Track tracking method, system, device and medium based on improved bee swarm algorithm

Abstract

The present invention relates to the field of unmanned boat control and provides a track tracking method, system, device and medium based on an improved bee swarm algorithm, including determining the position information of the unmanned boat through a navigation device; obtaining an expression for a reference heading angle through a predetermined path, calculating the azimuth error and the foresight distance, calculating the virtual input direction error, and generating an initial rudder angle; obtaining interference parameters, establishing a model attitude controller and predicting the motion attitude of the unmanned boat to obtain a path prediction error and construct an optimization function; calculating a dynamic weight coefficient and an adaptive scaling factor, migrating the initial solution to obtain an initial migration solution, selecting the current optimal solution, migrating the initial migration solution to obtain a target migration solution, obtaining the optimal solution of the optimization function to obtain a target rudder angle; and controlling the unmanned boat to complete the control of the unmanned boat. The present invention can enable the unmanned boat to achieve relatively accurate track tracking.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat control, and in particular to a track tracking method, system, equipment and medium based on an improved bee swarm algorithm. Background Art

[0002] Unmanned surface vehicles are a general-purpose intelligent surface mission platform that is increasingly being used. They have the characteristics of small size, low cost, maneuverability, high speed, intelligence, small radar reflection area, and no casualties. Surface unmanned vehicles play an increasingly important role in the fields of marine environment measurement, seabed topography measurement, marine resource development, etc., and have broad application prospects. Path tracking control is one of the key technologies for surface unmanned vehicles to achieve precise navigation and perform some complex line-following measurement and mapping tasks. High-precision path tracking is the key to ensuring accurate marine topography measurement and mapping. Path tracking is a key and difficult problem that needs to be solved in the application of surface unmanned vehicles in the field of marine measurement and mapping. Therefore, the study of path tracking control of surface unmanned vehicles is of great significance to improving the automation and intelligence level of surface unmanned vehicles and promoting the marketization of surface unmanned vehicles.

[0003] When sailing in complex sea conditions, surface unmanned vehicles will be disturbed by marine environmental forces such as wind, waves and currents, and the model parameters of the surface unmanned vehicles will show uncertainty. Therefore, a control method with strong robustness is needed to ensure the stability of the surface unmanned vehicle control system and the accuracy of the surface unmanned vehicle movement.

[0004] A Chinese invention patent with publication number CN115686002A, titled "A Path Tracking Control Method for Unmanned Surface Vehicles in Complex Sea Areas," provides an unmanned vehicle track tracking method based on a swarm algorithm. However, the swarm algorithm contains a large number of inferior solutions, which can lead to the failure of the optimal solution to achieve the optimal effect, thus causing inaccurate track tracking. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a track tracking method, system, device and medium based on an improved bee swarm algorithm to achieve accurate tracking of the track of an unmanned boat.

[0006] The present invention provides a track tracking method based on an improved bee swarm algorithm, comprising:

[0007] S1: Install the navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0008] S2: Determine a predetermined path, obtain an expression for the reference heading angle using the predetermined path, calculate the azimuth error using the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and calculate the direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error;

[0009] S3: Obtain the interference parameters, establish the model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct the optimization function based on the path prediction error;

[0010] S4: Calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle;

[0011] S5: Control the unmanned boat according to the target rudder angle, and iterate the target rudder angle to complete the control of the unmanned boat.

[0012] According to the track tracking method based on the improved bee swarm algorithm provided by the present invention, in step S1, a navigation device including an AIS device, a satellite navigation device, and a combined navigation device is determined, the navigation device is installed on the unmanned boat, and the position information of the unmanned boat is determined by the navigation device.

[0013] According to the track tracking method based on the improved bee swarm algorithm provided by the present invention, step S2 specifically includes:

[0014] S21: Determine a predetermined path of the unmanned boat, obtain a path tangent angle of the predetermined path, and obtain an expression for the reference heading angle based on the path tangent angle;

[0015] S22: determining path point coordinates according to the predetermined path, and calculating the orientation error using the path point coordinates and the position information;

[0016] S23: Determine a minimum foresight distance. Calculate the foresight distance based on the azimuth error and the minimum foresight distance, and calculate the virtual input based on the foresight distance.

[0017] S24: Calculate the reference heading angle through the expression of the reference heading angle, calculate the direction error through the reference heading angle and the virtual input, and generate the initial rudder angle according to the direction error.

[0018] According to the track tracking method based on the improved bee swarm algorithm provided by the present invention, step S3 specifically includes:

[0019] S31: Obtain interference parameters including wind interference parameters, wave interference parameters, viscosity interference parameters, and propeller force interference parameters, and establish a model attitude controller using the interference parameters and the initial rudder angle;

[0020] S32: Discretize the differential equation in the model attitude controller, thereby predicting the motion attitude of the unmanned boat through the model attitude controller to obtain a path prediction error, and construct an optimization function through the path prediction error.

[0021] According to the track tracking method based on the improved bee swarm algorithm provided by the present invention, in step S4, the maximum number of iterations is determined, the dynamic weight coefficient and the adaptive scaling factor are calculated according to the maximum number of iterations, and the initial solution is migrated using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution.

[0022] According to the track tracking method based on the improved swarm algorithm provided by the present invention, in step S4, the current optimal solution is selected, the adaptive coefficient is calculated, the initial migration solution is migrated using the current optimal solution and the adaptive coefficient to obtain a target migration solution, and the optimal solution of the optimization function is obtained using the target migration solution to obtain the target rudder angle.

[0023] According to the track tracking method based on the improved bee swarm algorithm provided by the present invention, in step S5, the unmanned boat is controlled according to the target rudder angle, and the target rudder angle replaces the initial rudder angle in step S3, thereby iterating the target rudder angle to complete the control of the unmanned boat.

[0024] The present invention also provides a track tracking system based on an improved bee swarm algorithm, comprising:

[0025] Navigation module: used to install navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0026] Initial rudder angle module: used to determine the predetermined path, obtain the expression of the reference heading angle through the predetermined path, calculate the azimuth error through the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and use the virtual input and the expression of the reference heading angle to calculate the direction error, and generate the initial rudder angle based on the direction error;

[0027] Optimization function module: used to obtain interference parameters, establish a model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct an optimization function based on the path prediction error;

[0028] Artificial bee colony algorithm module: used to calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain the initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain the target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain the target rudder angle;

[0029] Rudder angle iteration module: used to control the unmanned boat according to the target rudder angle and iterate the target rudder angle to complete the control of the unmanned boat.

[0030] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the track tracking method based on the improved swarm algorithm as described above are implemented.

[0031] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the track tracking method based on the improved bee swarm algorithm as described above are implemented.

[0032] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0033] The track tracking method, system, device and medium based on the improved artificial bee swarm algorithm provided by the present invention calculate the direction error with higher precision by calculating the dynamic foresight distance, thereby providing a basis for the subsequent generation of a higher precision initial rudder angle; then, the improved artificial bee swarm algorithm is used to migrate the initial solution twice using the current optimal solution, so that the artificial bee swarm algorithm can obtain an optimal solution that is more in line with the global optimum, thereby obtaining the target rudder angle for controlling the unmanned boat.

[0034] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 It is a flow chart of the track tracking method based on the improved bee swarm algorithm provided by the present invention.

[0037] Figure 2It is a schematic diagram of the track tracking results of the track tracking experiment based on the improved bee swarm algorithm provided by the present invention.

[0038] Figure 3 It is a schematic diagram of the error control results of the track tracking experiment based on the improved bee swarm algorithm provided by the present invention.

[0039] Figure 4 It is a structural diagram of the track tracking system based on the improved bee swarm algorithm provided by the present invention.

[0040] Figure 5 It is a structural schematic diagram of a track tracking device based on an improved bee swarm algorithm provided by the present invention.

[0041] Reference numerals:

[0042] 100, navigation module; 200, initial rudder angle module; 300, optimization function module; 400, artificial bee colony algorithm module; 500, rudder angle iteration module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0044] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0045] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0046] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0047] The following combination Figures 1 to 5 Describe the specific embodiments of the present invention, Figure 1 The present invention provides a flow chart of a track tracking method based on an improved bee swarm algorithm. The present invention provides a track tracking method based on an improved bee swarm algorithm, which specifically includes:

[0048] S1: Install the navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0049] Furthermore, the purpose of this stage is to arrange navigation equipment and determine the location information of the unmanned boat through the navigation equipment. Specifically, in step S1, the navigation equipment including AIS equipment, satellite navigation equipment, and combined navigation equipment is determined, and the navigation equipment is installed on the unmanned boat, and the location information of the unmanned boat is determined through the navigation equipment.

[0050] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0051] First, navigation equipment must be selected and installed on the unmanned vehicle. Specifically, this includes satellite navigation equipment that receives satellite navigation signals; integrated navigation equipment that uses multiple data sources, including satellite navigation signals and inertial navigation equipment, for navigation; and AIS (Automatic Identification System) equipment that receives positioning information from carriers and ground-based equipment. Once these navigation devices are installed on the unmanned vehicle, the vessel's location—its specific coordinates—can be determined.

[0052] S2: Determine a predetermined path, obtain an expression for the reference heading angle using the predetermined path, calculate the azimuth error using the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and calculate the direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error;

[0053] Furthermore, the purpose of this stage is to calculate the forward distance and the virtual input, thereby calculating the direction error and generating the initial rudder angle. Specifically, step S2 includes:

[0054] S21: Determine a predetermined path of the unmanned boat, obtain a path tangent angle of the predetermined path, and obtain an expression for the reference heading angle based on the path tangent angle;

[0055] S22: determining path point coordinates according to the predetermined path, and calculating the orientation error using the path point coordinates and the position information;

[0056] S23: Determine a minimum foresight distance. Calculate the foresight distance based on the azimuth error and the minimum foresight distance, and calculate the virtual input based on the foresight distance.

[0057] S24: Calculate the reference heading angle through the expression of the reference heading angle, calculate the direction error through the reference heading angle and the virtual input, and generate the initial rudder angle according to the direction error.

[0058] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0059] First, it is necessary to plan a predetermined path for the unmanned boat according to its mission objectives, and then determine its path tangent angle according to the nth path point on the predetermined path. , the tangent angle is the arc tangent value of the slope of the predetermined path at the path point, and then the expression of the reference heading angle is obtained, and the reference heading angle is :

[0060]

[0061] in, is the forward distance, β is the sideslip angle of the unmanned boat measured by the inertial navigation equipment on the unmanned boat, is the lateral error of the unmanned boat. From the expression of the reference heading angle, it can be seen that the lateral error and the foresight distance are unknown. Then the path point coordinates of the nth path point on the predetermined path are determined, and the azimuth error is calculated using the path point coordinates and position information:

[0062]

[0063] in, is the longitudinal error of the unmanned boat in the azimuth error, is the x-axis coordinate of the position information of the unmanned boat, is the y-axis coordinate of the position information of the unmanned boat, is the x-axis coordinate of the nth path point, is the y-axis coordinate of the nth path point. Here, the x-axis is the horizontal axis and the y-axis is the vertical axis.

[0064] Next, in traditional technologies, the forward sight distance is fixed. Generally, the forward sight distance is 2 to 3 times the length of the unmanned boat based on experience. However, in a cluttered ocean environment, a fixed forward sight distance is not conducive to high-precision path tracking control of the surface unmanned boat. Therefore, the minimum forward sight distance is determined. , where the minimum foresight distance is the length of the unmanned boat, and then the foresight distance is calculated based on the azimuth error and the minimum foresight distance:

[0065]

[0066] in, is the forward sight distance calculation coefficient, and is obtained based on the hydrodynamic test results of the unmanned boat, and U is the speed of the unmanned boat. In this way, the virtual input can be calculated by the forward sight distance :

[0067]

[0068] Among them, sign() is the sign function, is the first design parameter determined empirically, is the second design parameter determined empirically, is the third design parameter determined empirically.

[0069] The reference heading angle is then calculated using the expression for the reference heading angle, and the direction error is calculated using the reference heading angle and the virtual input:

[0070]

[0071] in, is the longitudinal direction error of the unmanned boat in the direction error, is the lateral direction error of the unmanned boat in the direction error, is the derivative of the path tangent angle, is the kinematic uncertainty parameter, given by and It is obtained that the servo can be activated according to the direction error to form an initial rudder angle for correcting the direction error.

[0072] S3: Obtain the interference parameters, establish the model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct the optimization function based on the path prediction error;

[0073] Furthermore, the purpose of this stage is to establish a model attitude controller through interference parameters and initial rudder angles and predict the motion attitude of the unmanned boat, thereby constructing an optimization function. Specifically, step S3 includes:

[0074] S31: Obtain interference parameters including wind interference parameters, wave interference parameters, viscosity interference parameters, and propeller force interference parameters, and establish a model attitude controller using the interference parameters and the initial rudder angle;

[0075] S32: Discretize the differential equation in the model attitude controller, thereby predicting the motion attitude of the unmanned boat through the model attitude controller to obtain a path prediction error, and construct an optimization function through the path prediction error.

[0076] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0077] First, the interference parameters including wind interference parameters, wave interference parameters, viscosity interference parameters and propeller force interference parameters are obtained based on experience and the results of unmanned boat hydrodynamic tests. Then, a model attitude controller for the unmanned boat navigation can be established based on the interference parameters and the initial rudder angle. The model attitude controller is composed of multiple differential equations, which can express the longitudinal ground velocity component of the unmanned boat, the lateral ground velocity component of the unmanned boat, the rotation angle of the unmanned boat, and the current rudder angle of the unmanned boat.

[0078] The differential equations in the model attitude controller are then discretized, allowing the model attitude controller to predict the UAV's motion at multiple future moments, thereby obtaining the UAV's position at those future moments. The error in the UAV's position relative to the planned path is then calculated to obtain the path prediction error. In the UAV track tracking process of this embodiment, the lateral displacement is set to have the greatest impact on the track tracking accuracy. Therefore, the path prediction error is calculated for multiple future moments based on the lateral displacement. This allows the optimization function to be constructed using the path prediction error:

[0079]

[0080] in, is the number of moments to be predicted, is the target rudder angle of the unmanned boat, is the minimum value of the rudder angle of the unmanned boat, is the maximum value of the rudder angle of the unmanned boat, To optimize the function, is the path prediction error at the k+jth moment, Q is the weight matrix set according to experience, T represents transpose, j is used to represent the jth moment in the future, and min represents the minimum value.

[0081] S4: Calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle;

[0082] Furthermore, the purpose of this stage is to migrate the initial solution to obtain an initial migrated solution, and then further migrate the initial migrated solution to obtain a target migrated solution, thereby obtaining the optimal solution and obtaining the target rudder angle. Specifically, in step S4, a maximum number of iterations is determined, the dynamic weight coefficient and the adaptive scaling factor are calculated based on the maximum number of iterations, and the initial solution is migrated using the dynamic weight coefficient and the adaptive scaling factor to obtain the initial migrated solution.

[0083] In step S4, the current optimal solution is selected, the adaptive coefficient is calculated, the initial migration solution is migrated using the current optimal solution and the adaptive coefficient to obtain a target migration solution, the optimal solution of the optimization function is obtained using the target migration solution, and the target rudder angle is obtained.

[0084] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0085] First, we need to build an artificial bee colony algorithm. The artificial bee colony algorithm is an optimization method proposed by imitating the behavior of bees. It is a specific application of cluster intelligence. Its main feature is that it does not require understanding of specific information about the problem, but only requires comparison of advantages and disadvantages. Through the local optimization behavior of each artificial bee individual, the global optimal value will eventually emerge in the group, with a faster convergence speed. Then determine the maximum number of iterations of the artificial bee colony algorithm. , calculate the dynamic weight coefficient of the tth iteration through the maximum number of iterations and adaptive scaling factors :

[0086]

[0087]

[0088] in, is the minimum adaptive scaling factor determined empirically, is the maximum value of the adaptive scaling factor determined empirically, so that the initial solution can be migrated through the dynamic weight coefficient and the adaptive scaling factor to obtain the initial migration solution of the tth iteration in the artificial bee colony algorithm :

[0089]

[0090] in, is the Lth solution of the optimization function randomly selected from multiple solutions in the artificial bee colony algorithm, is the Hth solution of the optimization function randomly selected from multiple solutions in the artificial bee colony algorithm, A randomly generated number greater than 0, is an initial solution of the optimization function, which can be a poor solution or a newly added solution in the artificial bee colony algorithm.

[0091] Then select the current optimal solution. Here, if it is the first time to solve the target rudder angle, the global optimal initial solution is selected from the randomly generated solutions as the current optimal solution. Otherwise, change the summation upper limit in the optimization function to a value lower than The value of is converted into a new optimization function and the traditional artificial bee colony algorithm is used to find the optimal solution for this new optimization function, which is used as the current optimal solution. The value of the summation upper limit depends on the accuracy requirements and hardware performance on the one hand, and on the curvature of the unmanned boat's current position on the predetermined path on the other hand. For example, in this embodiment, the unmanned boat's current position on the predetermined path is a straight line, that is, when the curvature is 0, the summation upper limit selected is 6, and the greater the curvature, the greater the summation upper limit. Then calculate the first adaptive coefficient and the second adaptive coefficient Adaptive coefficient of:

[0092]

[0093] Then, the initial migration solution is migrated by the current optimal solution and the adaptive coefficient to obtain the target migration solution at the tth iteration of the artificial bee colony algorithm. :

[0094]

[0095] in, is a Gaussian perturbation term that the system can adaptively adjust. The target migration solution obtained at the tth iteration is used as the solution in the artificial bee colony algorithm and added to the artificial bee colony algorithm. This can make the inferior solution or the newly added solution in the artificial bee colony algorithm iteration process migrate to the high-quality solution, thereby avoiding the inferior solution from causing significant interference to the artificial bee colony algorithm, improving the accuracy of the artificial bee colony algorithm, and iterating the artificial bee colony algorithm to obtain the optimal solution of the optimization function to obtain the target rudder angle.

[0096] S5: Control the unmanned boat according to the target rudder angle, and iterate the target rudder angle to complete the control of the unmanned boat.

[0097] Furthermore, the purpose of this stage is to control the unmanned boat and iterate the target rudder angle. Specifically, in step S5, the unmanned boat is controlled according to the target rudder angle, and the target rudder angle replaces the initial rudder angle in step S3, thereby iterating the target rudder angle to complete the control of the unmanned boat.

[0098] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0099] After obtaining the target rudder angle, the UAV's steering gear can be controlled according to the target rudder angle to ensure stable tracking of the UAV. To maintain stable tracking, it is necessary to continuously replace the initial rudder angle in step S3 with the target rudder angle and continuously predict the UAV's motion posture to obtain a new target rudder angle. The target rudder angle is then iterated to complete the control of the UAV, ensuring stable tracking of the UAV.

[0100] The present invention verifies the effectiveness of the track tracking method based on the improved bee swarm algorithm. The existing ocean measurement unmanned boat is used as the simulation object and simulation is carried out in MATLAB. The ocean measurement unmanned boat has a draft of 0.4m, a full load draft of 4.1t, a length of 7.5m, a width of 2.8m, a block coefficient of 0.07, and a propeller diameter of 0.8m. Figure 2 This is a schematic diagram of the track tracking results of the track tracking experiment. Figure 3 This is a schematic diagram of the error control results from a track tracking experiment, illustrating the tracking error during the track tracking process. The comparison solution is the solution provided in Chinese invention patent publication number CN115686002A, titled "A Path Tracking Control Method for Unmanned Surface Vehicles in Complex Seas." As can be seen, the solution provided by the present invention achieves better track tracking results and higher accuracy.

[0101] The track tracking device based on the improved bee swarm algorithm provided by the present invention is described below. The track tracking device based on the improved bee swarm algorithm described below and the track tracking method based on the improved bee swarm algorithm described above can refer to each other.

[0102] Figure 4 The structural diagram of the track tracking system based on the improved bee swarm algorithm is shown as follows: Figure 4 As shown, the method for executing the track tracking method based on the improved bee swarm algorithm as described above includes:

[0103] Navigation module 100: used to install navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0104] Initial rudder angle module 200: used to determine a predetermined path, obtain an expression for a reference heading angle based on the predetermined path, calculate an azimuth error based on position information, calculate a foresight distance based on the azimuth error, calculate a virtual input based on the foresight distance, calculate a direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error;

[0105] Optimization function module 300: used to obtain interference parameters, establish a model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct an optimization function based on the path prediction error;

[0106] Artificial bee colony algorithm module 400: used to calculate a dynamic weight coefficient and an adaptive scaling factor, migrate an initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select a current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle;

[0107] Rudder angle iteration module 500: used to control the unmanned boat according to the target rudder angle and iterate the target rudder angle to complete the control of the unmanned boat.

[0108] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the track tracking method based on the improved bee swarm algorithm, which includes:

[0109] S1: Install the navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0110] S2: Determine a predetermined path, obtain an expression for the reference heading angle using the predetermined path, calculate the azimuth error using the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and calculate the direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error;

[0111] S3: Obtain the interference parameters, establish the model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct the optimization function based on the path prediction error;

[0112] S4: Calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle;

[0113] S5: Control the unmanned boat according to the target rudder angle, and iterate the target rudder angle to complete the control of the unmanned boat.

[0114] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0115] On the other hand, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer, the computer is capable of performing the track tracking method based on the improved bee swarm algorithm provided by the above methods, the method comprising:

[0116] S1: Install the navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0117] S2: Determine a predetermined path, obtain an expression for the reference heading angle using the predetermined path, calculate the azimuth error using the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and calculate the direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error;

[0118] S3: Obtain the interference parameters, establish the model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct the optimization function based on the path prediction error;

[0119] S4: Calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle;

[0120] S5: Control the unmanned boat according to the target rudder angle, and iterate the target rudder angle to complete the control of the unmanned boat.

[0121] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program is implemented to perform the above-mentioned track tracking method based on the improved bee swarm algorithm, the method comprising:

[0122] S1: Install the navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment;

[0123] S2: Determine a predetermined path, obtain an expression for the reference heading angle using the predetermined path, calculate the azimuth error using the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and calculate the direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error;

[0124] S3: Obtain the interference parameters, establish the model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct the optimization function based on the path prediction error;

[0125] S4: Calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle;

[0126] S5: Control the unmanned boat according to the target rudder angle, and iterate the target rudder angle to complete the control of the unmanned boat.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0128] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. The track tracking method based on the improved bee swarm algorithm is characterized by: include: S1: Install the navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment; S2: Determine a predetermined path, obtain an expression for the reference heading angle using the predetermined path, calculate the azimuth error using the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and calculate the direction error using the virtual input and the expression for the reference heading angle, and generate an initial rudder angle based on the direction error; S3: Obtain the interference parameters, establish the model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct the optimization function based on the path prediction error; S4: Calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain a target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain a target rudder angle; S5: Control the unmanned boat according to the target rudder angle, and iterate the target rudder angle to complete the control of the unmanned boat.

2. The track tracking method based on the improved bee swarm algorithm according to claim 1, characterized in that: In step S1, a navigation device including an AIS device, a satellite navigation device, and a combined navigation device is determined, the navigation device is installed on the unmanned boat, and the position information of the unmanned boat is determined by the navigation device.

3. The track tracking method based on the improved bee swarm algorithm according to claim 1, characterized in that: Step S2 specifically includes: S21: Determine a predetermined path of the unmanned boat, obtain a path tangent angle of the predetermined path, and obtain an expression for the reference heading angle based on the path tangent angle; S22: determining path point coordinates according to the predetermined path, and calculating the orientation error using the path point coordinates and the position information; S23: Determine a minimum foresight distance, calculate the foresight distance according to the azimuth error and the minimum foresight distance, and calculate the virtual input according to the foresight distance; S24: Calculate the reference heading angle through the expression of the reference heading angle, calculate the direction error through the reference heading angle and the virtual input, and generate the initial rudder angle according to the direction error.

4. The track tracking method based on the improved bee swarm algorithm according to claim 1, characterized in that: Step S3 specifically includes: S31: Obtain interference parameters including wind interference parameters, wave interference parameters, viscosity interference parameters, and propeller force interference parameters, and establish a model attitude controller using the interference parameters and the initial rudder angle; S32: Discretize the differential equation in the model attitude controller, thereby predicting the motion attitude of the unmanned boat through the model attitude controller to obtain a path prediction error, and construct an optimization function through the path prediction error.

5. The track tracking method based on the improved bee swarm algorithm according to claim 1, characterized in that: In step S4, the maximum number of iterations is determined, the dynamic weight coefficient and the adaptive scaling factor are calculated according to the maximum number of iterations, and the initial solution is migrated using the dynamic weight coefficient and the adaptive scaling factor to obtain an initial migration solution.

6. The track tracking method based on the improved bee swarm algorithm according to claim 1, characterized in that: In step S4, the current optimal solution is selected, the adaptive coefficient is calculated, the initial migration solution is migrated using the current optimal solution and the adaptive coefficient to obtain a target migration solution, the optimal solution of the optimization function is obtained using the target migration solution, and the target rudder angle is obtained.

7. The track tracking method based on the improved bee swarm algorithm according to claim 1, characterized in that: In step S5, the unmanned boat is controlled according to the target rudder angle, and the initial rudder angle in step S3 is replaced by the target rudder angle, thereby iterating the target rudder angle to complete the control of the unmanned boat.

8. A track tracking system based on an improved bee swarm algorithm, used to execute the track tracking method based on an improved bee swarm algorithm according to any one of claims 1 to 7, characterized in that: include: Navigation module: used to install navigation equipment on the unmanned boat and determine the location information of the unmanned boat through the navigation equipment; Initial rudder angle module: used to determine the predetermined path, obtain the expression of the reference heading angle through the predetermined path, calculate the azimuth error through the position information, calculate the foresight distance based on the azimuth error, calculate the virtual input based on the foresight distance, and use the virtual input and the expression of the reference heading angle to calculate the direction error, and generate the initial rudder angle based on the direction error; Optimization function module: used to obtain interference parameters, establish a model attitude controller based on the interference parameters and the initial rudder angle, predict the motion attitude of the unmanned boat, obtain the path prediction error, and construct an optimization function based on the path prediction error; Artificial bee colony algorithm module: used to calculate the dynamic weight coefficient and the adaptive scaling factor, migrate the initial solution using the dynamic weight coefficient and the adaptive scaling factor to obtain the initial migration solution, select the current optimal solution, migrate the initial migration solution using the current optimal solution to obtain the target migration solution, and complete the optimal solution of the optimization function using the target migration solution to obtain the target rudder angle; Rudder angle iteration module: used to control the unmanned boat according to the target rudder angle and iterate the target rudder angle to complete the control of the unmanned boat.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the track tracking method based on the improved bee swarm algorithm as described in any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the track tracking method based on the improved bee swarm algorithm according to any one of claims 1 to 7 are implemented.

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