Underwater robot and umbilical cable system cooperative control simulation system

Abstract

The invention discloses a cooperative control simulation system for an underwater robot and an umbilical cable system, belongs to the technical field of ocean engineering simulation and automatic control, and is used for analyzing system dynamic characteristics in the research and development stage of deep sea operation equipment. Comprising an environment initialization and marine environment construction module, a mother ship model construction module, an underwater robot and umbilical cable coupling module, an instruction generation module, a man-machine interaction control module and a data acquisition and storage module. According to the method, an external interference source with physical authenticity is provided for a control algorithm by combining an energy density function and simulating a composite environment modeling method of a sea level height field and an LOD dynamic grid, and a distributed model prediction control algorithm for uniformly optimizing underwater robot trajectory tracking and umbilical cable tension safety maintenance is constructed; data such as the motion state of the underwater robot and the tension of the umbilical cable can be output in real time, algorithm performance testing is completed in a virtual environment, and dependence on high-cost sea testing is not needed.

Description

Technical Field

[0001] This invention discloses a simulation system for the collaborative control of underwater robots and umbilical cable systems, belonging to the field of marine engineering simulation and automatic control technology. Background Technology

[0002] As marine resource development rapidly expands into the deep sea, underwater robots (UVs) and umbilical cable coupling systems, as core equipment for deep-sea exploration and resource extraction, directly determine the success or failure of these missions through their stable operation and control precision. Currently, performance testing and control algorithm verification of UV robot-umbilical cable coupling systems mainly rely on two methods: actual sea trials and traditional simulation software analysis. Both methods have significant technical shortcomings. Actual sea trials need to be carried out in a complex and ever-changing deep-sea environment. Not only are the testing costs high and the cycle long, but they are also susceptible to environmental interference such as wind, waves, currents, and seabed topography, which can lead to equipment damage and distorted test data, posing serious technical risks and economic costs. Traditional simulation software, such as ROS and MATLAB, can perform basic simulation calculations, but their visualization level is low. They are unable to intuitively reproduce the dynamic coupling state of underwater robots and umbilical cables and their interaction with complex marine environments. They cannot provide high-fidelity visual feedback and multi-dimensional data support for algorithm verification and system optimization. Existing simulation studies are mostly limited by single-dimensional analysis. Some studies focus on the tension characteristics of the umbilical cable, analyzing its tension fluctuations under different sea conditions and depths, but fail to consider the motion state of the underwater robot. Other studies often ignore the interference of umbilical cable tension when performing kinematic and dynamic analysis or algorithm verification of underwater robots, and only model the underwater robot body. This results in a large deviation between the simulation results and the actual operation scenario of the dynamic coupling between the underwater robot and the umbilical cable, which cannot support the needs of system-level optimization and verification.

[0003] Furthermore, existing technologies lack a comprehensive simulation platform capable of simultaneously achieving high-fidelity reproduction of complex marine environments, dynamic coupling simulation of underwater robot-umbilical cable systems, online verification of multiple control algorithms, and full-process data acquisition and backtracking. Researchers struggle to complete full-dimensional testing and algorithm iteration of the coupled system in a low-risk, low-cost virtual environment. Therefore, exploring and developing a more comprehensive, high-fidelity, and multifunctional simulation platform for underwater robot-umbilical cable coupling systems to support the reproduction of complex marine environments, visualization of dynamic coupling processes, and performance verification of collaborative control algorithms has become an urgent need and an important direction for technological development in the field of deep-sea equipment research and development. Summary of the Invention

[0004] The purpose of this invention is to provide a simulation system for the collaborative control of underwater robots and umbilical cable systems, in order to solve the problem in the prior art that it is difficult for researchers to complete full-dimensional testing and algorithm iteration of the coupled system in a low-risk, low-cost virtual environment.

[0005] The underwater robot and umbilical cable system collaborative control simulation system includes an environment initialization and marine environment construction module, a mother ship model construction module, an underwater robot and umbilical cable coupling module, a command generation module, a human-machine interaction control module, and a data acquisition and storage module. The underwater robot and umbilical cable coupling module includes a virtual simulation engine and a solver.

[0006] A sea surface wind speed model is constructed using the environmental initialization and marine environment construction modules. This model includes a wind spectrum model and a significant wave height set according to a preset sea state level. The average wind speed at a reference height in the wind spectrum model is then assigned. Introducing the energy density function : ; ; In the formula, This is the current calculated height. This serves as the reference height for the wind spectrum model. For the shape parameters of the wind spectrum model, , where represents the scale coefficient of the wind spectrum model. The wind profile height index. For wind fluctuation frequency, For dimensionless frequency, The frequency coefficient is a dimensionless frequency coefficient. pass Calculate the fluctuating wind speed component Including to Equal-frequency sampling is performed, and random phases are superimposed on each frequency component. The frequency domain signal is then converted into a time domain signal using inverse Fourier transform, yielding... .

[0007] Superimposed base wind speed Gust weight Gradual wind component The combined wind speed is obtained. The expression: ; ; ; ; ; In the formula, The input is the set wind speed. Maximum gust speed, For time, This is the start time of the gust. The duration of gusts. The maximum wind gradient speed, This represents the start time of the gradual wind change. This is the end time of the gradual wind transition. The duration for which the gradually changing wind remains constant. This represents the total number of frequency sampling points. For the index of the frequency sampling point, , For the first The energy density function of each frequency sampling point For the first Each frequency sampling point The frequency sampling interval, For the first Random phase of each frequency sampling point.

[0008] The environmental initialization and marine environment construction module, based on the simulated sea level height field, normal, and sharp waves, uses Fast Fourier Transform to construct an ocean wave model. This includes constructing the simulated sea level height field and setting the wavenumber range of the two-dimensional wave space wavenumber domain. and , , , For the waves along Spatial wavenumber in the direction, For the waves along Spatial wavenumber in the direction, For the waves along Number of samples at discrete points in the direction. For the waves along Number of samples at discrete points in the direction. For the waves along Sea surface grid size in the direction, For the waves along The size of the sea surface grid in the direction; spatial location wave height at the location Represented as the superposition of wavenumber components in the two-dimensional wave space wavenumber domain, frequency domain signal Amplitudes were calculated from Phillips empirical wave spectra and generated by superimposing random phases; By using inverse Fourier transform Transforming to the spatial domain, the sea surface height field is finally obtained. : ; In the formula, For wave vectors, , The imaginary unit, It is a natural exponential function; Introducing the Philip empirical wave spectrum Determine the wave height amplitude corresponding to each wave number component: ; In the formula, For Philips spectral parameters, It is a two-dimensional wave vector. Wavenumber modulus, , For wind direction, For the largest wave, , For wind speed, It is the acceleration due to gravity; The normal vector of each grid point is calculated based on the spatial variation of the simulated sea level height field. : ; In the formula, for The fundamental wavenumber in the direction, , for The fundamental wavenumber in the direction, ; Introducing a horizontal displacement field Describe the horizontal motion of wave particles: ; Will Decomposed into , Two directions: ; ; In the formula, for exist directional components, for exist Component of direction; Representing overlapping waves using Jacobian determinants : ; ; ; ; ; In the formula, The wave intensity coefficient is the peak wave intensity coefficient. The sign is for the partial derivative; overlapping waves are the crests of overlapping ocean waves. when determinant When the value is less than 0, a nonlinear compression function is used to... Make corrections.

[0009] A steady ocean current model is constructed using the environmental initialization and marine environment construction modules, including a two-dimensional irrotational ocean current model. , For ocean current speed, In the direction of the ocean current; Import the 3D models of the mother ship and underwater robot using the mother ship model building module.

[0010] The underwater robot and umbilical cable coupling module constructs a dynamic model of the coupling between the underwater robot and the umbilical cable, including the construction of the geodetic coordinate system E-xyz, the underwater robot body coordinate system O-xyz, and the umbilical cable local coordinate system btn; Configure the inertial matrix for the underwater robot in the virtual simulation engine. Based on the parameters of mass, center of gravity, and center of buoyancy, a six-degree-of-freedom motion model is established using the Fossen marine vehicle dynamics framework. The umbilical cable is discretized using the lumped mass method. Duan He Each quality node, for each node Establish the motion control equations for the umbilical cable: ; In the formula, For nodes The mass matrix, for The corresponding acceleration, For nodes gravity, For nodes buoyancy, For nodes Resistance from water flow, For the first Elastic tension between nodes For the first Elastic tension between nodes; Define the umbilical cable starting node constraint and rigidly constrain the umbilical cable starting node at the position of the mother ship mooring point; Define constraints on the end nodes of the umbilical cable, synchronize the position coordinates of the end nodes with the tethering point of the underwater robot in real time, and include the tension generated at the end of the cable as an external force in the force balance equation of the underwater robot. The force balance equation of the underwater robot is as follows: ; In the formula, The mass matrix of the underwater robot. For the acceleration of the underwater robot, The Coriolis centripetal force matrix, For the speed of the underwater robot, For hydrodynamic damping matrix, Here is the restoring force matrix. For the propulsion force and torque vector, This represents the tension and torque vector of the umbilical cable. This represents the external environmental disturbance vector experienced by the underwater robot. Using the force balance equations of underwater robots, the motion control equations of the umbilical cable, the constraints of the umbilical cable starting node, the constraints of the umbilical cable ending node, and the definition of velocity differential. A set of coupled dynamic equations is constructed, and the solver uses the fourth-order Runge-Kutta method to solve the dynamic equations in the time domain, obtaining the position and attitude sequence and velocity sequence of the underwater robot, as well as the position sequence, velocity sequence and tension distribution of each node of the umbilical cable.

[0011] The instruction generation module includes a controller that collects the pose sequence of the underwater robot and the tension data at the end of the umbilical cable, decomposes the underwater robot and umbilical cable coupling system into an underwater robot motion subsystem and a winch umbilical cable deployment and retrieval subsystem, and establishes a state space prediction model. Based on the roll angle of underwater robots Pitch angle Bow angle Longitudinal velocity lateral velocity Vertical velocity Roll rate Pitch angular velocity and yaw rate Construct the state vector of the underwater robot for: ; In the formula, for The position and attitude of the underwater robot at all times. for The linear velocity and angular velocity of the underwater robot at all times; The control input is the thruster thrust. The state vector of the winch umbilical cable deployment and retrieval subsystem is defined as the position and velocity of each node of the cable in the geodetic coordinate system, and the control input. Defined as the difference between the current velocity and the previous velocity of each node in the cable. Within each sampling period, the current velocity is... Using the position and velocity of the umbilical cable node as initial values, in the prediction time domain Inside, the state space prediction model is used to predict the state information of the underwater robot motion subsystem and the winch umbilical cable deployment and retrieval subsystem; A quadratic programming objective function with multiple objectives was constructed for the underwater robot and the umbilical cable coupling system. : ; ; ; ; In the formula, , and To replace the variable, This represents the desired reference state for the underwater robot. This is the weighting matrix of state errors. For the control input of the underwater robot, The cost of inputting information to underwater robots To control the weighting matrix of input costs, For the tension of the umbilical cable, for The cost, The weighted matrix of tension costs. The weighting coefficient for the relaxation penalty term. For the beginning of the umbilical cable, The end of the umbilical cable, The relaxation coefficient, For cable length, Input the cost weighting matrix for the cable node. The cost of input to the umbilical cable node.

[0012] Set constraints for the underwater robot and umbilical cable, and impose constraints on the underwater robot's build state: ; In the formula, Let be the lower bound of the underwater robot's state vector. This represents the upper bound of the underwater robot's state vector; Construct control constraints for the control inputs: ; In the formula, This represents the lower limit of the control input for the underwater robot. This represents the upper limit of the control input for the underwater robot. Set umbilical cable constraints: ; ; ; In the formula, For the first The position of the bow node that is constantly connected to the mother ship. For the first The location of the mothership at all times. For the first The speed of the bow node that is constantly connected to the mother ship. For the first The speed of the mothership at all times For node labels, For the mother ship's identification, For the node connected to the mother ship, For the first The location of the underwater robot at all times. This represents the position of the end effector point in the underwater robot's body coordinate system. This is the transformation matrix from the underwater robot's body coordinate system to the geodetic coordinate system. For the first The position of the end node constantly connected to the underwater robot. For the first The speed of the end node constantly connected to the underwater robot. The lower limit input for underwater robots and umbilical cable systems. Upper limit for inputs to underwater robots and umbilical cable systems; The coupled dynamic equations and the umbilical cable constraints together constitute the dynamic constraints.

[0013] At each sampling time, a constrained quadratic programming optimization problem is constructed, which includes the objective function, dynamic constraints, control constraints, and state constraints. The smooth Newton method is used to solve the problem and obtain the optimal control increment sequence in the prediction time domain. Model predictive control is performed at each sampling time using the smoothed Newton method, including the extraction of the control time domain. The optimal control increment sequence of the first step is converted into the thruster voltage signal and winch frequency. The command is sent to the simulation execution layer. When the next sampling time arrives, the underwater robot and umbilical cable system use the measured state for feedback correction, re-initialize the prediction model and solve the constrained quadratic programming optimization problem in a rolling manner until the processing of all sampling time moments is completed. The underwater robot motion subsystem and the winch umbilical cable deployment and retrieval subsystem share predicted state information and alternately iteratively solve a constrained quadratic programming optimization problem.

[0014] The human-computer interaction control module includes a marine parameter configuration interface, an underwater robot and umbilical cable system simulation test interface, and a data playback interface; The data acquisition and storage module records the underwater robot's speed, position data, and umbilical cable end tension data, generating replayable logs and reports.

[0015] Compared to existing technologies, this invention offers the following advantages: First, the system not only achieves visual digital twins but also simulates the combined effects of real sea conditions on the system at the dynamic level. This allows control parameters tested in the simulation platform to be directly transferred to physical testing, greatly improving the reliability of the algorithm. Second, by using the DMPC algorithm for real-time monitoring and predictive adjustment of umbilical cable tension, the invention addresses the significant problem of underwater robots being severely affected by umbilical cable swaying during deep-water operations. The collaborative control logic compensates for the drag force from the cable, enabling the underwater robot to maintain high-precision hovering or trajectory tracking capabilities even in complex sea conditions. Third, addressing the lack of integrated algorithm verification in existing tools, this invention embeds a distributed model predictive control algorithm in Unity3D, designs a dual-view and interactive UI, and stores multi-dimensional data at 50Hz sampling, enhancing human-computer interaction and data processing capabilities, improving R&D efficiency, and reducing R&D costs and the risk of equipment loss due to uncertainties in the actual testing environment. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the technical process of the present invention. Figure 2 This is a flowchart of the simulation system of the present invention; Figure 3 This is a map showing the drift trajectory of an underwater robot. Figure 4 This is a graph showing the longitudinal velocity variation of the underwater robot. Figure 5 This is a graph showing the change in the lateral velocity of the underwater robot. Figure 6 This is a graph showing the vertical velocity variation of an underwater robot. Figure 7 A graph showing the change in the roll angular velocity of an underwater robot; Figure 8 This is a graph showing the pitch angular velocity variation of an underwater robot. Figure 9 This is a graph showing the variation of the yaw rate of an underwater robot. Figure 10 This is a diagram showing the change in the bow angle of an underwater robot. Figure 11A graph showing the pitch angle variation of an underwater robot; Figure 12 This is a graph showing the change in roll angle of an underwater robot. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0018] The underwater robot and umbilical cable system collaborative control simulation system includes an environment initialization and marine environment construction module, a mother ship model construction module, an underwater robot and umbilical cable coupling module, a command generation module, a human-machine interaction control module, and a data acquisition and storage module. The underwater robot and umbilical cable coupling module includes a virtual simulation engine and a solver.

[0019] A sea surface wind speed model is constructed using the environmental initialization and marine environment construction modules. This model includes a wind spectrum model and a significant wave height set according to a preset sea state level. The average wind speed at a reference height in the wind spectrum model is then assigned. Introducing the energy density function : ; ; In the formula, This is the current calculated height. This serves as the reference height for the wind spectrum model. For the shape parameters of the wind spectrum model, , where represents the scale coefficient of the wind spectrum model. The wind profile height index. For wind fluctuation frequency, For dimensionless frequency, The frequency coefficient is a dimensionless frequency coefficient. pass Calculate the fluctuating wind speed component Including to Equal-frequency sampling is performed, and random phases are superimposed on each frequency component. The frequency domain signal is then converted into a time domain signal using inverse Fourier transform, yielding... .

[0020] Superimposed base wind speed Gust weight Gradual wind component The combined wind speed is obtained. The expression: ; ; ; ; ; In the formula, The input is the set wind speed. Maximum gust speed, For time, This is the start time of the gust. The duration of gusts. The maximum wind gradient speed, This represents the start time of the gradual wind change. This is the end time of the gradual wind transition. The duration for which the gradually changing wind remains constant. This represents the total number of frequency sampling points. For the index of the frequency sampling point, , For the first The energy density function of each frequency sampling point For the first Each frequency sampling point The frequency sampling interval, For the first Random phase of each frequency sampling point.

[0021] The environmental initialization and marine environment construction module, based on the simulated sea level height field, normal, and sharp waves, uses Fast Fourier Transform to construct an ocean wave model. This includes constructing the simulated sea level height field and setting the wavenumber range of the two-dimensional wave space wavenumber domain. and , , , For the waves along Spatial wavenumber in direction, For the waves along Spatial wavenumber in the direction, For the waves along Number of samples at discrete points in the direction. For the waves along Number of samples at discrete points in the direction. For the waves along Sea surface grid size in the direction, For the waves along The size of the sea surface grid in the direction; spatial location wave height at the location Represented as the superposition of wavenumber components in the two-dimensional wave space wavenumber domain, frequency domain signal Amplitudes were calculated from Phillips empirical wave spectra and generated by superimposing random phases; By using inverse Fourier transform Transforming to the spatial domain, the sea surface height field is finally obtained. : ; In the formula, For wave vectors, , The imaginary unit, It is a natural exponential function; Introducing the Philip empirical wave spectrum Determine the wave height amplitude corresponding to each wave number component: ; In the formula, For Philips spectral parameters, It is a two-dimensional wave vector. Wavenumber modulus, , For wind direction, For the largest wave, , For wind speed, It is the acceleration due to gravity; The normal vector of each grid point is calculated based on the spatial variation of the simulated sea level height field. : ; In the formula, for The fundamental wavenumber in the direction, , for The fundamental wavenumber in the direction, ; Introducing a horizontal displacement field Describe the horizontal motion of wave particles: ; Will Decomposed into , Two directions: ; ; In the formula, for exist directional components, for exist Component of direction; Representing overlapping waves using Jacobian determinants : ; ; ; ; ; In the formula, The wave intensity coefficient is the peak wave intensity coefficient. The sign is for the partial derivative; overlapping waves are the crests of overlapping ocean waves. when determinant When the value is less than 0, a nonlinear compression function is used to... Make corrections.

[0022] A steady ocean current model is constructed using the environmental initialization and marine environment construction modules, including a two-dimensional irrotational ocean current model. , For ocean current speed, In the direction of the ocean current; Import the 3D models of the mother ship and underwater robot using the mother ship model building module.

[0023] The underwater robot and umbilical cable coupling module constructs a dynamic model of the coupling between the underwater robot and the umbilical cable, including the construction of the geodetic coordinate system E-xyz, the underwater robot body coordinate system O-xyz, and the umbilical cable local coordinate system btn; Configure the inertial matrix for the underwater robot in the virtual simulation engine. Based on the parameters of mass, center of gravity, and center of buoyancy, a six-degree-of-freedom motion model is established using the Fossen marine vehicle dynamics framework. The umbilical cable is discretized using the lumped mass method. Duan He Each quality node, for each node Establish the motion control equations for the umbilical cable: ; In the formula, For nodes The mass matrix, for The corresponding acceleration, For nodes gravity, For nodes buoyancy, For nodes Resistance from water flow, For the first Elastic tension between nodes For the first Elastic tension between nodes; Define the umbilical cable starting node constraint and rigidly constrain the umbilical cable starting node at the position of the mother ship mooring point; Define constraints on the end nodes of the umbilical cable, synchronize the position coordinates of the end nodes with the tethering point of the underwater robot in real time, and include the tension generated at the end of the cable as an external force in the force balance equation of the underwater robot. The force balance equation of the underwater robot is as follows: ; In the formula, The mass matrix of the underwater robot. For the acceleration of the underwater robot, The Coriolis centripetal force matrix, For the speed of the underwater robot, For hydrodynamic damping matrix, Here is the restoring force matrix. For the propulsion force and torque vector, This represents the tension and torque vector of the umbilical cable. This represents the external environmental disturbance vector experienced by the underwater robot. Using the force balance equations of underwater robots, the motion control equations of the umbilical cable, the constraints of the umbilical cable starting node, the constraints of the umbilical cable ending node, and the definition of velocity differential. A set of coupled dynamic equations is constructed, and the solver uses the fourth-order Runge-Kutta method to solve the dynamic equations in the time domain, obtaining the position and attitude sequence and velocity sequence of the underwater robot, as well as the position sequence, velocity sequence and tension distribution of each node of the umbilical cable.

[0024] The instruction generation module includes a controller that collects the pose sequence of the underwater robot and the tension data at the end of the umbilical cable, decomposes the underwater robot and umbilical cable coupling system into an underwater robot motion subsystem and a winch umbilical cable deployment and retrieval subsystem, and establishes a state space prediction model. Based on the roll angle of underwater robots Pitch angle Bow angle Longitudinal velocity lateral velocity Vertical velocity Roll rate Pitch angular velocity and yaw rate Construct the state vector of the underwater robot for: ; In the formula, for The position and attitude of the underwater robot at all times. for The linear velocity and angular velocity of the underwater robot at all times; The control input is the thruster thrust. The state vector of the winch umbilical cable deployment and retrieval subsystem is defined as the position and velocity of each node of the cable in the geodetic coordinate system, and the control input. Defined as the difference between the current velocity and the previous velocity of each node in the cable. Within each sampling period, the current velocity is... Using the position and velocity of the umbilical cable node as initial values, in the prediction time domain Inside, the state space prediction model is used to predict the state information of the underwater robot motion subsystem and the winch umbilical cable deployment and retrieval subsystem; A quadratic programming objective function with multiple objectives was constructed for the underwater robot and the umbilical cable coupling system. : ; ; ; ; In the formula, , and To replace the variable, This represents the desired reference state for the underwater robot. This is the weighting matrix of state errors. For the control input of the underwater robot, The cost of inputting information to underwater robots To control the weighting matrix of input costs, For the tension of the umbilical cable, for The cost, The weighted matrix of tension costs. The weighting coefficient for the relaxation penalty term. For the beginning of the umbilical cable, The end of the umbilical cable, The relaxation coefficient, For cable length, Input the cost weighting matrix for the cable node. The cost of input to the umbilical cable node.

[0025] Set constraints for the underwater robot and umbilical cable, and impose constraints on the underwater robot's build state: ; In the formula, Let be the lower bound of the underwater robot's state vector. This represents the upper bound of the underwater robot's state vector; Construct control constraints for the control inputs: ; In the formula, This represents the lower limit of the control input for the underwater robot. This represents the upper limit of the control input for the underwater robot. Set umbilical cable constraints: ; ; ; In the formula, For the first The position of the bow node that is constantly connected to the mother ship. For the first The location of the mothership at all times. For the first The speed of the bow node that is constantly connected to the mother ship. For the first The speed of the mothership at all times For node labels, For the mother ship's identification, For the node connected to the mother ship, For the first The location of the underwater robot at all times. This represents the position of the end effector point in the underwater robot's body coordinate system. This is the transformation matrix from the underwater robot's body coordinate system to the geodetic coordinate system. For the first The position of the end node constantly connected to the underwater robot. For the first The speed of the end node constantly connected to the underwater robot. The lower limit input for underwater robots and umbilical cable systems. Upper limit for inputs to underwater robots and umbilical cable systems; The coupled dynamic equations and the umbilical cable constraints together constitute the dynamic constraints.

[0026] At each sampling time, a constrained quadratic programming optimization problem is constructed, which includes the objective function, dynamic constraints, control constraints, and state constraints. The smooth Newton method is used to solve the problem and obtain the optimal control increment sequence in the prediction time domain. Model predictive control is performed at each sampling time using the smoothed Newton method, including the extraction of the control time domain. The optimal control increment sequence of the first step is converted into the thruster voltage signal and winch frequency. The command is sent to the simulation execution layer. When the next sampling time arrives, the underwater robot and umbilical cable system use the measured state for feedback correction, re-initialize the prediction model and solve the constrained quadratic programming optimization problem in a rolling manner until the processing of all sampling time moments is completed. The underwater robot motion subsystem and the winch umbilical cable deployment and retrieval subsystem share predicted state information and alternately iteratively solve a constrained quadratic programming optimization problem.

[0027] The human-computer interaction control module includes a marine parameter configuration interface, an underwater robot and umbilical cable system simulation test interface, and a data playback interface; The data acquisition and storage module records the underwater robot's speed, position data, and umbilical cable end tension data, generating replayable logs and reports.

[0028] The following description, in conjunction with the accompanying drawings and embodiments, provides further details. A flowchart of the technical process of the present invention is shown below. Figure 1 As shown, the simulation system proceeds sequentially as follows: environmental initialization and marine environment construction; construction of ROV and mother ship visualization models; construction of ROV-umbilical cable coupled dynamics models; calculation of cable tension using the umbilical cable dynamics model; and construction of a six-degree-of-freedom dynamics model of the ROV-umbilical cable system. Then, DMPC collaborative instructions are generated, deconstructing the ROV-umbilical cable system into two coupled control units, and implementing closed-loop control through rolling optimization and feedback correction. Finally, the human-machine interaction control module, marine parameter configuration interface, ROV-umbilical cable system simulation test interface, and data return interface are developed. This simulation system supports parameter setting, real-time monitoring, and data visualization. Finally, experimental data is acquired and stored, then split into two branches: the first branch outputs simulation results, and the second branch returns to the DMPC collaborative instruction generation.

[0029] The operation process of the simulation system of this invention is as follows: Figure 2 As shown, the simulation platform is first started, and the marine parameter configuration interface is entered. The required marine parameters are set, and the ROV-umbilical cable system simulation test interface is accessed. Then, the system state is initialized, and the DMPC collaborative control algorithm is started for data output. This process is divided into three branches: the first branch maintains or adjusts parameters to continue the simulation; the second branch jumps to the data playback interface, loads historical simulation data, sets data playback parameters, and performs visual playback; the third branch stops the simulation, saves the data, and performs offline data analysis. Finally, the simulation results are evaluated, and the simulation ends.

[0030] In this embodiment of the invention, the International Sea State Standard (ISS) level is first used as the preset sea state level. The environmental baseline is automatically configured based on the input ISS level. The wind spectrum model calls a wind spectrum model based on the API specification. If sea state 4 is input, the system sets the effective wave height in the range of 1.25m to 2.5m. Assign the average wind speed at 10m height in the API wind spectrum according to sea states 0 to 7. In this embodiment, at sea state 0 Sea state 7 Among them, air density Wind load factor is taken , , Since ROVs are relatively small, their impact on the vertical axis is not considered when taking into account the effect of wind on the ROV. This is achieved through the formula... and Calculate wind force and corresponding yaw moment to provide realistic wind load input for ROV movement: ; ; In the formula, The wind force experienced by the ROV for The corresponding yaw moment, air density, , , The wind load factor for the three degrees of freedom of the ROV is given. The angle of attack of the ROV. This is the forward projection of the ROV. This is the lateral projection of the ROV. The length of the ROV; set up , , calculate ;set up When the value is less than 0.3, sharp wave generation is triggered. In the process of constructing an infinite sea surface, multi-level detail (LOD) mesh technology is used for real-time rendering optimization. The near-field area within a 10-meter radius of the virtual camera's field of view is defined as a high-density mesh area with a mesh sampling interval of 0.1m. The distant area beyond 50 meters is sparsified by using the distance from the vertex to the camera as the sparsity criterion and a vertex merging algorithm to achieve mesh sparsity. The sampling interval for the distant area is 4 times that for the near area. Under the premise of ensuring the physical fidelity of the waveform, the simulation system can run stably at a frame rate of over 30fps.

[0031] During the construction of the steady ocean current model, ocean current velocities were assigned according to sea states from 0 to 7. For sea state 0... At sea state 7, Ocean current direction It can be set from 0° to 360° through the UI interface, with the default direction along the positive x-axis.

[0032] ROV and Mothership Model Construction. 3D models of the mothership and ROV are created using Solidworks, then imported into Unity3D to form renderable ROV and mothership models.

[0033] Construction of the ROV-umbilical cable coupled dynamic model. In order to describe the complex motion of the system in the same coordinate system, three mutually transformable coordinate systems were established: a geodetic coordinate system fixed to the earth (E-xyz), a body coordinate system fixed to the center of gravity of the ROV (O-xyz), and a local coordinate system of the umbilical cable attached to the umbilical cable (btn).

[0034] Based on the actual physical design of the ROV, the system sets the ROV model dimensions in the virtual simulation engine to be 1.581m long, 0.940m wide, and 1.050m high, with a mass of 278.392kg and a center of buoyancy coordinate system. centroid coordinates The moments of inertia about the three axes are 34.87 and 34.87 respectively. 84.66 86.22 The inertia matrix is: ; The additional mass matrix is: ; A six-degree-of-freedom motion model based on the Fossen marine vehicle dynamics framework was established. The umbilical cable parameters were set as follows: cable length 150m, diameter 0.04m, weight per unit length in the air 1.22kg / m, normal drag coefficient 1.2, tangential drag coefficient 0.03, and Young's modulus... Minimum breaking tension The system uses the lumped mass method to discretize the umbilical cable into... Duan He Each quality node, for each node Establish the equations of motion; The solver uses a fourth-order Runge-Kutta method with a step size of 0.01s to solve the above-mentioned... A system of nonlinear differential equations involving state variables was solved simultaneously to accurately simulate the dynamic disturbance of the umbilical cable on the ROV's operational posture under the influence of water flow resistance and tension feedback. The final dynamic equations of the ROV-umbilical cable system are: ; The hydrodynamic damping coefficients were obtained through Fluent simulation fitting: the second-order translational damping coefficient for the x-axis was 309.32, and the linear damping coefficient was 4.788; the second-order translational damping coefficient for the y-axis was 603.12, and the linear damping coefficient was 1.205; and the second-order translational damping coefficient for the z-axis was 835.38, and the linear damping coefficient was 0.6626. For propulsion and torque, For umbilical cable tension and torque, This is the external environmental interference vector experienced by the ROV.

[0035] Establishing a state-space prediction model involves redefining the kinematic model of the ROV to meet its motion requirements. : ; ; ; in , , They represent circumference respectively. , , The angle of rotation of the axis; make ,in, This represents the rotation matrix that transforms the motion states of the ROV from the body coordinate system to the geodetic coordinate system. Therefore, the prediction model for the ROV system can be designed as follows: ; ; In the formula, Indicates the first ROV system state variables at any given time. Indicates the first ROV system state variables at any given time. Indicates the first The rotation matrix for transforming the ROV's body coordinate system to the geodetic coordinate system at any given time. Indicates the first The system velocity vector at time t, Indicates the first The system acceleration at any given time is determined by the control input. Indicates a time interval; The prediction model for the umbilical cord is as follows: ; In the formula, Indicates the first The moment the umbilical cable is first The location of each node in the geodetic coordinate system Indicates the first The moment the umbilical cable is first The location of each node in the geodetic coordinate system Indicates the first The moment the umbilical cable is first The velocity of each node in the geodetic coordinate system For time intervals.

[0036] Figures 3 to 12 This is a diagram of the ROV dynamics verification experiment of the present invention. In this diagram, the initial position, linear velocity and angular velocity of the ROV are all set to zero, and the wind direction is uniformly set to 0 degrees, that is, the positive x-axis direction points to the negative x-axis direction. Only the wind speed is changed, and the changes in the motion state of the ROV are observed. Figure 3 The drift trajectory of the ROV on the sea surface is significantly affected by wind and wave conditions: as wind speed increases, wave height increases accordingly, resulting in a significant increase in the horizontal drift distance of the ROV. In a horizontal coordinate system, it can be observed that under strong wind and wave conditions, the drift range of the ROV expands rapidly, and its trajectory exhibits more violent oscillation characteristics. Figures 4 to 6 The change in ROV linear velocity ( For longitudinal velocity, For lateral velocity, (Vertical velocity). Figures 7 to 9 The change in ROV angular velocity ( The roll rate is angular velocity. The pitch angular velocity, (yaw rate) Figures 10 to 12 The marine environment significantly influences the motion characteristics of ROVs, representing changes in ROV attitude (Heading, Pitch, and Roll). As wind speed increases, the ROV's motion response becomes significantly stronger, with more dramatic fluctuations in linear velocity, greater range of angular velocity changes, and more pronounced attitude angular oscillations. Simulation results demonstrate that this platform can effectively simulate the motion of ROVs under different wind speed conditions and also meets the requirements for subsequent simulation experiments.

[0037] Set the sampling period to 0.02s. Cooperative instructions based on Distributed Model Predictive Control (DMPC) are generated. Considering the actual situation and simulation requirements, constraints are set for the ROV and umbilical cable. During the ROV's task completion, it is not necessary to... , , To set constraints, considering the ROV's movement underwater, its attitude needs to be monitored. , , linear velocity , , and angular velocity , , Set constraints, including , , , , , , , , .

[0038] The design and development of the human-computer interaction control module involved developing an interactive UI with three interfaces: marine parameter configuration, ROV-umbilical cable system simulation testing, and data playback, to improve ease of operation. Running the ROV-umbilical cable coupling simulation testing system leads to the marine parameter configuration interface, where users can set relevant marine environmental parameters such as sea state, wave age, wind direction, and current direction. After setting these parameters, clicking the "Settings" button redirects to the ROV-umbilical cable system simulation testing interface, where the simulation test begins. This interface allows users to set desired motion parameters such as desired speed, desired altitude, and desired heading, and outputs real-time dynamic data of the system under algorithm control, such as ROV motion status and umbilical cable tension changes.

[0039] Experimental data acquisition and storage. The ROV velocity, position data, and umbilical cable end tension data of the ROV-umbilical cable coupling system during the experiment were created and written using the StreamWriter class in Unity3D. Data was stored at a refresh rate of 50Hz, i.e., data was stored every 20 milliseconds, generating a .csv format log file. This facilitates subsequent data analysis and processing, and also allows for real-time playback of the experimental data in the data playback interface.

[0040] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation system for coordinated control of an underwater robot and an umbilical cable system, characterized in that, It includes an environment initialization and marine environment construction module, a mother ship model construction module, an underwater robot and umbilical cable coupling module, an instruction generation module, a human-machine interaction control module, and a data acquisition and storage module; The underwater robot and umbilical cable coupling module includes a virtual simulation engine and a solver.

2. The underwater robot and umbilical cable system collaborative control simulation system according to claim 1, characterized in that, A sea surface wind speed model is constructed using the environmental initialization and marine environment construction modules. This model includes a wind spectrum model and a significant wave height set according to a preset sea state level. The average wind speed at a reference height in the wind spectrum model is then assigned. Introducing the energy density function : ； ； In the formula, This is the current calculated height. As the reference height for the wind spectrum model, For the shape parameters of the wind spectrum model, , where represents the scale coefficient of the wind spectrum model. The wind profile height index. For wind fluctuation frequency, For dimensionless frequency, The frequency coefficient is a dimensionless frequency coefficient. pass Calculate the fluctuating wind speed component Including to Equal-frequency sampling is performed, and random phases are superimposed on each frequency component. The frequency domain signal is then converted to a time domain signal using inverse Fourier transform, yielding... .

3. The underwater robot and umbilical cable system collaborative control simulation system according to claim 2, characterized in that, Superimposed base wind speed Gust weight Gradual wind component The combined wind speed is obtained. The expression: ； ； ； ； ； In the formula, The input is the set wind speed. Maximum gust speed, For time, This is the start time of the gust. The duration of gusts. The maximum wind gradient speed, This represents the start time of the gradual wind change. This is the end time of the gradual wind transition. The duration for which the gradually changing wind remains constant. This represents the total number of frequency sampling points. This is the index of the frequency sampling point. , For the first The energy density function of each frequency sampling point For the first Each frequency sampling point The frequency sampling interval, For the first Random phase of each frequency sampling point.

4. The underwater robot and umbilical cable system collaborative control simulation system according to claim 3, characterized in that, The environmental initialization and marine environment construction module, based on the simulated sea level height field, normal, and sharp waves, uses Fast Fourier Transform to construct an ocean wave model. This includes constructing the simulated sea level height field and setting the wavenumber range of the two-dimensional wave space wavenumber domain. and , , , For the waves along Spatial wavenumber in direction, For the waves along Spatial wavenumber in direction, For the waves along Number of samples at discrete points in the direction. For the waves along Number of samples at discrete points in the direction. For the waves along Sea surface grid size in the direction, For the waves along The size of the sea surface grid in the direction; spatial location wave height at the location Represented as the superposition of wavenumber components in the two-dimensional wave space wavenumber domain, frequency domain signal Amplitudes were calculated from Phillips empirical wave spectra and generated by superimposing random phases; By using inverse Fourier transform Transforming to the spatial domain, the sea surface height field is finally obtained. : ； In the formula, For wave vectors, , The imaginary unit, It is a natural exponential function; Introducing the Philip empirical wave spectrum Determine the wave height amplitude corresponding to each wave number component: ； In the formula, For Philips spectral parameters, It is a two-dimensional wave vector. Wavenumber modulus, , For wind direction, For the largest wave, , For wind speed, It is the acceleration due to gravity; The normal vector of each grid point is calculated based on the spatial variation of the simulated sea level height field. : ； In the formula, for The fundamental wavenumber in the direction, , for The fundamental wavenumber in the direction, ; Introducing a horizontal displacement field Describe the horizontal motion of wave particles: ； Will Decomposed into , Two directions: ； ； In the formula, for exist directional components, for exist Component of direction; Representing overlapping waves using Jacobian determinants : ； ； ； ； ； In the formula, The wave intensity coefficient is the peak wave intensity coefficient. The sign is for the partial derivative; overlapping waves are the crests of overlapping ocean waves. when determinant When the value is less than 0, a nonlinear compression function is used to... Make corrections.

5. The underwater robot and umbilical cable system collaborative control simulation system according to claim 4, characterized in that, A steady ocean current model is constructed using the environmental initialization and marine environment construction modules, including a two-dimensional irrotational ocean current model. , For ocean current speed, In the direction of the ocean current; Import the 3D models of the mother ship and underwater robot using the mother ship model building module.

6. The underwater robot and umbilical cable system collaborative control simulation system according to claim 5, characterized in that, The underwater robot and umbilical cable coupling module constructs a dynamic model of the coupling between the underwater robot and the umbilical cable, including the construction of the geodetic coordinate system E-xyz, the underwater robot body coordinate system O-xyz, and the umbilical cable local coordinate system btn; Configure the inertial matrix for the underwater robot in the virtual simulation engine. Based on the parameters of mass, center of gravity, and center of buoyancy, a six-degree-of-freedom motion model is established using the Fossen marine vehicle dynamics framework. The umbilical cable is discretized using the lumped mass method. Duan He Each quality node, for each node Establish the motion control equations for the umbilical cable: ； In the formula, For nodes The mass matrix, for The corresponding acceleration, For nodes gravity, For nodes buoyancy, For nodes Resistance from water flow, For the first Elastic tension between nodes For the first Elastic tension between nodes; Define the umbilical cable starting node constraint and rigidly constrain the umbilical cable starting node at the position of the mother ship mooring point; Define constraints on the end nodes of the umbilical cable, synchronize the position coordinates of the end nodes with the tethering point of the underwater robot in real time, and include the tension generated at the end of the cable as an external force in the force balance equation of the underwater robot. The force balance equation of the underwater robot is as follows: ； In the formula, The mass matrix of the underwater robot. For the acceleration of the underwater robot, The Coriolis centripetal force matrix, For the speed of the underwater robot, For hydrodynamic damping matrix, For the restoring force matrix, For the propulsion force and torque vector, This represents the tension and torque vector of the umbilical cable. This represents the external environmental disturbance vector experienced by the underwater robot. Using the force balance equations of underwater robots, the motion control equations of the umbilical cable, the constraints of the umbilical cable starting node, the constraints of the umbilical cable ending node, and the definition of velocity differential. A set of coupled dynamic equations is constructed, and the solver uses the fourth-order Runge-Kutta method to solve the dynamic equations in the time domain, obtaining the position and attitude sequence and velocity sequence of the underwater robot, as well as the position sequence, velocity sequence and tension distribution of each node of the umbilical cable.

7. The underwater robot and umbilical cable system collaborative control simulation system according to claim 6, characterized in that, The instruction generation module includes a controller that collects the pose sequence of the underwater robot and the tension data at the end of the umbilical cable, decomposes the underwater robot and umbilical cable coupling system into an underwater robot motion subsystem and a winch umbilical cable deployment and retrieval subsystem, and establishes a state space prediction model. Based on the roll angle of underwater robots Pitch angle Bow angle Longitudinal velocity lateral velocity Vertical velocity Roll rate Pitch angular velocity and yaw rate Construct the state vector of the underwater robot for: ； In the formula, for The position and attitude of the underwater robot at all times. for The linear velocity and angular velocity of the underwater robot at all times; The control input is the thruster thrust. The state vector of the winch umbilical cable deployment and retrieval subsystem is defined as the position and velocity of each node of the cable in the geodetic coordinate system, and the control input. Defined as the difference between the current velocity and the previous velocity of each node in the cable. Within each sampling period, the current velocity is... Using the position and velocity of the umbilical cable node as initial values, in the prediction time domain Inside, the state space prediction model is used to predict the state information of the underwater robot motion subsystem and the winch umbilical cable deployment and retrieval subsystem; A quadratic programming objective function with multiple objectives was constructed for the underwater robot and the umbilical cable coupling system. : ； ； ； ； In the formula, , and To replace the variable, This represents the desired reference state for the underwater robot. This is the weighting matrix of state errors. For the control input of the underwater robot, The cost of inputting information to underwater robots To control the weighting matrix of the input cost, For the tension of the umbilical cable, for The cost, Let be the weighted matrix of tension costs. The weighting coefficient for the relaxation penalty term. For the beginning of the umbilical cable, The end of the umbilical cable, The relaxation coefficient, For cable length, Input the cost weighting matrix for the cable node. The cost of input to the umbilical cable node.

8. The underwater robot and umbilical cable system collaborative control simulation system according to claim 7, characterized in that, Set constraints for the underwater robot and umbilical cable, and impose constraints on the underwater robot's build state: ； In the formula, Let be the lower bound of the underwater robot's state vector. This represents the upper bound of the underwater robot's state vector; Construct control constraints for the control inputs: ； In the formula, This represents the lower limit of the control input for the underwater robot. This represents the upper limit of the control input for the underwater robot. Set umbilical cable constraints: ； ； ； In the formula, For the first The position of the bow node that is constantly connected to the mother ship. For the first The location of the mothership at all times. For the first The speed of the bow node that is constantly connected to the mother ship. For the first The speed of the mothership at all times For node labels, For the mother ship's identification, For the node connected to the mother ship, For the first The location of the underwater robot at all times. This represents the position of the end effector point in the underwater robot's body coordinate system. This is the transformation matrix from the underwater robot's body coordinate system to the geodetic coordinate system. For the first The position of the end node constantly connected to the underwater robot. For the first The speed of the end node constantly connected to the underwater robot. The lower limit input for underwater robots and umbilical cable systems. Upper limit for inputs to underwater robots and umbilical cable systems; The coupled dynamic equations and the umbilical cable constraints together constitute the dynamic constraints.

9. The underwater robot and umbilical cable system collaborative control simulation system according to claim 8, characterized in that, At each sampling time, a constrained quadratic programming optimization problem is constructed, which includes the objective function, dynamic constraints, control constraints, and state constraints. The smooth Newton method is used to solve the problem and obtain the optimal control increment sequence in the prediction time domain. Model predictive control is performed at each sampling time using the smoothed Newton method, including the extraction of the control time domain. The optimal control increment sequence of the first step is converted into the thruster voltage signal and winch frequency. The command is sent to the simulation execution layer. When the next sampling time arrives, the underwater robot and umbilical cable system use the measured state for feedback correction, re-initialize the prediction model and solve the constrained quadratic programming optimization problem in a rolling manner until the processing of all sampling time moments is completed. The underwater robot motion subsystem and the winch umbilical cable deployment and retrieval subsystem share predicted state information and alternately iteratively solve a constrained quadratic programming optimization problem.

10. The underwater robot and umbilical cable system collaborative control simulation system according to claim 9, characterized in that, The human-computer interaction control module includes a marine parameter configuration interface, an underwater robot and umbilical cable system simulation test interface, and a data playback interface; The data acquisition and storage module records the underwater robot's speed, position data, and umbilical cable end tension data, generating replayable logs and reports.

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