Water surface robot high-precision trajectory tracking control method and related equipment

Abstract

The invention belongs to the technical field of control, and discloses a water surface robot high-precision trajectory tracking control method and related equipment, and the method comprises the steps: constructing a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm embedded into a water flow kinetic equation constraint is adopted to carry out global path planning, smooth parameterization processing is carried out on a global path obtained through planning, and a reference trajectory is generated; according to the vector information of the water flow field, flow field disturbance is predicted based on a fluid-structure interaction dynamics model, system residual disturbance is estimated by using an extended state observer, and feedforward control quantity is generated in combination; according to the real-time flow velocity of the water flow field and the trajectory tracking error, a nonlinear model prediction controller and an adaptive sliding mode controller are adaptively switched, and a control instruction is generated in combination with a feedforward control quantity to drive the water surface robot to track a reference trajectory; therefore, high-precision trajectory tracking control is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control, in particular to a high-precision trajectory tracking control method for water surface robots and related equipment. BACKGROUND

[0002] Water surface robots have wide application prospects in the fields of environmental monitoring, water quality sampling, underwater exploration, etc. However, in actual application, water surface robots often face complex water surface environments, such as water surface reflection, dynamic floating obstacles, and complex and variable flow fields, etc. These factors seriously affect the autonomous operation ability and trajectory tracking accuracy of water surface robots.

[0003] The existing autonomous operation control strategy of water surface robots mainly relies on traditional image visual detection algorithms for environment perception. However, these traditional algorithms are easily disturbed by mirror reflection in the environment with strong water surface reflection, resulting in a decrease in the detection accuracy of water surface floating objects, and even missing detection or false detection, thereby reducing the environmental adaptability of water surface robots. In addition, the floating objects on the water surface are often dynamic, and traditional static obstacle detection methods are difficult to effectively cope with, further increasing the difficulty of path planning and obstacle avoidance of water surface robots.

[0004] In terms of path planning, traditional geometric-based path planning methods usually do not fully consider the coupling effect of fluid and robot dynamics. This means that when planning the path, the motion characteristics of the robot itself and the influence of the water flow on the robot are not effectively integrated, resulting in a higher energy consumption of the planned trajectory in the actual execution process, and it is difficult to accurately track. Especially in the environment with complex flow field, this planning method often cannot generate the optimal path with high energy efficiency.

[0005] In terms of trajectory tracking control, traditional PID controllers, MPC model predictive controllers, etc. often have a lag in response when facing complex hydrodynamic disturbances such as nonlinear surges and sudden flow fields, resulting in large trajectory tracking errors. These controllers have limitations in dealing with strong nonlinear, time-varying systems and external disturbances, and are difficult to meet the needs of water surface robots to achieve high-precision trajectory tracking in complex environments. Therefore, the existing technology urgently needs a control method that can achieve high-precision trajectory tracking in complex flow fields.

[0006] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0007] The present application aims to provide a high-precision trajectory tracking control method for water surface robots and related equipment, which aims to solve the problems that traditional environment perception algorithms are easily disturbed by mirror reflection, path planning does not fully consider the coupling effect of fluid dynamics, and trajectory tracking control has a lag in response to complex hydrodynamic disturbances, etc. in complex water surface environments.

[0008] In a first aspect, the application provides a high-precision trajectory tracking control method for a water surface robot, comprising the following steps: A1. Collecting environmental information by a multi-source heterogeneous sensor to construct a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; A2. According to the dynamic multi-modal environment map, using an improved quantum particle swarm optimization algorithm embedded with water flow dynamics equation constraints to perform global path planning, and performing smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory; A3. According to the water flow field vector information in the dynamic multi-modal environment map, predicting flow field disturbance based on a fluid-structure coupling dynamics model, and estimating system residual disturbance using an extended state observer, and generating a feedforward control amount in combination with the flow field disturbance and the system residual disturbance; A4. According to the real-time water flow field flow rate and trajectory tracking error, adaptively switching a nonlinear model predictive controller and an adaptive sliding mode controller, and generating a control instruction in combination with the feedforward control amount to drive the water surface robot to track the reference trajectory.

[0009] Preferably, the multi-source heterogeneous sensor comprises a binocular vision camera, a millimeter wave radar, and an acoustic Doppler current profiler array; In a second aspect, the application provides a high-precision trajectory tracking control system for a water surface robot, comprising: A multi-modal environment map generation module for collecting environmental information by a multi-source heterogeneous sensor to construct a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; A reference trajectory generation module for, according to the dynamic multi-modal environment map, using an improved quantum particle swarm optimization algorithm embedded with water flow dynamics equation constraints to perform global path planning, and performing smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory; A feedforward control amount generation module for, according to the water flow field vector information in the dynamic multi-modal environment map, predicting flow field disturbance based on a fluid-structure coupling dynamics model, and estimating system residual disturbance using an extended state observer, and generating a feedforward control amount in combination with the flow field disturbance and the system residual disturbance; A control driving module for, according to the real-time water flow field flow rate and trajectory tracking error, adaptively switching a nonlinear model predictive controller and an adaptive sliding mode controller, and generating a control instruction in combination with the feedforward control amount to drive the water surface robot to track the reference trajectory.

[0010] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, the steps of the water surface robot high-precision trajectory tracking control method described above are executed.

[0011] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the water surface robot high-precision trajectory tracking control method described above are executed.

[0012] Beneficial effects: The water surface robot high-precision trajectory tracking control method and related devices provided by the present application effectively solve the problem of inaccurate environmental perception caused by water surface reflection interference in the prior art by constructing a dynamic multi-modal environment map containing water surface three-dimensional geometric topological information and water flow field vector information through multi-source heterogeneous sensor data fusion. On this basis, the present application uses an improved quantum particle swarm optimization algorithm embedded with water flow dynamics equation constraints for global path planning, and performs smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory, thereby overcoming the problem that the traditional path planning method does not fully consider the fluid-robot dynamics coupling effect, resulting in high energy consumption and difficulty in accurate tracking. In addition, the present application predicts flow field disturbance based on a fluid-structure coupling dynamics model according to the water flow field vector information in the dynamic multi-modal environment map, estimates system residual disturbance using an extended state observer, and generates a feedforward control amount in combination with the flow field disturbance and the system residual disturbance, thereby effectively compensating for external disturbance. Furthermore, the present application adaptively switches a nonlinear model predictive controller and an adaptive sliding mode controller according to the real-time water flow field flow rate and trajectory tracking error, generates a control instruction in combination with the feedforward control amount to drive the water surface robot to track the reference trajectory, thereby solving the problem of response lag and large trajectory tracking error of the traditional controller when facing complex hydrodynamic disturbances such as nonlinear surge and sudden flow field. In summary, the present application significantly improves the environmental perception ability, energy efficiency and safety of path planning, and the accuracy and robustness of trajectory tracking of the water surface robot in complex water surface environment through the above technical solutions, realizes high-precision trajectory tracking control, and has significant technical progress and practical value. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 A flowchart of the water surface robot high-precision trajectory tracking control method provided by the present application.

[0014] Figure 2 A schematic diagram of the water surface robot high-precision trajectory tracking control system provided by the present application.

[0015] Figure 3 A structural schematic diagram of the electronic device provided by the present application.

[0016] Label description: 1, multi-modal environment map generation module; 2, reference trajectory generation module; 3, feedforward control quantity generation module; 4, control driving module; 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0018] It should be noted that: similar labels and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0019] Please refer to Figure 1 A water surface robot high-precision trajectory tracking control method in some embodiments of the present application, the method comprising the following steps: A1. Collecting environmental information through multi-source heterogeneous sensors to fuse and construct a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; A2. According to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm embedded with water flow dynamics equation constraint is used for global path planning, and the global path obtained by planning is subjected to smoothing parameterization processing to generate a reference trajectory; A3. According to the water flow field vector information in the dynamic multi-modal environment map, the flow field disturbance is predicted based on the fluid-structure coupling dynamics model, and the system residual disturbance is estimated by using the extended state observer, and the feedforward control quantity is generated by combining the flow field disturbance and the system residual disturbance; A4. According to the real-time water flow field velocity and trajectory tracking error, the nonlinear model predictive controller and the adaptive sliding mode controller are adaptively switched, the control instruction is generated to drive the water surface robot to track the reference trajectory in combination with the feedforward control quantity.

[0020] The application aims to provide a high-precision trajectory tracking control method for water surface robots, to solve the limitations of water surface robots in environmental perception, path planning and trajectory tracking control in complex water surface environments. This method constructs a dynamic multi-modal environment map through multi-source heterogeneous sensor fusion technology, providing comprehensive and accurate environmental information for subsequent path planning and trajectory tracking. In the path planning stage, an improved quantum particle swarm optimization algorithm constrained by hydrodynamic equations is introduced to ensure the energy efficiency and safety of the planned path. In the trajectory tracking control stage, the feedforward control amount is generated by combining flow field disturbance prediction and system residual disturbance estimation, and an adaptive switching controller is used to effectively cope with complex hydrodynamic disturbances, thereby achieving high-precision trajectory tracking of water surface robots.

[0021] The high-precision trajectory tracking control method for water surface robots proposed in this application is based on a series of coordinated steps to ensure the autonomous operation capability and trajectory tracking accuracy of water surface robots in complex water environments.

[0022] First, in step A1, environmental information is collected by multi-source heterogeneous sensors to construct a dynamic multi-modal environment map containing three-dimensional geometric topological information and water flow field vector information. The "multi-source heterogeneous sensors" can include but are not limited to binocular vision cameras, millimeter wave radars, acoustic Doppler current profiler arrays, etc. For example, binocular vision cameras can be used to collect water surface images, millimeter wave radars can obtain water surface radar point cloud data, and acoustic Doppler current profiler arrays can obtain water flow field vector information. The raw data collected by these sensors needs to be preprocessed and fused to generate a comprehensive environment map. For example, water surface images may need to be polarized to eliminate water surface specular reflection interference, and then use image processing algorithms (such as improved YOLOv7 algorithm) for water surface floating object detection to obtain obstacle detection information. At the same time, based on binocular vision images, binocular vision SLAM algorithm can be used to generate binocular SLAM point cloud data. Finally, the obstacle detection information, binocular SLAM point cloud data, radar point cloud data and water flow field vector information are fused to construct a dynamic multi-modal environment map containing three-dimensional geometric topological information (such as the position, shape and size of obstacles) and water flow field vector information (such as water flow velocity and direction). The environment map is dynamic, meaning it can be updated in real time to reflect changes in the water surface environment.

[0023] Secondly, in step A2, according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm with embedded water flow dynamics equation constraints is used for global path planning, and the global path obtained by planning is subjected to smoothing parameterization processing to generate a reference trajectory. When performing path planning, the traditional geometric path planning method often ignores the influence of water flow on the water surface robot. In order to solve this problem, the application introduces an improved quantum particle swarm optimization algorithm with embedded water flow dynamics equation constraints. For example, in the fitness function of the quantum particle swarm optimization algorithm, the discretized Navier-Stokes equation can be embedded as a physical constraint condition to optimize the energy consumption and safety of the path. The water flow field vector information can include the flow velocity and direction data of the gridded water flow field, and the Navier-Stokes equation discretization model of the local drainage basin is established by obtaining the vertical profile distribution data of the water flow, thereby providing the dynamic parameter information of the flow field. This method can make the planned path more consistent with the motion characteristics of the water surface robot in the actual water flow environment, reduce energy consumption and improve safety. The global path obtained by planning is usually discrete, and in order to enable the water surface robot to track smoothly, it needs to be subjected to smoothing parameterization processing. For example, the global path obtained by planning can be subjected to smoothing parameterization processing by using a B-spline curve to generate a continuous and smooth reference trajectory.

[0024] Thirdly, in step A3, according to the water flow field vector information in the dynamic multi-modal environment map, the flow field disturbance is predicted based on a fluid-structure coupling dynamics model, and the system residual disturbance is estimated by using an extended state observer, and the flow field disturbance and the system residual disturbance are combined to generate a feedforward control amount. The water flow field will produce significant disturbance to the motion of the water surface robot, in order to improve the trajectory tracking accuracy, it is necessary to predict and compensate these disturbances. The fluid-structure coupling dynamics model can be used to predict the torque and force generated by the water flow field to the water surface robot, thereby obtaining the flow field disturbance. At the same time, due to the existence of unmodeled dynamics, parameter uncertainty and external unknown disturbances in the system, the extended state observer can be used to estimate these system residual disturbances (the use of the extended state observer to estimate the system residual disturbance is prior art, which will not be described in detail here). The predicted flow field disturbance and the estimated system residual disturbance are superimposed to generate a feedforward control amount. This feedforward control amount can compensate in advance for the influence of water flow and system internal uncertainty on the motion of the water surface robot, thereby improving the response speed and tracking accuracy of the control system. The fluid-structure coupling dynamics model can be a discretized Navier-Stokes equation, which can be described by the following equation based on the immersed boundary method (IBM) for discretizing the relationship between the robot and the fluid: ; wherein, a density of the fluid (water), a dynamic viscosity of the fluid, a flow rate, a fluid pressure, an interaction force between the flow field and the surface robot, a system state (vector), represents the interaction force between the flow field and the surface robot under the conditions of t time and system state N is the number of boundary points of the boundary between the surface robot and the fluid, is the force of the kth boundary point, is the flow field meshing scale parameter of water, is the position vector of the kth boundary point at t time.

[0025] Finally, in step A4, according to the real-time water flow field flow rate and trajectory tracking error, the adaptive switching nonlinear model predictive controller and adaptive sliding mode controller are combined with the feedforward control amount to generate control instructions to drive the surface robot to track the reference trajectory. In order to cope with the complex and variable water surface environment, the present application adopts an adaptive switching control strategy. For example, the water flow field flow rate and the trajectory tracking error can be obtained in real time. When the water flow field flow rate is not greater than the preset flow rate threshold and the trajectory tracking error is not greater than the preset error threshold (for example, the preset error threshold is 0.3 m and the preset flow rate threshold is 2 m / s), the nonlinear model predictive controller is selected as the feedback controller. The nonlinear model predictive controller can generate a feedback control amount by optimizing the optimal control sequence in the control time domain window according to the current system state and the expected system state sequence of the reference trajectory in the prediction time domain window. Preferably, in the process of solving the optimal control sequence, based on the differential flatness theory, the nonlinear system corresponding to the dynamic model is mapped to the flat output space, and a linear prediction model is constructed in the flat output space to map the system state of the 6-dimensional state space (6-dimensional longitudinal and lateral positions, lateral position, heading angle, speed, angular velocity, acceleration) to the 2-dimensional flat output (2-dimensional longitudinal position, heading angle), thereby significantly reducing the calculation overhead and greatly improving the calculation efficiency. When the water flow field flow rate is greater than the preset flow rate threshold or the trajectory tracking error is greater than the preset error threshold, the adaptive sliding mode controller is selected as the feedback controller. The adaptive sliding mode controller can generate a feedback control amount through a sliding mode control model according to the tracking error and its first derivative, which has strong robustness to system parameter changes and external disturbances. The feedback control amount generated by the selected feedback controller is superimposed with the feedforward control amount generated in step A3 to generate the final control instruction. These control instructions are then sent to the actuator (e.g., vector thruster) of the surface robot to drive the surface robot to accurately track the reference trajectory.

[0026] The water surface robot high-precision trajectory tracking control method provided in the application, through multi-source heterogeneous sensor fusion technology, constructs a dynamic multi-modal environment map containing water surface three-dimensional geometric topological information and water flow field vector information. This innovation overcomes the limitations of traditional single sensor in complex water surface environment, especially solves the problem of water surface reflection interference on visual detection and dynamic floating object difficult to effectively identify. By fusing the data of binocular vision, millimeter wave radar and acoustic Doppler current profiler array, the application can provide more comprehensive and accurate environmental information, laying a solid foundation for subsequent path planning and trajectory tracking.

[0027] In terms of path planning, the application adopts an improved quantum particle swarm optimization algorithm embedded with flow dynamics equation constraints for global path planning, and performs smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory. Compared with traditional path planning methods which do not fully consider the coupling effect of fluid and robot dynamics, the application embeds the discretized Navier-Stokes equation as a physical constraint condition into the optimization algorithm, so that the planned path not only considers geometric feasibility, but also considers energy consumption and safety. This significantly improves the executability and efficiency of the path in the actual water flow environment, solving the problem of high energy consumption and difficulty in accurate tracking in complex water flow field of traditional methods.

[0028] In terms of trajectory tracking control, according to the water flow field vector information in the dynamic multi-modal environment map, the application predicts the flow field disturbance based on the fluid-structure coupling dynamics model, estimates the system residual disturbance using the extended state observer, and generates a feedforward control amount in combination with the flow field disturbance and the system residual disturbance. At the same time, according to the real-time water flow field flow rate and trajectory tracking error, the nonlinear model predictive controller and the adaptive sliding mode controller are adaptively switched, and the control command is generated in combination with the feedforward control amount to drive the water surface robot to track the reference trajectory. The innovation of this control strategy lies in that it combines feedforward control and adaptive switching feedback control. Feedforward control can compensate for predictable flow field disturbance and system residual disturbance in advance, significantly improving the response speed of the control system. The adaptive switching controller can dynamically select the most suitable controller according to the real-time environmental conditions (water flow field flow rate and trajectory tracking error), so that high-precision tracking performance can be maintained under different working conditions. Compared with traditional PID controllers or single MPC controllers, the control method of the application has stronger robustness and higher tracking accuracy when facing complex hydrodynamic disturbances such as nonlinear surges and sudden flow fields, effectively solving the problems of response lag and large tracking error of traditional controllers.

[0029] In summary, the present application realizes significant technical progress in the three key links of environmental perception, path planning and trajectory tracking control through multi-source heterogeneous sensor fusion, path planning embedded with water flow dynamics constraints and adaptive switching feedforward-feedback control strategy, and provides an effective solution for high-precision autonomous operation of water surface robots in complex water environment.

[0030] In some embodiments, the multi-source heterogeneous sensors include a binocular vision camera, a millimeter wave radar and an acoustic Doppler current profiler array; Step A1 includes: A101. Using a binocular vision camera to collect water surface images, using a millimeter wave radar to obtain radar point cloud data of the water surface, and using an acoustic Doppler current profiler array to obtain water flow field vector information; A102. Polarization filtering the water surface images to obtain binocular vision images, and using an improved YOLOv7 algorithm to detect water surface floating objects in the binocular vision images to obtain obstacle detection information; A103. Based on the binocular vision images, using a binocular vision SLAM algorithm to generate binocular SLAM point cloud data; A104. Fusing the obstacle detection information, the binocular SLAM point cloud data and the radar point cloud data to generate three-dimensional geometric topology information; A105. Fusing the three-dimensional geometric topology information and the water flow field vector information to obtain a dynamic multi-modal environment map.

[0031] Among them, the multi-source heterogeneous sensors specifically refer to the combination of multiple different types of sensors used to collect water surface environment information, and the purpose is to obtain comprehensive and complementary environmental data. The binocular vision camera can collect water surface images and provide rich visual information for identifying water surface objects and constructing a visual model of the environment. The millimeter wave radar can obtain radar point cloud data of the water surface, which is characterized by strong penetration and less affected by environmental factors such as light and weather, and can provide reliable distance and obstacle information. The acoustic Doppler current profiler array is specifically used to obtain water flow field vector information, which can accurately measure the speed and direction of water flow and provide basic data for water flow dynamics analysis.

[0032] Further, in step A102, the water surface image is polarized filtered to eliminate the interference of water surface specular reflection. Water surface often appears specular reflection under sunlight, which can seriously affect the quality of visual image and the subsequent image processing effect. Through polarization filtering technology, these reflections can be effectively suppressed, making the water surface floating object detection more clear and accurate. On this basis, the improved YOLOv7 algorithm is used to detect the water surface floating object in the binocular vision image, so as to obtain the obstacle detection information. The improved YOLOv7 algorithm is a high-efficiency target detection algorithm, through the optimization of which, various floating objects on the water surface can be more accurately and real-timely identified, and marked as obstacles.

[0033] In step A103, based on the binocular vision image, a binocular vision SLAM algorithm is used to generate binocular SLAM point cloud data. The binocular vision SLAM (Simultaneous Localization and Mapping) algorithm uses the parallax information provided by the binocular camera to realize real-time self-positioning and construct a three-dimensional map of the environment, and the generated binocular SLAM point cloud data contains the fine three-dimensional structure information of the water surface environment.

[0034] In step A104, the obstacle detection information, binocular SLAM point cloud data and radar point cloud data are fused to generate three-dimensional geometric topology information. This fusion process aims to combine the advantages of different sensors, such as visual information providing texture and semantics, radar information providing accurate distance and robustness, and obstacle detection information providing clear obstacle boundaries, so as to construct a more complete, accurate and robust three-dimensional geometric topology information of the water surface.

[0035] Finally, in step A105, the three-dimensional geometric topology information and the water flow field vector information are fused to obtain the dynamic multi-modal environment map. This step combines the static geometric structure information with the dynamic water flow field information to form a comprehensive environment representation, providing a comprehensive environment perception basis for the path planning and control of the water surface robot.

[0036] The scheme of the present application can obtain water surface environment information from different dimensions by using multi-source heterogeneous sensors such as binocular vision cameras, millimeter wave radars and acoustic Doppler current profiler arrays. Among them, the binocular vision camera combined with polarization filtering and improved YOLOv7 algorithm effectively solves the problem of water surface mirror reflection interference and improves the accuracy of water surface floating object detection; the millimeter wave radar provides reliable obstacle point cloud data under complex lighting or bad weather conditions; the acoustic Doppler current profiler array directly obtains water flow field vector information, providing accurate data for water flow dynamics analysis. These heterogeneous data are fused, not only constructing an environment model containing three-dimensional geometric topological information of the water surface, but also integrating water flow field vector information, thereby forming a dynamic multi-modal environment map. The map can comprehensively reflect the static obstacle distribution and dynamic water flow characteristics of the water surface environment, providing a solid data foundation for subsequent global path planning and flow field disturbance prediction.

[0037] Through the above technical scheme, the present application can significantly improve the comprehensiveness and accuracy of environment information collection. The cooperative work of multi-source heterogeneous sensors effectively makes up for the limitations of single sensors in specific environments, for example, the polarization filtering technology effectively suppresses the interference of water surface mirror reflection on visual detection, making water surface floating object detection more accurate. At the same time, by fusing three-dimensional geometric topological information and water flow field vector information, a dynamic multi-modal environment map is constructed, so that the water surface robot can not only perceive static obstacles, but also real-time master the dynamic changes of the water flow field, providing more abundant and reliable environment perception data for high-precision trajectory tracking, thereby improving the adaptability and operation precision of the water surface robot in complex water environment.

[0038] Specifically, the improved quantum particle swarm optimization algorithm embedded with the flow dynamics equation constraint is: In the fitness function of the quantum particle swarm optimization algorithm, the discretized Navier-Stokes equation is embedded as a physical constraint condition to optimize the energy consumption and safety of the path.

[0039] In particular, the quantum particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which finds the optimal solution by simulating the movement of particles in the solution space. The fitness function is a standard for evaluating the pros and cons of each particle (i.e., potential path scheme), and the higher the value, the more the path scheme meets the optimization goal. In this application, the fitness function is designed to not only consider traditional factors such as path length and obstacle avoidance, but more importantly, to embed the discretized Navier-Stokes equation as a physical constraint condition. The discretized Navier-Stokes equation is a discrete form of the partial differential equation set describing the motion of fluid, which can accurately reflect the dynamic characteristics of the water flow field. By embedding it as a constraint condition in the fitness function, it means that the algorithm will consider the influence of water flow on robot motion during path planning, so that the planned path is physically more feasible and more consistent with the actual water flow environment. In practical applications, the water flow field vector information includes the flow velocity and direction data of the gridded water flow field. These data can obtain the vertical profile water flow velocity through vector synthesis method, and based on the obtained vertical profile distribution data of water flow, a discretized model of Navier-Stokes equation of local flow field is established (for example, the equation in the foregoing based on immersed boundary method (IBM) to describe the robot-fluid coupling relationship). The discretized model is used to provide the dynamic parameter information of the flow field, so as to accurately predict the influence of water flow on the thrust and resistance of the robot during path planning, and then optimize the energy consumption and safety of the path. For example, by avoiding upstream or taking advantage of downstream navigation, energy consumption can be significantly reduced; by avoiding turbulent or dangerous eddy areas, navigation safety can be improved.

[0040] For example, the fitness function can be: ; wherein, is the fitness function value, is the start time, is the end time, is the time, is the dominant normalized weight coefficient of the flow field, is the energy constraint priority normalized weight coefficient, is the safety priority normalized weight coefficient, is the speed of the water surface robot, is the flow velocity of the water flow field, is the kinetic energy of the water surface robot, is the distance between the water surface robot and the obstacle, is the proportional coefficient.

[0041] The scheme of the present application embeds the discretized Navier-Stokes equations as physical constraints into the fitness function of the quantum particle swarm optimization algorithm, so that the path planning process can deeply integrate the dynamic characteristics of the water flow field. Specifically, the Navier-Stokes equations can describe the changes of physical quantities such as water flow velocity and pressure with time and space, thereby accurately simulating the force of water flow on the water surface robot. When these equations are discretized and introduced as constraints into the fitness function, the quantum particle swarm optimization algorithm, when searching for the optimal path, no longer only considers geometric distance or obstacle avoidance, but evaluates the energy consumption and potential risks of each candidate path under the actual water flow. For example, the algorithm will tend to select paths that can take advantage of water flow boost, reduce counterflow resistance, or avoid high shear force and strong vortex area. It is precisely due to the introduction of such physical constraints that the planned path is not only geometrically feasible, but also has optimal energy efficiency and highest safety in dynamics.

[0042] Through the above technical scheme, the present application can significantly improve the intelligence and adaptability of the global path planning of the water surface robot. Specifically, since the complex dynamic influence of the water flow field is fully considered in the path planning stage, the generated reference trajectory can better adapt to the actual water flow environment, thereby effectively reducing the energy consumption of the water surface robot when performing tasks and prolonging its endurance time. At the same time, by avoiding potential dangerous water flow areas such as strong vortex or rapids, the navigation safety of the water surface robot in complex water environment is significantly improved. This optimization method based on physical constraints makes the trajectory tracking control of the water surface robot not only accurate, but also more robust and reliable in actual application.

[0043] Preferably, in step A2, the global path obtained by planning can be subjected to smooth parameterization processing by B-spline curve to generate the reference trajectory.

[0044] Specifically, B-spline curve is a mathematical tool widely used in computer-aided design and graphics, which has the characteristic of generating smooth curves with arbitrary order continuity. B-spline curve is defined by a set of control points and node vectors, and its local controllability makes the modification of the curve only affect the local area, without affecting the whole curve. In addition, B-spline curve also has the excellent properties of convex hull and variation reduction, which can effectively avoid self-intersection of the path and ensure that the generated trajectory is smooth and meets the physical constraints. In practical application, the order of B-spline curve can be selected according to the required smoothness and computational efficiency, for example, cubic B-spline curve is often used in situations requiring C2 continuity, which can provide good smoothing effect.

[0045] The scheme of the present application can effectively solve the problem of trajectory discontinuity that may exist in traditional smoothing methods by using B-spline curves for smoothing parameterization. The mathematical properties of B-spline curves guarantee the high-order continuity of the generated trajectory in geometry, for example, a cubic B-spline curve can ensure the continuity of the second derivative (i.e. the rate of change of curvature) of the trajectory. This high-order continuity is crucial for the trajectory tracking of water surface robots, as it means that the robot can move along the trajectory in a more stable and predictable manner, avoiding sudden acceleration or deceleration or turning due to trajectory discontinuity, thereby reducing the impact on the actuators and improving the stability of the control system. In addition, the local controllability of B-spline curves allows for local adjustments to the global path by modifying only a few control points without the need to recalculate the entire path, which can significantly improve the efficiency of path planning and trajectory generation in dynamic environments or scenarios requiring real-time path updates.

[0046] By the above technical scheme, the global path is smoothed and parameterized using B-spline curves, which can generate a reference trajectory with high-order continuity and good smoothness. This allows the water surface robot to achieve more stable and accurate motion control when tracking the reference trajectory, effectively reducing trajectory tracking errors and reducing system oscillation and energy consumption caused by trajectory discontinuity. As a result, not only the performance of high-precision trajectory tracking of the water surface robot is improved, but also the service life of the actuators is prolonged, and the overall reliability and efficiency of the system are improved.

[0047] In some embodiments, step A4 comprises: A401. Real-time acquisition of water flow field velocity and trajectory tracking error; A402. When the water flow field velocity is not greater than a preset velocity threshold and the trajectory tracking error is not greater than a preset error threshold, a nonlinear model predictive controller is selected as the feedback controller; when the water flow field velocity is greater than the preset velocity threshold or the trajectory tracking error is greater than the preset error threshold, an adaptive sliding mode controller is selected as the feedback controller; A403. Generating a feedback control amount using the selected feedback controller; A404. Superimposing the feedback control amount and the feedforward control amount to generate a control instruction; A405. Driving the actuators of the water surface robot according to the control instruction.

[0048] Specifically, in step A401, real-time acquisition of water flow field velocity refers to continuous monitoring and acquisition of water flow velocity information in the current water area by sensors (such as acoustic Doppler current profiler arrays) carried by the water surface robot. At the same time, the trajectory tracking error refers to the deviation between the current actual position of the water surface robot and the corresponding expected position on the reference trajectory, which can be calculated based on the positioning information (such as GPS, inertial measurement unit, etc.) of the water surface robot and the reference trajectory.

[0049] wherein step A402 is the core of the present application scheme, which defines the adaptive switching strategy of the two feedback controllers. Specifically, when the water flow field flow rate is relatively small and the trajectory tracking error is within an acceptable range, the system is considered to be in a relatively stable working state, at which a nonlinear model predictive controller is selected as the feedback controller. The nonlinear model predictive controller can provide high-precision trajectory tracking under such conditions due to its ability to handle system nonlinearities, constraint conditions, and optimize future control performance. Conversely, when the water flow field flow rate is large or the trajectory tracking error exceeds the preset range, it indicates that the system may face large external disturbances or internal uncertainties, at which an adaptive sliding mode controller is selected as the feedback controller. The adaptive sliding mode controller is known for its strong robustness to system parameter uncertainty and external disturbances, and can effectively suppress errors to ensure stable tracking of the water surface robot in harsh environments. For example, the preset error threshold can be set to 0.3 m, and the preset flow rate threshold can be set to 2 m / s. These thresholds can be adjusted according to the specific performance requirements of the water surface robot and the actual application scenario.

[0050] In practical applications, step A403 refers to invoking the corresponding control algorithm to calculate the current feedback control amount according to the controller type determined in step A402. The feedback control amount aims to correct the real-time tracking error of the water surface robot.

[0051] Further, in step A404, the feedback control amount is superimposed with the feedforward control amount generated in step A3 to form the final control command. The feedforward control amount is mainly used to compensate for predictable flow field disturbances, while the feedback control amount is used to correct unmodeled dynamics and residual errors. The combination of the two can provide more comprehensive and accurate control effects.

[0052] Finally, in step A405, the generated control command is used to drive the actuators of the water surface robot, such as the vector thruster, to achieve precise adjustment of the motion state of the water surface robot, enabling it to track the reference trajectory with high precision.

[0053] The scheme of the present application effectively solves the problem that a single controller is difficult to balance high precision and strong robustness in complex water environment by introducing an adaptive controller switching mechanism based on water flow field flow velocity and trajectory tracking error. Specifically, in the case of smooth water flow and small tracking error, the nonlinear model predictive controller can exert its optimal control performance to achieve fine trajectory tracking; while in the challenging case of turbulent water flow and large tracking error, the adaptive sliding mode controller can quickly intervene to ensure that the water surface robot does not deviate too far from the predetermined trajectory and maintain the stability of the system by virtue of its strong disturbance suppression capability. In addition, the feedback control quantity and the feedforward control quantity are superimposed, so that the system can not only actively predict and offset the influence of known disturbances, but also respond and correct unknown disturbances and model errors in real time, thereby significantly improving the adaptability and accuracy of the overall control system.

[0054] Through the above technical scheme, the water surface robot can achieve high-precision trajectory tracking in a wider range of water environments, effectively overcoming the limitations of traditional control methods in the face of variable water flow conditions. The scheme significantly improves the reliability and efficiency of the water surface robot operation by intelligently selecting the most suitable controller for the current environment and performance requirements, and is particularly suitable for scenarios that require long-term and high-precision operation, such as water quality monitoring, underwater mapping, etc.

[0055] Preferably, in step A403, if the selected feedback controller is a nonlinear model predictive controller, the nonlinear model predictive controller generates a feedback control quantity by the following method: According to the current system state and the expected system state sequence of the reference trajectory within the prediction time domain window, the optimal control sequence within the control time domain window is solved by the following formula: ; Wherein, is the system state at time k, is the system state at time k+1, is the length of the prediction time domain window, is the expected system state at time k in the expected system state sequence of the reference trajectory within the prediction time domain window, is the optimal control sequence within the control time domain window, and the length of the control time domain window is less than (e.g. is 15, is 5, but not limited thereto), is the control quantity at time k, is a preset parameter matrix, is the water flow field flow velocity, and Q and R are both preset weight matrices, is the minimum control quantity that the system can output, is the maximum control amount that the system can output, is the distance between the water surface robot and the obstacle, is the preset safety distance, is the dynamics model of the water surface robot; extracts the control amount corresponding to k = 0 from the optimal control sequence as the feedback control amount at the current time.

[0056] Specifically, the system state refers to the kinematics and dynamics state of the water surface robot at time k, which may include its longitudinal position, lateral position, heading angle, speed, angular velocity, and acceleration, etc., and its purpose is to comprehensively describe the current motion of the robot. The length of the prediction horizon window defines the time range for the model predictive controller to predict the system behavior forward, while the length of the control horizon window represents the length of the control amount sequence that needs to be optimized in each control period, which is usually smaller than to balance the calculation complexity and control performance. is the expected system state of the reference trajectory at time k in the expected system state sequence within the prediction horizon window, and its purpose is to provide a clear tracking target for the controller. The control amount is the driving force or torque applied by the water surface robot at time k, and its purpose is to change the motion state of the robot. The parameter matrix is used to adjust the influence of the flow field flow rate on the system dynamics model, so as to more accurately predict the behavior of the robot under the disturbance of the water flow. The weight matrices Q and R are preset parameters used to balance the trajectory tracking error and control energy consumption in the optimization process, where Q is usually related to the state error term, and R is related to the control amount term, and its purpose is to ensure tracking accuracy while avoiding excessive control output. and represent the minimum and maximum control amounts that the system actuator can output, respectively, and their purpose is to ensure that the control instruction is within the physically feasible range. is the distance between the water surface robot and the obstacle, is the preset safety distance, and these two parameters together constitute the obstacle avoidance constraint, and their purpose is to ensure that the robot avoids collision with the obstacles in the environment while tracking the trajectory. is the dynamics model of the water surface robot, and its purpose is to describe the motion law of the water surface robot under the action of the control amount and external disturbance.

[0057] The scheme of the present application generates a feedback control quantity by constructing an optimization problem, which aims to minimize the tracking error between the water surface robot and the reference trajectory and the consumption of the control quantity, while satisfying the system dynamics constraints, actuator limitations and obstacle avoidance safety constraints. Preferably, in the process of solving the optimal control sequence, the nonlinear dynamics model of the water surface robot can be mapped to a simplified flat output space based on the differential flatness theory. Specifically, the originally complex 6-dimensional state space (including longitudinal position, lateral position, heading angle, speed, angular velocity and acceleration) is mapped to a 2-dimensional flat output (including only longitudinal position and heading angle). This mapping enables the construction of a linear prediction model in the flat output space, thereby converting the originally complex nonlinear optimization problem into a linear or quadratic programming problem with higher computational efficiency. As a result, the computational overhead required to solve the optimal control sequence is significantly reduced, enabling the nonlinear model predictive controller to achieve real-time and efficient feedback control on the water surface robot. Finally, the control quantity corresponding to the current time k=0 is extracted from the solved optimal control sequence as the current feedback control quantity, and this process is repeated in a rolling horizon manner to adapt to environmental changes and system disturbances.

[0058] Through the above technical scheme, the present application can significantly reduce the computational complexity of the nonlinear model predictive controller in the trajectory tracking application of the water surface robot, thereby greatly improving the computational efficiency of the control system. This enables the water surface robot to track trajectories in complex water environments with higher real-time requirements at a faster control period and higher response speed. Further, by mapping the nonlinear system to the flat output space and constructing a linear prediction model, not only the control performance is guaranteed, but also the demand for computational hardware resources is reduced, improving the robustness and practicality of the system. As a result, the water surface robot can achieve more accurate and stable trajectory tracking, effectively cope with flow disturbances and environmental changes, thereby improving its operation efficiency and safety.

[0059] Preferably, in step A403, if the selected feedback controller is an adaptive sliding mode controller, the adaptive sliding mode controller generates a feedback control quantity by the following way: According to the tracking error, the first derivative of the tracking error is obtained; The feedback control quantity is generated according to the following control model: ; ; ; wherein, is the feedback control quantity, is the sliding surface, is the tracking error, is the first derivative of the tracking error, is an integral weight coefficient, is a boundary layer thickness (e.g. set as 0.1), and are preset sliding mode controller parameters, is an adaptive sliding mode controller parameter, is the time start of the current rolling control period, is the current time (the rolling control period is a preset time length control period, for a rolling control period with a preset time length T, the time start of the first rolling control period is 0 time, and the end point is T time, whenever the time reaches the end point of the current rolling control period, the next rolling control period is entered, and the time start of the next rolling control period is set as the current time, wherein the preset time length T can be set as 10s-15s).

[0060] Specifically, the tracking error e is the deviation between the current position of the water surface robot and the desired position on the reference trajectory, and the first derivative e reflects the rate of change of the error with time. Obtaining the first derivative of the tracking error can generally be obtained by differentiating the real-time tracking error or using a state observer or filter. The sliding surface s is a core concept in sliding mode control theory, which defines a hyperplane in the state space of a system. When the system state is driven onto the hyperplane and slides along it, the system will exhibit the desired dynamic characteristics. In this application, the sliding surface s is defined as a linear combination of the tracking error e and its integral term, where λ is an integral weight coefficient for adjusting the influence of the integral term on the formation of the sliding surface, thereby helping to eliminate steady-state error. The feedback control quantity is generated by combining the proportional term , the derivative term and the sliding mode control term . Among them, and are preset sliding mode controller parameters for adjusting the response characteristics of proportional and derivative control. is an adaptive sliding mode controller parameter, the value of which is adaptively adjusted by the function. This adaptive mechanism enables the controller to dynamically adjust the control gain according to the size of the current tracking error e, when the error is large, increases to provide stronger control force to quickly reduce the error; when the error is small, decreases to avoid excessive oscillation. is a boundary layer thickness, which is used to introduce a continuous control law near the sliding surface to weaken the chattering phenomenon existing in traditional sliding mode control and improve the smoothness of control.

[0061] The scheme of the present application effectively solves the problems of strong disturbance and uncertainty faced by the system when the flow velocity of the water flow field is high or the trajectory tracking error is large by introducing an adaptive sliding mode controller. When the surface robot is in these challenging working conditions, the adaptive sliding mode controller is activated. The controller first constructs a sliding surface s according to the real-time tracking error e and its first derivative e, which aims to guide the system state to converge quickly to the desired trajectory. Through the adaptive adjustment of the parameter, the controller can dynamically adjust the control strength according to the error size, so as to provide strong correction force when the error is large, and maintain stable control when the error is small. In addition, the introduction of the boundary layer thickness effectively alleviates the chattering problem inherent in traditional sliding mode control, making the control output smoother and reducing the wear and tear on the actuator. This mechanism enables the surface robot to maintain robust tracking ability of the reference trajectory in complex and variable water flow environment.

[0062] Through the above technical scheme, the present application can provide a robust and efficient feedback control strategy when the surface robot faces challenging working conditions such as high flow velocity water flow disturbance or large trajectory tracking error. The adaptive sliding mode controller significantly improves the trajectory tracking accuracy and stability of the surface robot in complex environment through its strong suppression ability to uncertainty and external disturbance. In particular, the adaptive adjustment mechanism enables the controller to dynamically optimize control performance according to the actual working conditions, avoiding the performance degradation problem of fixed parameter controller in different working conditions. At the same time, the application of boundary layer thickness effectively suppresses control chattering, prolongs the service life of the actuator, and improves the smooth running performance of the system.

[0063] With reference to Figure 2 , the present application provides a high-precision trajectory tracking control system for a surface robot, which comprises: a multi-modal environment map generation module 1 for collecting environmental information through multi-source heterogeneous sensors to fuse and construct a dynamic multi-modal environment map containing three-dimensional geometric topological information and water flow field vector information of the water surface (the specific process can be referred to the step A1 in the foregoing) ; a reference trajectory generation module 2 for performing global path planning using an improved quantum particle swarm optimization algorithm with embedded water flow dynamics equation constraints according to the dynamic multi-modal environment map, and performing smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory (the specific process can be referred to the step A2 in the foregoing) ; The feedforward control quantity generation module 3 is configured to predict flow field disturbance based on a fluid-structure coupling dynamics model according to water flow field vector information in the dynamic multi-modal environment map, estimate system residual disturbance by using an extended state observer, and generate a feedforward control quantity in combination of the flow field disturbance and the system residual disturbance (for details, refer to step A3 in the foregoing). The control driving module 4 is configured to adaptively switch a nonlinear model predictive controller and an adaptive sliding mode controller according to real-time water flow field flow velocity and trajectory tracking error, generate a control instruction in combination of the feedforward control quantity to drive the water surface robot to track the reference trajectory (for details, refer to step A4 in the foregoing).

[0064] Please refer to Figure 3 A structure schematic diagram of an electronic device provided by the embodiment of the present application is provided, and the present application provides an electronic device, which comprises a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanism (not marked). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to execute the water surface robot high-precision trajectory tracking control method in any optional implementation manner of the above-mentioned embodiments, so as to realize the following functions: collecting environment information by using a multi-source heterogeneous sensor, so as to fuse and construct a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; performing global path planning by using an improved quantum particle swarm optimization algorithm embedded with a water flow dynamics equation constraint according to the dynamic multi-modal environment map, and performing smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory; predicting flow field disturbance based on a fluid-structure coupling dynamics model according to water flow field vector information in the dynamic multi-modal environment map, estimating system residual disturbance by using an extended state observer, and generating a feedforward control quantity in combination of the flow field disturbance and the system residual disturbance; adaptively switching a nonlinear model predictive controller and an adaptive sliding mode controller according to real-time water flow field flow velocity and trajectory tracking error, generating a control instruction in combination of the feedforward control quantity to drive the water surface robot to track the reference trajectory.

[0065] The embodiment of the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to execute the water surface robot high-precision trajectory tracking control method in any optional implementation manner of the above embodiment, so as to realize the following functions: environment information is collected by a plurality of source heterogeneous sensors, and is used to fuse and construct a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm with embedded water flow dynamics equation constraints is used for global path planning, and the global path obtained by planning is subjected to smoothing parameterization processing to generate a reference trajectory; according to water flow field vector information in the dynamic multi-modal environment map, a fluid-structure coupling dynamics model is used to predict flow field disturbance, and an extended state observer is used to estimate system residual disturbance, and the flow field disturbance and the system residual disturbance are combined to generate a feedforward control amount; according to real-time water flow field flow velocity and trajectory tracking error, a nonlinear model predictive controller and an adaptive sliding mode controller are adaptively switched, and the feedforward control amount is combined to generate a control instruction to drive the water surface robot to track the reference trajectory. The computer readable storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disk.

[0066] The above merely describes the embodiments of the application, and is not used to limit the protection scope of the application. For those skilled in the art, the application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A high-precision trajectory tracking control method for a water surface robot, characterized in that, The method comprises the following steps: A1. Collecting environmental information by a multi-source heterogeneous sensor to fuse and construct a dynamic multi-modal environment map containing three-dimensional geometric topological information and water flow field vector information of the water surface; A2. According to the dynamic multi-modal environment map, a global path planning is performed by using an improved quantum particle swarm optimization algorithm embedded with water flow dynamics equation constraints, and a reference trajectory is generated by performing smoothing parameterization processing on the global path obtained by planning; A3. According to the water flow field vector information in the dynamic multi-modal environment map, a flow field disturbance is predicted based on a fluid-structure coupling dynamics model, a system residual disturbance is estimated by using an extended state observer, and a feedforward control amount is generated in combination with the flow field disturbance and the system residual disturbance; A4. According to the real-time water flow field flow rate and trajectory tracking error, an adaptive switching is performed between a nonlinear model predictive controller and an adaptive sliding mode controller, a control instruction is generated in combination with the feedforward control amount to drive the water surface robot to track the reference trajectory.

2. The high-precision trajectory tracking control method for water surface robots according to claim 1, characterized in that, The multi-source heterogeneous sensor comprises a binocular vision camera, a millimeter wave radar and an acoustic Doppler current profiler array; Step A1 comprises: A101. Collecting water surface images by using the binocular vision camera, acquiring radar point cloud data of the water surface by using the millimeter wave radar, and acquiring water flow field vector information by using the acoustic Doppler current profiler array; A102. Polarization filtering is performed on the water surface images to obtain binocular vision images, and an improved YOLOv7 algorithm is used to detect water surface floating objects in the binocular vision images to obtain obstacle detection information; A103. Based on the binocular vision images, a binocular vision SLAM algorithm is used to generate binocular SLAM point cloud data; A104. The three-dimensional geometric topological information is generated by fusing the obstacle detection information, the binocular SLAM point cloud data and the radar point cloud data; A105. The dynamic multi-modal environment map is obtained by fusing the three-dimensional geometric topological information and the water flow field vector information.

3. The high-precision trajectory tracking control method for water surface robots according to claim 1, characterized in that, The improved quantum particle swarm optimization algorithm embedded with water flow dynamics equation constraints is: In the fitness function of the quantum particle swarm optimization algorithm, the discretized Navier-Stokes equation is embedded as a physical constraint condition to optimize the energy consumption and safety of the path.

4. The high-precision trajectory tracking control method for water surface robots according to claim 1, characterized in that, In step A2, a B-spline curve is used to perform smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory.

5. The high-precision trajectory tracking control method for water surface robots according to claim 1, characterized in that, Step A4 comprises: A401. Real-time acquisition of water flow field flow rate and trajectory tracking error; A402. When the water flow field flow rate is not greater than a preset flow rate threshold and the trajectory tracking error is not greater than a preset error threshold, a nonlinear model predictive controller is selected as a feedback controller; when the water flow field flow rate is greater than the preset flow rate threshold or the trajectory tracking error is greater than the preset error threshold, an adaptive sliding mode controller is selected as a feedback controller; A403. A feedback control amount is generated by using the selected feedback controller; A404. A control instruction is generated by superimposing the feedback control amount and the feedforward control amount; A405. The control instruction is used to drive the actuator of the water surface robot.

6. The high-precision trajectory tracking control method for water surface robots according to claim 5, characterized in that, In step A403, if the selected feedback controller is a nonlinear model predictive controller, the nonlinear model predictive controller generates a feedback control amount by the following method: According to the current system state and the sequence of desired system states of the reference trajectory within the prediction time domain window, the optimal control sequence within the control time domain window is solved by the following formula: ； wherein, is the system state at time k, is the system state at time k+1, is the length of the prediction horizon, is the desired system state at time k in the sequence of desired system states of the reference trajectory within the prediction horizon, is the optimal control sequence within the control horizon, the length of the control horizon is smaller than , is the control amount at time k, is a predetermined parameter matrix, is the water flow field velocity, Q and R are both predetermined weight matrices, is the minimum control amount that the system can output, is the maximum control amount that the system can output, is the distance between the water surface robot and the obstacle, is a predetermined safety distance, is the dynamics model of the water surface robot; extracting a control amount corresponding to the time k = 0 in the optimal control sequence as a feedback control amount at the current time.

7. The high-precision trajectory tracking control method for water surface robots according to claim 5, characterized in that, In step A403, if the selected feedback controller is an adaptive sliding mode controller, the adaptive sliding mode controller generates a feedback control amount by the following method: According to the tracking error, a first derivative of the tracking error is obtained; A feedback control amount is generated according to the following control model: ； ； ； wherein, is the feedback control amount, is a sliding surface, is the tracking error, is a first derivative of the tracking error, is an integral weight coefficient, is a boundary layer thickness, and is a preset sliding mode controller parameter, is an adaptive sliding mode controller parameter, is a time start point of a current rolling control period, is a current time.

8. A high-precision trajectory tracking control system for a water surface robot, characterized in that, The system comprises: A multi-modal environment map generation module is configured to collect environment information through multi-source heterogeneous sensors, and to fuse and construct a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; A reference trajectory generation module is configured to perform global path planning by using an improved quantum particle swarm optimization algorithm with embedded water flow dynamics equation constraints according to the dynamic multi-modal environment map, and to perform smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory; A feedforward control amount generation module is configured to predict flow field disturbance based on a fluid-structure coupling dynamics model according to water flow field vector information in the dynamic multi-modal environment map, to estimate system residual disturbance by using an extended state observer, and to generate a feedforward control amount in combination with the flow field disturbance and the system residual disturbance; A control driving module is configured to adaptively switch a nonlinear model predictive controller and an adaptive sliding mode controller according to real-time water flow field flow velocity and trajectory tracking error, to generate a control instruction in combination with the feedforward control amount to drive the water surface robot to track the reference trajectory.

9. An electronic device, comprising: A processor and a memory are included, the memory stores a computer program executable by the processor, and the processor executes the computer program to run the steps of the water surface robot high-precision trajectory tracking control method according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to run the steps of the water surface robot high-precision trajectory tracking control method according to any one of claims 1-7.

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