment scene is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot motion control, and relates to an AUV motion control method and system based on an LLM human-machine interaction framework. Background Art

[0002] In recent years, various types of robots have been continuously developed and widely used in land, sea, and air fields. Autonomous underwater robots (AUVs) have attracted widespread attention because they can help humans perform dangerous and challenging tasks such as seabed mapping, pipeline inspection, and pollution source location. In these tasks, motion control is crucial to improving the performance of AUVs. However, due to the complexity of marine experiments, how to enhance the interaction between AUVs and users to achieve flexible motion control is gradually becoming an important challenge.

[0003] Recently, LLMs (Large Language Models) have shown significant potential in understanding and processing natural language, especially in the field of interactive robot control. Traditional robot control methods often rely on predefined rules and mathematical models. Although this method is reliable in specific tasks, it lacks flexibility when facing a dynamic environment with ever-changing changes, and is slow to respond to new tasks and environments, making it difficult to adjust quickly. In addition, the complex programming logic, parameter adjustment, and debugging work in traditional control systems are not only time-consuming and labor-intensive, but also greatly increase the cost of development and maintenance. These problems are particularly prominent in scenarios with high technical requirements and complex environments such as marine experiments of autonomous underwater vehicles (AUVs). With the introduction and development of LLMs, this field has ushered in a major breakthrough. Through natural language interaction, operators can issue task instructions to robots in an intuitive and flexible manner, avoiding lengthy and complex programming processes. This advancement not only lowers the threshold for system use, but also makes robot control more adaptable and scalable. Especially in scenarios where humans and robots work together, LLMs can significantly improve the user experience because it allows users to describe tasks in a language they are familiar with without having to rely on technically proficient programming knowledge.

[0004] However, although LLMs have demonstrated excellent performance in high-level task processing, such as natural language understanding and mission planning, their performance in low-level control tasks faces significant challenges. Low-level control, such as precise attitude adjustment, path planning, and real-time trajectory tracking, requires robots to have fast response and high-precision operation capabilities. Such tasks usually involve continuous sensor feedback and real-time calculations, and LLMs, due to their computational complexity and resource requirements, have difficulty in providing sufficient processing speed and control accuracy under such time constraints. Therefore, although LLMs have shown revolutionary potential in high-level task decision-making and human-computer interaction, they are still limited in practical low-level control tasks of robots. In these tasks, traditional control methods, such as PID control and model predictive control (MPC), still dominate under real-time and accuracy requirements with their lightweight and efficient computing architecture. For complex sea trial scenarios, traditional control methods have technical problems of poor flexibility for AUV control. Summary of the invention

[0005] The purpose of the present invention is to provide an AUV motion control method and system based on the LLM human-computer interaction framework to solve the problem of poor control flexibility of AUV by traditional control methods in complex sea trial scenarios in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention discloses an AUV motion control method based on an LLM human-computer interaction framework, comprising: Acquire the target AUV and build a target AUV simulation platform based on ROS2; integrate LLM and NMPC in ROS2 to form the LLM human-computer interaction framework; Obtain user instructions, the LLM human-computer interaction framework converts user instructions into control instructions, and transmits the control instructions to the target AUV simulation platform; The AUV in the target AUV simulation platform executes control instructions, generates trajectory tracking and displays it.

[0007] Preferably, the building of the target AUV simulation platform based on ROS2 specifically comprises: setting a URDF robot description file in ROS2 and building a simulation scene, such as an ocean, an island, and the like; and obtaining the target AUV simulation platform.

[0008] Preferably, the LLM human-computer interaction framework includes: LLM high-level decision module is used to understand and analyze user instructions, generate high-level decisions, and send high-level decisions to the NMPC low-level control module; The NMPC bottom-level control module is used to receive high-level decisions, solve high-level decisions to obtain control instructions, and transmit the control instructions to the target AUV simulation platform.

[0009] Preferably, the high-level decision includes: generating a trajectory, modifying a desired position, modifying a desired speed, and modifying an NMPC weight matrix.

[0010] Preferably, the integration of LLM and NMPC in ROS2 to form an LLM human-computer interaction framework specifically includes: In the startup file of the LLM high-level decision module, add the publisher and subscriber nodes of ROS2 and define the data types related to user instructions; In the startup file of the NMPC underlying control module, add the publisher and subscriber nodes of ROS2; In Rviz2 of ROS2, add the AUV model, coordinate system, and sensor to the toolbar to obtain the LLM human-computer interaction framework.

[0011] Preferably, the data types related to the user instruction include: position, speed and posture information.

[0012] Preferably, the trajectory tracking is displayed in an LLM human-computer interaction framework.

[0013] In a second aspect, the present invention discloses an AUV motion control system based on an LLM human-computer interaction framework, comprising: The construction unit is used to acquire the target AUV and build the target AUV simulation platform based on ROS2; LLM is integrated into ROS2 to form the LLM human-computer interaction framework; The instruction generation unit is used to obtain user instructions. The LLM human-computer interaction framework converts the user instructions into control instructions and transmits the control instructions to the target AUV simulation platform; The instruction execution unit executes the control instructions of the AUV in the target AUV simulation platform, generates trajectory tracking and displays it.

[0014] In a third aspect, the present invention discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the steps of the AUV motion control method based on the LLM human-computer interaction framework described above when executing the computer program.

[0015] In a fourth aspect, the present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the AUV motion control method based on the LLM human-computer interaction framework described in any one of the above are implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention applies the large language model to the field of underwater robots, combines the human-computer interaction framework with the traditional NMPC control method, uses high-level decisions from LLM to realize the control of the underlying NMPC, and effectively improves the control flexibility of AUV in complex experimental scenarios through the interactive display interface of Rviz2. The present invention establishes a six-degree-of-freedom model of the underwater robot in a highly realistic Gazebo physical environment, adopts nonlinear model predictive control as the underlying motion controller, fully considers the nonlinear coupling terms of the underwater robot, and adopts a new Acados solver to effectively improve the control accuracy and efficiency of the AUV. The present invention is completed in the robot operating system ROS2, which has been significantly improved compared to ROS1 in terms of distributed, multi-platform and real-time support. By writing the URDF robot description file in ROS2, different robot models can be easily added, and different scenes can be rendered through plug-ins to meet the robot control, planning and recognition tasks in different fields, and have broad application prospects. The present invention combines LLMs with traditional control methods, using the language understanding ability of LLMs to process high-level task decisions, while leaving low-level control tasks to traditional methods or hybrid architectures to ensure the accuracy and response speed of the robot, ultimately achieving an efficient and flexible control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a block diagram of a human-computer interaction framework in an embodiment of the present invention; Figure 3 is a case diagram of LLM prompt in an embodiment of the present invention; Figure 4 is a block diagram of ROS2 message transmission in an embodiment of the present invention; Figure 5 This is a diagram of AUV experimental results in an embodiment of the present invention; Figure 6 It is a schematic diagram of an LLM-based human-computer interaction framework for AUV motion control provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0022] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0023] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0024] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] The present invention is further described in detail below in conjunction with the accompanying drawings: The present invention proposes a human-computer interaction framework based on LLM. In complex sea trial scenarios, natural language instructions provide researchers with a familiar and flexible way to describe tasks, eliminating the necessity of complex and repeated programming, thereby reducing the frequency of AUV recovery and debugging, and greatly reducing the unnecessary workload of sea trials. The present invention solves the problem of poor flexibility and interactivity of the above-mentioned traditional control methods by combining large language models with traditional optimal control methods. The specific method is as follows: See also Figure 1 , an AUV motion control method based on the LLM human-computer interaction framework, including: S1: Acquire the target AUV and build the target AUV simulation platform based on ROS2; integrate LLM and NMPC in ROS2 to form the LLM human-computer interaction framework; S2: Obtain user instructions. The LLM human-computer interaction framework converts the user instructions into control instructions and transmits the control instructions to the target AUV simulation platform. S3: The AUV in the target AUV simulation platform executes the control instructions, generates trajectory tracking and displays it.

[0026] In some embodiments, the construction of the target AUV simulation platform based on ROS2 is specifically as follows: setting a URDF robot description file in ROS2 and constructing a simulation scene, such as an ocean, an island, and the like; and obtaining the target AUV simulation platform.

[0027] In some embodiments, the LLM human-computer interaction framework includes: LLM high-level decision module is used to understand and analyze user instructions, generate high-level decisions, and send high-level decisions to the NMPC low-level control module; The NMPC bottom-level control module is used to receive high-level decisions, solve high-level decisions to obtain control instructions, and transmit the control instructions to the target AUV simulation platform.

[0028] In some embodiments, the high-level decision making includes generating a trajectory, modifying a desired position, modifying a desired velocity, and modifying an NMPC weight matrix.

[0029] In some embodiments, the integration of LLM and NMPC in ROS2 to form an LLM human-computer interaction framework specifically includes: In the startup file of the LLM high-level decision module, add the publisher and subscriber nodes of ROS2 and define the data types related to user instructions; In the startup file of the NMPC underlying control module, add the publisher and subscriber nodes of ROS2; In the Rviz2 interface of ROS2, add the AUV model, coordinate system, and sensor to the toolbar to obtain the LLM human-computer interaction framework.

[0030] In some embodiments, the data types related to the user instructions include: position, speed and posture information.

[0031] In some embodiments, the trajectory tracking is displayed in an LLM human-computer interaction framework.

[0032] In some embodiments, an AUV motion control method based on the LLM human-computer interaction framework may specifically include the following steps: Step 1: Design the target AUV simulation platform and write the URDF robot model file of the AUV used; integrate LLM and NMPC in ROS2 to form the LLM human-computer interaction framework; Step 2: Get user instructions, write high-level decisions based on LLM according to user instructions, and complete them in combination with ROS2 mechanism; Step 3: Write the driver file based on the underlying NMPC controller according to the high-level decision, and transfer it to the target AUV simulation platform in combination with ROS2; Step 4: The AUV in the AUV simulation platform executes control instructions, generates trajectory tracking and displays it, and realizes motion control of the AUV.

[0033] See also Figure 2 In an embodiment of the present invention, a human-computer interaction framework based on LLM is used for AUV motion control, which specifically includes: Step 1: Design the target AUV simulation platform and write the URDF robot model file of the AUV used; integrate LLM and NMPC in ROS2 to form the LLM human-computer interaction framework; use ENIRE Bot developed by Baidu as the LLM option to understand and analyze user control instructions, and realize the connection with the AUV simulation platform by combining the ROS2 mechanism; Step 2, the LLM high-level decision module designs navigation and optimization functions according to the predefined functions of the prompt engineering design, including generating trajectories, modifying the desired position and speed, and modifying the NMPC weight matrix; Step 3: The NMPC underlying control module calculates the control input of the thruster through iterative optimization and sends it to the AUV in the Gazebo simulation platform (AUV simulation platform) to complete the trajectory tracking task.

[0034] See also Figure 3 This figure describes an embodiment of analyzing user speed instructions based on the human-computer interaction framework of LLM. For this embodiment, the following steps may be specifically included: Step 1: Design a prompt function in the ROS2 system to modify the current expected speed of the AUV; Step 2: Use JSON format to design the information input to the AUV simulation platform; Step 3: Design the subscriber of the ROS2 node to subscribe to the speed status of the AUV and lay the foundation for the callback function; Step 4: Design a callback function in the ROS2 system to realize automatic detection of AUV status information in the simulation system and set the expected speed according to user instructions.

[0035] See also Figure 4 In the example of the present invention, the LLM high-level decision module and the NMPC low-level control module are completed through the node message mechanism of ROS2, which specifically includes the following steps: Step 1: In the LLM startup file, add the publisher and subscriber nodes of ROS2 to detect the AUV status and publish the expected command message; Step 2: Define the data types related to user commands, including position, speed, and posture information, for analysis of command messages; Step 3: In the NMPC startup file, define the publisher and subscriber nodes of ROS2, and realize communication with the AUV in the Gazebo environment through bridging; Step 4: Start the Rviz2 tool, add the AUV model, coordinate system, and sensor to the toolbar, and display the current AUV status information and trajectory tracking effect in the visual interface; Step 5: In the launched Gazebo and Rviz2 interfaces, the motion status of the AUV can be observed. Human intervention can be performed in emergency situations to simulate the prevention of abnormal AUV accidents in reality.

[0036]

Test example

[0037] The specific implementation steps include: setting the initial position of the AUV, setting the initial position x and y coordinates to 0, and the initial z coordinate is about -0.1 due to the buoyancy of the physical properties of seawater in the Gazebo simulation environment. Secondly, set the initial prediction step and control step of the NMPC controller to 10, set the control input weight matrix of the AUV thruster to diag (0.1, 0.1, 0.1, 0.1, 0.1, 0.1), and set the state weight matrix of the AUV model state equation to diag (10, 10, 10, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1. From this matrix, it can be seen that this method focuses on the position tracking accuracy of the AUV, and the sampling time is set to 0.1s, which can meet the transmission requirements of the actual AUV sensor.

[0038] First, use the designed Launch file to start the AUV simulation platform, including the Gazebo sim and Rviz2 interface, and set the display coordinate system, sensor, etc. in the interface. Secondly, start the control main program file of LLM and NMPC, and expand it through the following two embodiments: (1) Navigation command test In order to test the accuracy of the human-computer interaction framework proposed in the present invention in analyzing the user's navigation instructions, this embodiment inputs the text "Set the speeds of the x-axis and z-axis to 0.7 m / s and -0.2 m / s respectively, and the y coordinate follows a sine wave with a period of 20 seconds and an amplitude of 5 meters" in the control interface of the AUV. After analyzing the text using Baidu's ERINE Bot 4.0 model large language model, the prompt engineering set by the present invention is used to correctly understand the user's instructions; generate reasonable ROS2 code and publish it to the terminal of the AUV, and observe whether the published message is reasonable through the terminal; after receiving the ROS2 instruction, the AUV in the Gazebo environment passes it to the specific ROS2 interface of the present invention, so that the AUV moves according to the desired trajectory under the control of the NMPC, such as Figure 5 shown.

[0039] (2) Bottom-level optimization control instruction test In addition to the test of navigation instructions, users can input the underlying NMPC control related instructions through the interactive interface, and then change the weight matrix and other parameters of the controller. In this embodiment, input "increase weight, set the control input weight matrix to diag (0.2, 0.2, 0.2, 0.2, 0.2, 0.2)", and LLM analyzes this instruction and publishes it to the specially designed receiving function in the NMPC controller in the form of ROS2 message, so as to modify the weight matrix of NMPC. In order to ensure that the user's instructions do not affect the normal operation of AUV, the present invention sets the waiting time to 5s, which means that within 5s, if the system does not receive the correct instructions from the user, the system AUV will operate according to the default parameters.

[0040] In addition, the framework of the present invention can also modify other parameters in NMPC, such as the prediction step size, which is used to input the command "increase the prediction step size to 15", thereby setting the prediction step size Np to 15. The interactive interface can return the message published by ROS2, so that the user can confirm whether LLM correctly understands the user message to complete the modification of the prediction step size. After the modification is completed, the default trajectory tracking task is automatically started, and by comparing the trajectory tracking effects before and after the experiment, it can be seen again that the framework of the invention is effective.

[0041] In summary, the embodiment of the present invention discloses a human-computer interaction framework based on LLM for AUV motion control, which effectively improves the flexibility of AUV sea trial program debugging through LLM; the NMPC underlying control ensures the motion control accuracy of the AUV by establishing a nonlinear six-degree-of-freedom motion model; the visualized interaction method improves the flexibility and interactivity of the user's control of the AUV; this method introduces the large prediction model into the field of underwater robots, which can effectively improve the convenience of researchers in deploying underwater robot tasks, and each component unit is simple to implement in ROS2, which is suitable for robot experimental testing in other fields and has strong applicability.

[0042] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0043] See also Figure 6 The present application also discloses an AUV motion control system based on the LLM human-computer interaction framework, including: The construction unit is used to acquire the target AUV and build the target AUV simulation platform based on the ROS2 system; LLM is integrated into ROS2 to form the LLM human-computer interaction framework; The instruction generation unit is used to obtain user instructions. The LLM human-computer interaction framework converts the user instructions into control instructions and transmits the control instructions to the target AUV simulation platform; The instruction execution unit, the AUV in the AUV simulation platform executes control instructions, generates trajectory tracking and displays it.

[0044] The present application also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the AUV motion control method based on the LLM human-computer interaction framework described above are implemented.

[0045] The present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the AUV motion control method based on the LLM human-computer interaction framework described above are implemented.

[0046] In some embodiments, a computer-readable storage medium includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the motion control operation of AUV based on the human-computer interaction framework of LLM.

[0047] Large Language Model: In the field of artificial intelligence and natural language processing, LLM usually refers to large neural network models that can understand and generate human language. These models are trained with large amounts of text data and can perform various tasks such as text generation, question answering, translation, etc. For example, OpenAI's GPT series models (such as GPT-3, GPT-4) and Google's BERT model are both famous LLMs.

[0048] AUV refers to "Autonomous Underwater Vehicle". It is an unmanned submersible that can autonomously navigate and perform tasks underwater without human operation. AUV can perform a variety of tasks such as deep-sea exploration, topographic mapping, underwater archaeology, and environmental monitoring. It is an important tool in modern marine scientific research and technological applications.

[0049] ROS2, the full name of Robot Operating System 2, is an open source operating system that provides robots with hardware abstraction, device drivers, function libraries, visualization tools, message communication, and software package management.

[0050] NMPC, the full name of Nonlinear Model Predictive Control, is an advanced control strategy that is particularly suitable for dealing with nonlinear systems with complex dynamic characteristics and constraints. NMPC achieves effective control of the system by predicting the future behavior of the system and optimizing the current control input based on these predictions.

[0051] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0052] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0053] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for AUV motion control based on LLM human-computer interaction framework, characterized in that: include: Get the target AUV and build the target AUV simulation platform based on ROS2; Integrate LLM and NMPC in ROS2 to form the LLM human-computer interaction framework; Obtain user instructions, the LLM human-computer interaction framework converts user instructions into control instructions, and transmits the control instructions to the target AUV simulation platform; The AUV in the target AUV simulation platform executes control instructions, generates trajectory tracking and displays it.

2. The AUV motion control method based on the LLM human-computer interaction framework according to claim 1 is characterized in that: The target AUV simulation platform is constructed based on the ROS2 system, specifically: a URDF robot description file is set in ROS2 and a simulation scene is constructed to obtain the target AUV simulation platform.

3. The AUV motion control method based on the LLM human-computer interaction framework according to claim 1 is characterized in that: The LLM human-computer interaction framework includes: LLM high-level decision module is used to understand and analyze user instructions, generate high-level decisions, and send high-level decisions to the NMPC low-level control module; The NMPC bottom-level control module is used to receive high-level decisions, solve high-level decisions to obtain control instructions, and transmit the control instructions to the target AUV simulation platform.

4. The AUV motion control method based on the LLM human-computer interaction framework according to claim 3 is characterized in that: The high-level decisions include: generating a trajectory, modifying a desired position, modifying a desired velocity, and modifying an NMPC weight matrix.

5. The AUV motion control method based on the LLM human-computer interaction framework according to claim 3 is characterized in that: The integration of LLM and NMPC in ROS2 to form an LLM human-computer interaction framework specifically includes: In the startup file of the LLM high-level decision module, add the publisher and subscriber nodes of ROS2 and define the data types related to user instructions; In the startup file of the NMPC underlying control module, add the publisher and subscriber nodes of ROS2; In the Rviz2 interface of ROS2, add the AUV model, coordinate system, and sensor to the toolbar to obtain the LLM human-computer interaction framework.

6. The AUV motion control method based on the LLM human-computer interaction framework according to claim 5 is characterized in that: The data types related to the user instructions include: position, speed and posture information.

7. The AUV motion control method based on the LLM human-computer interaction framework according to claim 1 is characterized in that: The trajectory tracking is displayed in the LLM human-computer interaction framework.

8. An AUV motion control system based on the LLM human-computer interaction framework, characterized in that: include: A construction unit is used to acquire the target AUV and build a target AUV simulation platform based on ROS2; Integrate LLM into ROS2 to form the LLM human-computer interaction framework; The instruction generation unit is used to obtain user instructions. The LLM human-computer interaction framework converts the user instructions into control instructions and transmits the control instructions to the target AUV simulation platform; The instruction execution unit executes the control instructions of the AUV in the target AUV simulation platform, generates trajectory tracking and displays it.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the AUV motion control method based on the LLM human-computer interaction framework as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the AUV motion control method based on the LLM human-computer interaction framework according to any one of claims 1 to 7.

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