Anti-interference autonomous berthing regulation method and system for unmanned ship

Abstract

The application relates to the technical field of unmanned ships, and relates to an anti-interference autonomous berthing regulation method and system for an unmanned ship, which comprises the following steps: obtaining a berthing three-dimensional coordinate system based on the unmanned ship and a target berthing area; confirming a plurality of environment sensing nodes in the berthing three-dimensional coordinate system according to a sensing distance; obtaining target environment data based on a target sensor set and the environment sensing nodes; obtaining node coordinates of the environment sensing nodes based on a situation awareness instruction, performing an identification operation on the node coordinates by using the target environment data to obtain situation awareness coordinates, and collecting the situation awareness coordinates to obtain a situation awareness coordinate set; obtaining a target monitoring area based on the situation awareness coordinate set; regulating the unmanned ship based on an anti-interference regulation instruction and the target monitoring area to obtain a target berthing unmanned ship; and realizing anti-interference autonomous berthing regulation based on the target berthing unmanned ship. The application can realize anti-interference regulation of berthing of the unmanned ship.

Description

Anti-interference autonomous berthing control method and system for unmanned surface vessels Technical Field

[0001] This invention relates to the field of unmanned vessel technology, and in particular to an anti-interference autonomous berthing control method and system for unmanned vessels. Background Technology

[0002] With the rapid development of autonomous driving technology and the marine economy, unmanned surface vessels (USVs), as intelligent marine equipment, have been widely used in fields such as environmental monitoring, hydrographic surveying, security patrols, and port transportation. Among these, autonomous berthing is a crucial terminal link in the USV's mission execution chain, and its performance directly determines the safety and reliability of operations.

[0003] Currently, autonomous berthing of unmanned vessels mainly relies on high-precision GNSS (Global Navigation Satellite System) for path tracking, or on automated berthing through preset waypoints and headings.

[0004] Although the above methods can achieve basic berthing functions, they do not establish a dynamic compensation relationship between environmental interference and control commands, nor do they achieve intelligent phased management of the berthing process. As a result, when facing complex environments, the control system often maintains its course through frequent and drastic steering and thrust adjustments. This not only significantly increases the system's energy consumption but also exacerbates equipment wear. Therefore, how to achieve anti-interference control for unmanned vessel berthing has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides an anti-interference autonomous berthing control method and a computer-readable storage medium for unmanned vessels, the main purpose of which is to achieve anti-interference control of unmanned vessel berthing.

[0006] To achieve the above objectives, the present invention provides an anti-interference autonomous berthing control method for unmanned vessels, comprising:

[0007] The autonomous control system receives autonomous control commands and confirms the autonomous control environment based on the autonomous control commands. The autonomous control environment includes an autonomous control system and an unmanned vessel to be docked and controlled. The autonomous control system includes an environmental perception unit, a situational perception unit, and an anti-interference control unit.

[0008] A three-dimensional coordinate system for docking is obtained based on the unmanned vessel and the preset target docking area;

[0009] Confirm receipt of environmental sensing instructions from the environmental sensing unit, obtain the sensing distance for setting the environmental sensing nodes based on the environmental sensing instructions, and identify multiple environmental sensing nodes in the berthing three-dimensional coordinate system according to the sensing distance.

[0010] Perform the following operation on each of the multiple environment sensing nodes:

[0011] The target sensor set is identified based on the environmental perception command, and the target environment data is obtained based on the target sensor set and the environmental perception node. The target sensor set includes multiple target sensors, which are a visual sensor, a lidar and a millimeter-wave radar, respectively.

[0012] Receive situational awareness instructions from the situational awareness unit, obtain the node coordinates of the environmental awareness node based on the situational awareness instructions, perform an identification operation on the node coordinates using target environmental data to obtain situational awareness coordinates, and summarize the situational awareness coordinates to obtain a situational awareness coordinate set.

[0013] The target monitoring area is obtained based on the situational awareness coordinate set, wherein the target monitoring area is a safe area, an early warning area, or a dangerous area;

[0014] The system receives anti-interference control instructions from the anti-interference control unit, and controls the unmanned vessel based on the anti-interference control instructions and the target monitoring area to obtain the target berthing unmanned vessel.

[0015] Based on the target berthing, the unmanned vessel can achieve anti-interference autonomous berthing control.

[0016] Optionally, obtaining the berthing three-dimensional coordinate system based on the unmanned vessel and the preset target berthing area includes:

[0017] The parking center point is determined based on the target parking area;

[0018] The center point of the unmanned vessel was determined based on the aforementioned unmanned vessel.

[0019] The distance value is obtained based on the center point and docking center point of the unmanned vessel;

[0020] Compare the distance value with the preset distance threshold. If the distance value is less than or equal to the distance threshold, then establish a three-dimensional spatial coordinate system with the center point of the unmanned vessel as the origin to obtain the berthing three-dimensional coordinate system. In the berthing three-dimensional coordinate system, the x-axis direction is the same as the movement direction of the unmanned vessel, and the z-axis direction is perpendicular to the sea surface where the unmanned vessel is located.

[0021] If the distance value is greater than the distance threshold, the difference between the distance value and the distance threshold is calculated to obtain the distance control value, and the current unmanned vessel's sailing speed is obtained.

[0022] Based on the distance control value, the sailing speed is controlled using a pre-built fuzzy control algorithm to obtain an updated sailing speed. The unmanned vessel is then optimized using the updated sailing speed to obtain an updated unmanned vessel. The updated unmanned vessel is then used as the unmanned vessel, and the process returns to the step of confirming the center point of the unmanned vessel based on the updated unmanned vessel, until the berthing three-dimensional coordinate system is obtained.

[0023] Optionally, acquiring target environment data based on the target sensor set and environmental sensing nodes includes:

[0024] The target sampling frequency is obtained based on the target sensor set;

[0025] The target sensor set is optimized using the target sampling frequency to obtain an optimized sensor set, wherein the optimized sensor set includes multiple optimized sensors, and the optimized sensors are an optimized visual sensor, an optimized lidar, and an optimized millimeter-wave radar.

[0026] For each optimized sensor in the optimized sensor set, perform the following operation:

[0027] Based on the preset monitoring start time, the environmental sensing nodes are monitored using optimized sensors to obtain the initial monitoring dataset;

[0028] The initial monitoring datasets are summarized to obtain an initial monitoring dataset group, which includes multiple initial monitoring datasets, namely an environmental image set, an initial point cloud set, and an initial distance set, and the initial point cloud set includes multiple spatial coordinate points;

[0029] The environmental image set is preprocessed to obtain the target environmental image set;

[0030] The initial point set is denoised to obtain the target point set;

[0031] The target environment data is obtained by summarizing the target environment image set, target point cloud set, and initial distance set.

[0032] Optionally, obtaining the target sampling frequency based on the target sensor set includes:

[0033] For each target sensor in the target sensor set, perform the following operation:

[0034] Obtain the sampling frequency range of the target sensor, whereby the sampling frequency range includes the minimum sampling frequency and the maximum sampling frequency;

[0035] By summing the minimum and maximum sampling frequencies respectively, we obtain the minimum sampling frequency set and the maximum sampling frequency set;

[0036] The lower limit sampling frequency is obtained based on the set of minimum sampling frequencies, wherein the lower limit sampling frequency is the minimum sampling frequency that is the largest in the set of minimum sampling frequencies;

[0037] The upper limit sampling frequency is obtained based on the set of maximum sampling frequencies, wherein the upper limit sampling frequency is the minimum maximum sampling frequency in the set of maximum sampling frequencies;

[0038] Compare the lower sampling frequency and the upper sampling frequency. If the lower sampling frequency is less than or equal to the upper sampling frequency, then obtain the target sampling frequency based on the lower sampling frequency and the upper sampling frequency, wherein the target sampling frequency is the average of the lower sampling frequency and the upper sampling frequency.

[0039] If the lower sampling frequency is greater than the upper sampling frequency, the target sensor corresponding to the lower sampling frequency is replaced to obtain an updated target sensor. The updated target sensor is then used as the target sensor, and the process returns to the step of obtaining the sampling frequency range of the target sensor until the target sampling frequency is obtained.

[0040] Optionally, the preprocessing of the environmental image set to obtain the target environmental image set includes:

[0041] For each environment image in the environment image set, perform the following operations:

[0042] The environmental image is converted to grayscale to obtain a grayscale image. The grayscale image is then filtered using a pre-built filter to obtain an initial image.

[0043] A pixel set is obtained based on the initial image, wherein the pixel set includes multiple pixels;

[0044] A grayscale value set is obtained based on the pixel set, wherein the grayscale value set includes multiple grayscale values, and the pixels in the pixel set correspond one-to-one with the grayscale values.

[0045] Perform the following operation on each pixel in the pixel set:

[0046] A local neighborhood is obtained based on the pixel, wherein the local neighborhood is a square region centered on the pixel with a side length of a preset size;

[0047] The local grayscale mean is obtained based on the local neighborhood, wherein the local grayscale mean is the average grayscale value of each pixel in the local neighborhood.

[0048] An initial grayscale value is obtained based on the pixel and grayscale value set, wherein the initial grayscale value is the grayscale value corresponding to the pixel.

[0049] Compare the initial gray value with the local gray average value. If the initial gray value is greater than the local gray average value, then the initial gray value is assigned the value of 255. If the initial gray value is less than or equal to the local gray average value, then the initial gray value is assigned the value of 0.

[0050] The target image is obtained based on the initial image corresponding to the assigned initial grayscale value;

[0051] The target images are then aggregated to obtain a target environment image set.

[0052] Optionally, the step of denoising the initial point cloud to obtain the target point cloud includes:

[0053] Spatial coordinate points are extracted sequentially from the initial point cloud set, and the following operations are performed on the extracted spatial coordinate points:

[0054] The target neighborhood range is determined based on the extracted spatial coordinates and the preset neighborhood spatial range.

[0055] The number of spatial coordinate points in the target neighborhood is counted to obtain the number of neighborhood points;

[0056] Summarize the number of neighborhood points to obtain the neighborhood point count set;

[0057] A threshold for the number of neighboring points is obtained based on the set of neighboring points, wherein the threshold for the number of neighboring points is the sum of the number of neighboring points in the set of neighboring points multiplied by a preset percentage;

[0058] The number of neighboring points is compared with a threshold for the number of neighboring points. If the number of neighboring points is less than the threshold, the extracted spatial coordinate points are used as noise coordinate points.

[0059] The noise coordinate points are summarized to obtain a noise coordinate point set. The noise coordinate point set is then removed from the initial point cloud to obtain the target point cloud.

[0060] Optionally, obtaining the target monitoring area based on the situational awareness coordinate set includes:

[0061] The first monitoring area is obtained based on the unmanned vessel and the preset safe distance threshold.

[0062] For each situation awareness coordinate in the situation awareness coordinate set, perform the following operation:

[0063] The positional relationship between the situational awareness coordinates and the first monitoring area is determined. If the situational awareness coordinates are located within the first monitoring area, the situational awareness coordinates are used as the initial sensing coordinates. Based on the target environment data corresponding to the initial sensing coordinates, it is determined whether there are preset obstacles in the initial sensing coordinates. If the target point cluster and initial distance set in the corresponding target environment data are not 0, then there are obstacles. If the target point cluster and initial distance set in the corresponding target environment data are 0, then there are no obstacles. The initial sensing coordinates with obstacles are used as danger sensing coordinates, and the initial sensing coordinates without obstacles are used as safety sensing coordinates.

[0064] By summing the safety perception coordinates and the hazard perception coordinates respectively, we obtain the safety perception coordinate set and the hazard perception coordinate set;

[0065] The number of safety-sensing coordinates in the safety-sensing coordinate set is counted to obtain the number of safety coordinates.

[0066] The number of hazard-sensing coordinates in the hazard-sensing coordinate set is counted to obtain the number of hazard coordinates.

[0067] If the number of dangerous coordinates is 0, then the first monitored area is the safe area;

[0068] If the number of safe coordinates is greater than the number of dangerous coordinates, then the first monitoring area will be the warning area;

[0069] If the number of safe coordinates is less than or equal to the number of dangerous coordinates, then the first monitoring area is the dangerous area;

[0070] The target monitoring area is the aforementioned safe area, warning area, or danger area.

[0071] Optionally, the step of controlling the unmanned vessel based on the anti-interference control command and the target monitoring area to obtain the target berthing unmanned vessel includes:

[0072] The berthing path is determined based on the aforementioned anti-interference control command;

[0073] By controlling environmental disturbances, a stable unmanned surface vessel can be obtained.

[0074] If the target monitoring area is a safe area, the stable unmanned vessel is docked using the docking path to obtain the target docked unmanned vessel;

[0075] If the target monitoring area is a warning area, then an obstacle information set is obtained based on the danger perception coordinate set and the stable unmanned vessel. The obstacle information set includes multiple obstacle information sets, and the multiple obstacle information sets contain obstacle types, obstacle volumes and obstacle distances and orientations.

[0076] Based on the obstacle information set and the berthing path, an updated berthing path is obtained, and the stable unmanned vessel is berthed using the updated berthing path to obtain the target berthed unmanned vessel.

[0077] If the target monitoring area is a dangerous area, the stable unmanned vessel is braked urgently to stop the unmanned vessel, and the target docking area is reset to obtain an updated docking area. Based on the updated docking area and the stopped unmanned vessel, the target docked unmanned vessel is obtained.

[0078] Optionally, the step of regulating environmental disturbances to obtain a stable unmanned vessel includes:

[0079] The unmanned vessel is monitored using pre-built wind speed and direction sensors to obtain the target wind speed and relative wind direction;

[0080] Seawater velocity and direction are obtained using a pre-built Doppler log and an unmanned surface vessel;

[0081] The wind interference force is calculated based on the target wind speed and relative wind direction, and the formula for calculating the wind interference force is as follows: in, Indicates wind interference force. Indicates air density, Indicates the wind pressure coefficient. Indicates relative wind direction. Indicates the target wind speed. This represents the projected area of ​​the hull above the waterline, both front and side.

[0082] The flow disturbance force is calculated based on the seawater flow velocity and direction, and the calculation method for the flow disturbance force is as follows: in, Indicates flow interference force. This indicates the density of water. This represents the area of ​​the ship's hull surface below the waterline. Indicates the fluid dynamics coefficient. Indicates the direction of seawater flow. Indicates the velocity of seawater flow;

[0083] The environmental disturbance force is obtained by vector synthesis of the wind disturbance force and the flow disturbance force.

[0084] The feedforward compensation force is obtained based on the environmental interference force, wherein the feedforward compensation force is equal in magnitude and opposite in direction to the environmental interference force;

[0085] The unmanned vessel is stabilized by adjusting the feedforward compensation force and the pre-built controller.

[0086] To achieve the above objectives, the present invention also provides an anti-interference autonomous berthing control system for unmanned vessels, comprising:

[0087] The berthing environment confirmation module is used to receive autonomous control commands and confirm the autonomous control environment based on the autonomous control commands. The autonomous control environment includes an autonomous control system and an unmanned vessel to be berthed and controlled. The autonomous control system includes an environmental perception unit, a situational perception unit, and an anti-interference control unit.

[0088] The environmental perception module is used to obtain a three-dimensional coordinate system for docking based on the unmanned vessel and the preset target docking area;

[0089] Confirm receipt of environmental sensing instructions from the environmental sensing unit, obtain the sensing distance for setting the environmental sensing nodes based on the environmental sensing instructions, and identify multiple environmental sensing nodes in the berthing three-dimensional coordinate system according to the sensing distance.

[0090] Perform the following operation on each of the multiple environment sensing nodes:

[0091] The target sensor set is identified based on the environmental perception command, and the target environment data is obtained based on the target sensor set and the environmental perception node. The target sensor set includes multiple target sensors, which are a visual sensor, a lidar and a millimeter-wave radar, respectively.

[0092] The monitoring area confirmation module is used to receive situational awareness instructions from the situational awareness unit, obtain the node coordinates of the environmental awareness nodes based on the situational awareness instructions, perform an identification operation on the node coordinates using target environmental data to obtain situational awareness coordinates, and summarize the situational awareness coordinates to obtain a situational awareness coordinate set.

[0093] The target monitoring area is obtained based on the situational awareness coordinate set, wherein the target monitoring area is a safe area, an early warning area, or a dangerous area;

[0094] An anti-interference control module is used to receive anti-interference control instructions from an anti-interference control unit, and to control the unmanned vessel based on the anti-interference control instructions and the target monitoring area to obtain a target berthing unmanned vessel.

[0095] Based on the target berthing, the unmanned vessel can achieve anti-interference autonomous berthing control.

[0096] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0097] A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the aforementioned anti-interference autonomous berthing control method for unmanned vessels.

[0098] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned anti-interference autonomous berthing control method for unmanned vessels.

[0099] To address the problems described in the background art, this invention receives autonomous control commands and, based on these commands, identifies the autonomous control environment. This environment includes an autonomous control system and an unmanned surface vessel (USV) to be berthed and controlled. The autonomous control system comprises an environmental perception unit, a situational awareness unit, and an anti-interference control unit. Therefore, before performing anti-interference control on the USV's berthing, this invention considers the USV's operating conditions under different environments or circumstances. Thus, it identifies the autonomous control system and the USV to be berthed and controlled, and then obtains a berthing three-dimensional coordinate system based on the USV and a preset target berthing area. This demonstrates that this invention, when performing anti-interference control on the USV's berthing, also considers… To address the issue of setting multiple environmental sensing nodes around an unmanned surface vessel (USV), a three-dimensional spatial coordinate system is established to make the setting of environmental sensing nodes more accurate. This lays the foundation for subsequent data monitoring of the environmental sensing nodes. The system confirms receipt of environmental sensing commands from the environmental sensing unit, obtains the sensing distance for setting the environmental sensing nodes based on the commands, and identifies multiple environmental sensing nodes in the berthing three-dimensional coordinate system based on the sensing distance. It is evident that this embodiment of the invention also considers the issue of ensuring that the distance between different environmental sensing nodes is not too close or too far when setting them, and determines the location of each environmental sensing node by setting the sensing distance. Knowing the distance between nodes enables the determination of environmental sensing nodes, thereby improving the accuracy of monitoring environmental data around the unmanned vessel. Based on the environmental sensing command, a target sensor set is identified, and target environmental data is acquired based on the target sensor set and the environmental sensing nodes. The target sensor set includes multiple target sensors, namely visual sensors, lidar, and millimeter-wave radar. It is evident that this embodiment of the invention, when monitoring data from environmental sensing nodes, also considers the problems of anomalies in the monitored point cloud data and unclear environmental images. Therefore, by preprocessing the initial monitoring dataset, target environmental data is obtained, and further processing is performed... When performing anti-interference control, considering the potential changes in environmental factors, the system receives situational awareness commands from the situational awareness unit, obtains the node coordinates of the environmental awareness nodes based on these commands, performs an identification operation on the node coordinates using target environmental data to obtain situational awareness coordinates, summarizes these coordinates to obtain a situational awareness coordinate set, and then obtains the target monitoring area based on the situational awareness coordinate set. This target monitoring area can be a safe area, a warning area, or a danger area. The system also receives anti-interference control commands from the anti-interference control unit, and adjusts the unmanned vessel based on these commands and the target monitoring area to achieve target berthing of the unmanned vessel. Therefore, this invention can achieve anti-interference control for unmanned vessel berthing. Attached Figure Description

[0100] Figure 1 is a flowchart illustrating an anti-interference autonomous berthing control method for unmanned vessels provided in an embodiment of the present invention.

[0101] Figure 2 is a functional block diagram of an anti-interference autonomous berthing control system for unmanned vessels provided in an embodiment of the present invention;

[0102] Figure 3 is a schematic diagram of the structure of an electronic device for implementing the anti-interference autonomous berthing control method for unmanned vessels according to an embodiment of the present invention.

[0103] Explanation of reference numerals in the attached figures:

[0104] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0105] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0106] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0107] This application provides an anti-interference autonomous berthing control method for unmanned surface vessels (USVs). The executing entity of this anti-interference autonomous berthing control method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the anti-interference autonomous berthing control method for USVs can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0108] Referring to Figure 1, a flowchart illustrating an anti-interference autonomous berthing control method for unmanned surface vessels (USVs) according to an embodiment of the present invention is shown. In this embodiment, the anti-interference autonomous berthing control method for USVs includes:

[0109] S1. Receive autonomous control instructions, and confirm the autonomous control environment based on the autonomous control instructions. The autonomous control environment includes an autonomous control system and an unmanned vessel to be docked and controlled. The autonomous control system includes an environmental perception unit, a situational perception unit, and an anti-interference control unit.

[0110] It should be explained that the autonomous control command is issued by personnel who wish to achieve autonomous berthing control of the unmanned vessel against interference. The autonomous control environment refers to the necessary environment for achieving autonomous berthing control of the unmanned vessel against interference. The autonomous control system refers to the software or app used to achieve autonomous berthing control of the unmanned vessel against interference. An unmanned vessel refers to an unmanned vessel that needs to achieve autonomous berthing through interference control. The autonomous control system includes an environmental perception unit, a situational awareness unit, and an interference control unit. For the specific application of these units, please refer to subsequent embodiments. The main purpose of this invention is to improve the accuracy of autonomous berthing of unmanned vessels.

[0111] For example, Zhang, as the safety officer responsible for the berthing of an unmanned vessel, in order to achieve interference-resistant autonomous berthing control of the unmanned vessel and avoid collisions caused by inaccurate autonomous berthing control, issues the autonomous control command and confirms the autonomous control environment.

[0112] S2. Obtain a three-dimensional coordinate system for docking based on the unmanned vessel and the preset target docking area.

[0113] Furthermore, the step of obtaining the berthing three-dimensional coordinate system based on the unmanned vessel and the preset target berthing area includes:

[0114] The parking center point is determined based on the target parking area;

[0115] The center point of the unmanned vessel was determined based on the aforementioned unmanned vessel.

[0116] The distance value is obtained based on the center point and docking center point of the unmanned vessel;

[0117] Compare the distance value with the preset distance threshold. If the distance value is less than or equal to the distance threshold, then establish a three-dimensional spatial coordinate system with the center point of the unmanned vessel as the origin to obtain the berthing three-dimensional coordinate system. In the berthing three-dimensional coordinate system, the x-axis direction is the same as the movement direction of the unmanned vessel, and the z-axis direction is perpendicular to the sea surface where the unmanned vessel is located.

[0118] If the distance value is greater than the distance threshold, the difference between the distance value and the distance threshold is calculated to obtain the distance control value, and the current unmanned vessel's sailing speed is obtained.

[0119] Based on the distance control value, the sailing speed is controlled using a pre-built fuzzy control algorithm to obtain an updated sailing speed. The unmanned vessel is then optimized using the updated sailing speed to obtain an updated unmanned vessel. The updated unmanned vessel is then used as the unmanned vessel, and the process returns to the step of confirming the center point of the unmanned vessel based on the updated unmanned vessel, until the berthing three-dimensional coordinate system is obtained.

[0120] It is clear that identifying the docking center point based on the target docking area means determining the center point of the area on a horizontal plane within the specified target docking area. The docking center point refers to the center point of the target docking area. The unmanned surface vessel (USV) center point refers to the center point of the USV. Obtaining the distance value based on the USV center point and the docking center point means using distance sensors to monitor the USV center point and the docking center point to obtain the distance between the two points. The distance value refers to the distance between the USV center point and the docking center point.

[0121] Understandably, if the distance value is less than or equal to the distance threshold, it indicates that the unmanned vessel has approached the target docking area. Therefore, a three-dimensional spatial coordinate system is established with the center point of the unmanned vessel as the origin, resulting in a docking three-dimensional coordinate system, which lays the foundation for subsequent determination of environmental perception nodes. The docking three-dimensional coordinate system refers to a three-dimensional spatial coordinate system established with the center point of the unmanned vessel as the origin. If the distance value is greater than the distance threshold, it indicates that the distance between the unmanned vessel and the target docking area is too far. Therefore, the difference between the distance value and the distance threshold is calculated to obtain a distance adjustment value, and the current sailing speed of the unmanned vessel is obtained. Based on the distance adjustment value, a pre-constructed fuzzy control algorithm is used to adjust the sailing speed to obtain an updated sailing speed. The distance adjustment value is the difference between the distance value and the distance threshold. The sailing speed refers to the speed of the unmanned vessel. The updated sailing speed refers to the speed adjusted by the fuzzy control algorithm. Optionally, a variable universe adaptive fuzzy PID control algorithm is used as the fuzzy control algorithm to adjust the sailing speed. The method of using a fuzzy control algorithm to adjust the sailing speed is existing technology and will not be elaborated here. Optimizing the unmanned surface vessel (USV) with the updated sailing rate means setting the USV's sailing rate to the updated sailing rate. An updated USV is an USV whose sailing rate is the updated sailing rate.

[0122] It should be explained that the purpose of using the pre-built fuzzy control algorithm to regulate the sailing speed is to ensure that the unmanned vessel maintains a safe and controllable speed as it approaches the target docking area, based on the logic of "higher speed for greater distance and slower speed for closer distance". When the unmanned vessel is far from the target docking area, the sailing speed is increased to reduce the overall docking time. As the unmanned vessel gets closer to the target area, the speed will gradually decrease, thus allowing sufficient reaction time and distance for braking or fine-tuning, fundamentally avoiding the risk of collision.

[0123] S3. Confirm receipt of environmental sensing instructions from the environmental sensing unit, obtain the sensing distance for setting the environmental sensing nodes based on the environmental sensing instructions, and identify multiple environmental sensing nodes in the berthing three-dimensional coordinate system according to the sensing distance.

[0124] It is clear that the environmental perception unit is a functional module in the autonomous control system used to identify environmental perception nodes for monitoring the environmental data around the unmanned vessel.

[0125] It is clear that the environmental perception command refers to the operational command to begin monitoring the environmental data around the unmanned vessel. The perception distance refers to the distance used to set different environmental perception nodes in the berthing three-dimensional coordinate system, and the distance between two adjacent environmental perception nodes is the perception distance. An environmental perception point refers to a point identified using the perception distance for monitoring target environmental data. For example, in the berthing three-dimensional coordinate system, if the origin coordinates are (0, 0, 0) and the perception distance is 2m, then multiple environmental perception nodes can be identified in the berthing three-dimensional coordinate system using the perception distance, such as (0, 2, 0) and (0, 4, 0), and the distance between two adjacent environmental perception nodes is 2m.

[0126] It should be explained that the control of autonomous docking of unmanned vessels requires the combination of different environmental data at various locations in the environment. By identifying multiple environmental perception nodes in the three-dimensional coordinate system of docking, it is possible to control the docking of unmanned vessels in different areas, thereby improving the accuracy of the control of unmanned vessel docking.

[0127] S4. Based on the environmental perception command, the target sensor set is identified, and target environmental data is obtained based on the target sensor set and the environmental perception node. The target sensor set includes multiple target sensors, and the multiple target sensors are a visual sensor, a lidar and a millimeter-wave radar.

[0128] It is clear that identifying the target sensor set based on the aforementioned environmental perception command means activating multiple target sensors on the unmanned surface vessel (USV) when it receives the environmental perception command. These multiple target sensors include visual sensors, lidar, and millimeter-wave radar. Target sensors are sensors used to monitor environmental data surrounding the USV.

[0129] Furthermore, the acquisition of target environment data based on the target sensor set and environmental sensing nodes includes:

[0130] The target sampling frequency is obtained based on the target sensor set;

[0131] The target sensor set is optimized using the target sampling frequency to obtain an optimized sensor set, wherein the optimized sensor set includes multiple optimized sensors, and the optimized sensors are an optimized visual sensor, an optimized lidar, and an optimized millimeter-wave radar.

[0132] For each optimized sensor in the optimized sensor set, perform the following operation:

[0133] Based on the preset monitoring start time, the environmental sensing nodes are monitored using optimized sensors to obtain the initial monitoring dataset;

[0134] The initial monitoring datasets are summarized to obtain an initial monitoring dataset group, which includes multiple initial monitoring datasets, namely an environmental image set, an initial point cloud set, and an initial distance set, and the initial point cloud set includes multiple spatial coordinate points;

[0135] The environmental image set is preprocessed to obtain the target environmental image set;

[0136] The initial point set is denoised to obtain the target point set;

[0137] The target environment data is obtained by summarizing the target environment image set, target point cloud set, and initial distance set.

[0138] Furthermore, the step of obtaining the target sampling frequency based on the target sensor set includes:

[0139] For each target sensor in the target sensor set, perform the following operation:

[0140] Obtain the sampling frequency range of the target sensor, whereby the sampling frequency range includes the minimum sampling frequency and the maximum sampling frequency;

[0141] By summing the minimum and maximum sampling frequencies respectively, we obtain the minimum sampling frequency set and the maximum sampling frequency set;

[0142] The lower limit sampling frequency is obtained based on the set of minimum sampling frequencies, wherein the lower limit sampling frequency is the minimum sampling frequency that is the largest in the set of minimum sampling frequencies;

[0143] The upper limit sampling frequency is obtained based on the set of maximum sampling frequencies, wherein the upper limit sampling frequency is the minimum maximum sampling frequency in the set of maximum sampling frequencies;

[0144] Compare the lower sampling frequency and the upper sampling frequency. If the lower sampling frequency is less than or equal to the upper sampling frequency, then obtain the target sampling frequency based on the lower sampling frequency and the upper sampling frequency, wherein the target sampling frequency is the average of the lower sampling frequency and the upper sampling frequency.

[0145] If the lower sampling frequency is greater than the upper sampling frequency, the target sensor corresponding to the lower sampling frequency is replaced to obtain an updated target sensor. The updated target sensor is then used as the target sensor, and the process returns to the step of obtaining the sampling frequency range of the target sensor until the target sampling frequency is obtained.

[0146] It is clear that obtaining the sampling frequency range of the target sensor refers to confirming the sampling frequency range of the target sensor in the product manual corresponding to the target sensor. The sampling frequency range refers to the range of sampling frequencies when the target sensor monitors data. The minimum sampling frequency refers to the minimum sampling frequency in the target sensor. The maximum sampling frequency refers to the maximum sampling frequency in the target sensor. Obtaining the lower limit sampling frequency based on the set of minimum sampling frequencies means extracting the largest minimum sampling frequency from the set of minimum sampling frequencies. Obtaining the upper limit sampling frequency based on the set of maximum sampling frequencies means extracting the smallest minimum sampling frequency from the set of maximum sampling frequencies. The lower limit sampling frequency is the largest minimum sampling frequency in the set of minimum sampling frequencies. The upper limit sampling frequency is the smallest maximum sampling frequency in the set of maximum sampling frequencies. For example, if the set of minimum sampling frequencies is (4, 6, 4, 2), then the lower limit sampling frequency is 6; if the set of maximum sampling frequencies is (10, 12, 11, 15), then the upper limit sampling frequency is 10.

[0147] Understandably, if the lower sampling frequency is less than or equal to the upper sampling frequency, it indicates that there exists a sampling frequency within the range of the lower and upper sampling frequencies that allows each target sensor to monitor environmental data at this sampling frequency. Therefore, the target sampling frequency is obtained based on the lower and upper sampling frequencies. Obtaining the target sampling frequency based on the lower and upper sampling frequencies means calculating the average of the lower and upper sampling frequencies and using this average as the target sampling frequency. The target sampling frequency is the average of the lower and upper sampling frequencies. If the lower sampling frequency is greater than the upper sampling frequency, it indicates that the minimum sampling frequency of the target sensor corresponding to the lower sampling frequency is greater than the maximum sampling frequency of a certain target sensor, making it impossible to monitor data at the same sampling frequency. Therefore, the target sensor corresponding to the lower sampling frequency is replaced to obtain an updated target sensor. Using the updated target sensor as the target sensor, the process returns to the step of obtaining the sampling frequency range of the target sensor until the target sampling frequency is obtained. The updated target sensor refers to the target sensor obtained after replacing the target sensor corresponding to the lower sampling frequency. The target sampling frequency refers to the sampling frequency of the target sensor cluster when monitoring the environmental data around the unmanned vessel.

[0148] It is clear that optimizing the target sensor set using the target sampling frequency means setting the sampling frequency of all target sensors in the target sensor set to the target sampling frequency. An optimized sensor set refers to a set of target sensors that acquire data at the target sampling frequency. An optimized vision sensor refers to a vision sensor that acquires data at the target sampling frequency. An optimized LiDAR refers to a LiDAR that acquires data at the target sampling frequency. An optimized millimeter-wave radar refers to a millimeter-wave radar that acquires data at the target sampling frequency.

[0149] Understandably, the initial monitoring dataset refers to the dataset obtained by using optimized sensors to monitor environmental perception nodes multiple times, with the monitoring start time as the starting point. The monitoring start time point refers to the starting time point when monitoring the environmental data around the unmanned vessel begins. The environmental image set refers to the image set obtained by optimizing visual sensors to monitor environmental perception nodes. The initial point cloud refers to multiple spatial coordinate points established in the berthing three-dimensional coordinate system obtained by optimizing lidar to monitor environmental perception nodes, and is used to describe the geometric contours of surrounding objects. The initial distance set refers to the distance set between the unmanned vessel and surrounding objects obtained by optimizing millimeter-wave radar to monitor environmental perception nodes. For example, after the optimized millimeter-wave radar emits a pulse signal, if there is an object nearby, the pulse signal will be reflected back to the millimeter-wave radar. The distance between the unmanned vessel and the object is calculated based on the time of emitting the pulse signal and receiving the pulse signal again, as well as the speed of light.

[0150] Furthermore, the preprocessing of the environmental image set to obtain the target environmental image set includes:

[0151] For each environment image in the environment image set, perform the following operations:

[0152] The environmental image is converted to grayscale to obtain a grayscale image. The grayscale image is then filtered using a pre-built filter to obtain an initial image.

[0153] A pixel set is obtained based on the initial image, wherein the pixel set includes multiple pixels;

[0154] A grayscale value set is obtained based on the pixel set, wherein the grayscale value set includes multiple grayscale values, and the pixels in the pixel set correspond one-to-one with the grayscale values.

[0155] Perform the following operation on each pixel in the pixel set:

[0156] A local neighborhood is obtained based on the pixel, wherein the local neighborhood is a square region centered on the pixel with a side length of a preset size;

[0157] The local grayscale mean is obtained based on the local neighborhood, wherein the local grayscale mean is the average grayscale value of each pixel in the local neighborhood.

[0158] An initial grayscale value is obtained based on the pixel and grayscale value set, wherein the initial grayscale value is the grayscale value corresponding to the pixel.

[0159] Compare the initial gray value with the local gray average value. If the initial gray value is greater than the local gray average value, then the initial gray value is assigned the value of 255. If the initial gray value is less than or equal to the local gray average value, then the initial gray value is assigned the value of 0.

[0160] The target image is obtained based on the initial image corresponding to the assigned initial grayscale value;

[0161] The target images are then aggregated to obtain a target environment image set.

[0162] It is clear that grayscale conversion refers to the operation of converting a color image into a grayscale image. In this case, it is the step of converting an environmental image into a grayscale image. Grayscale conversion is an existing technology and will not be elaborated further. Optionally, the filter is a high-pass filter; other techniques can achieve the same effect and will not be elaborated further. The initial image refers to the image obtained after performing a filtering operation on the grayscale image using the filter.

[0163] It is understood that obtaining a pixel set based on the initial image means that the initial image contains multiple pixels, and these pixels are identified using OpenCV tools to obtain the pixel set. Obtaining a grayscale value set based on the pixel set means that each pixel in the initial image corresponds to a grayscale value, and the grayscale value can be obtained using OpenCV tools.

[0164] It should be explained that a local neighborhood refers to a square region centered on a pixel with a preset side length. The local grayscale mean refers to the average grayscale value of each pixel within the local neighborhood. For example, in the initial image, the first row of pixels corresponds to grayscale values ​​of 100, 102, and 104; the second row to 90, 94, and 96; and the third row to 96, 96, and 98. If the pixel corresponds to grayscale value 94, and the local neighborhood is a square region with a side length of 3, then the local grayscale mean is approximately 97. Obtaining the initial grayscale value based on the pixel and grayscale value set means identifying the grayscale value corresponding to the pixel within the grayscale value set. The initial grayscale value refers to the grayscale value corresponding to the pixel.

[0165] It is clear that when the initial grayscale value is less than or equal to the local grayscale mean, the pixel can be identified as containing obstacles in the environment, and therefore, the grayscale value at that point is assigned to 0. When the initial grayscale value is greater than the local grayscale mean, the pixel can be identified as a point in the background of the initial image, i.e., a point that does not contain obstacles, and therefore, the grayscale value at that point is assigned to 255. Binarizing the image allows for faster identification of obstacles in the environmental perception nodes. The target image refers to the initial image after binarization. The target environment image set refers to the collection of target images.

[0166] Furthermore, the denoising of the initial point cloud to obtain the target point cloud includes:

[0167] Spatial coordinate points are extracted sequentially from the initial point cloud set, and the following operations are performed on the extracted spatial coordinate points:

[0168] The target neighborhood range is determined based on the extracted spatial coordinates and the preset neighborhood spatial range.

[0169] The number of spatial coordinate points in the target neighborhood is counted to obtain the number of neighborhood points;

[0170] Summarize the number of neighborhood points to obtain the neighborhood point count set;

[0171] A threshold for the number of neighboring points is obtained based on the set of neighboring points, wherein the threshold for the number of neighboring points is the sum of the number of neighboring points in the set of neighboring points multiplied by a preset percentage;

[0172] The number of neighboring points is compared with a threshold for the number of neighboring points. If the number of neighboring points is less than the threshold, the extracted spatial coordinate points are used as noise coordinate points.

[0173] The noise coordinate points are summarized to obtain a noise coordinate point set. The noise coordinate point set is then removed from the initial point cloud to obtain the target point cloud.

[0174] It is clear that the target neighborhood range refers to the area defined around the extracted spatial coordinate point as its center. Counting the number of spatial coordinate points within the target neighborhood range means counting the number of spatial coordinate points within the target neighborhood range. The number of neighborhood points refers to the number of spatial coordinate points within the target neighborhood range. Obtaining the neighborhood point number threshold based on the neighborhood point count set involves calculating the sum of the neighborhood point counts in the neighborhood point count set and multiplying it by a specified percentage. For example, if the sum of the neighborhood point counts in the neighborhood point count set is 100 and the specified percentage is 5%, then the neighborhood point number threshold is 5.

[0175] It should be explained that if the number of neighboring points is less than the threshold for the number of neighboring points, it means that there are few spatial coordinate points around the spatial coordinate point corresponding to that number of neighboring points. Therefore, it is determined to be a discrete point, and thus, the extracted spatial coordinate points are used as noise coordinate points. Noise coordinate points refer to spatial coordinate points that are determined to be discrete points. The target point set refers to the initial point set that does not contain noise coordinate points.

[0176] S5. Receive situational awareness instructions from the situational awareness unit, obtain the node coordinates of the environmental awareness nodes based on the situational awareness instructions, perform an identification operation on the node coordinates using the target environmental data to obtain situational awareness coordinates, and summarize the situational awareness coordinates to obtain a situational awareness coordinate set.

[0177] It should be explained that the situational awareness unit is a functional module in the autonomous control system used for target environment data fusion and understanding of environmental sensing nodes. The situational awareness command refers to the operational command to initiate target environment data fusion and understanding of the environmental sensing nodes. Obtaining the node coordinates of the environmental sensing nodes based on the situational awareness command means confirming the three-dimensional coordinates of the environmental sensing nodes in the docking three-dimensional coordinate system before the environmental sensing nodes begin target environment data fusion and understanding. Node coordinates refer to the coordinate points of the environmental sensing nodes in the docking three-dimensional coordinate system.

[0178] It is clear that using target environment data to identify node coordinates involves determining the type and volume of obstacles at the node coordinates based on the environmental image set and target point cloud in the target environment data, and confirming the distance between the obstacle and the unmanned vessel and the obstacle's movement speed based on the initial distance set. Situational awareness coordinates refer to node coordinates that contain information on obstacle type, volume, distance, and movement speed. For example, an environmental awareness node 10 meters ahead of the unmanned vessel's movement direction has coordinates (10, 0, 0). By understanding and fusing the target environment data collected at this location, it can be determined that a stationary yacht exists at this location.

[0179] S6. Based on the situational awareness coordinate set, obtain multiple monitoring areas, including safe zones, early warning zones, and danger zones.

[0180] Furthermore, the step of obtaining the target monitoring area based on the situational awareness coordinate set includes:

[0181] The first monitoring area is obtained based on the unmanned vessel and the preset safe distance threshold.

[0182] For each situation awareness coordinate in the situation awareness coordinate set, perform the following operation:

[0183] The positional relationship between the situational awareness coordinates and the first monitoring area is determined. If the situational awareness coordinates are located within the first monitoring area, the situational awareness coordinates are used as the initial sensing coordinates. Based on the target environment data corresponding to the initial sensing coordinates, it is determined whether there are preset obstacles in the initial sensing coordinates. If the target point cluster and initial distance set in the corresponding target environment data are not 0, then there are obstacles. If the target point cluster and initial distance set in the corresponding target environment data are 0, then there are no obstacles. The initial sensing coordinates with obstacles are used as danger sensing coordinates, and the initial sensing coordinates without obstacles are used as safety sensing coordinates.

[0184] By summing the safety perception coordinates and the hazard perception coordinates respectively, we obtain the safety perception coordinate set and the hazard perception coordinate set;

[0185] The number of safety-sensing coordinates in the safety-sensing coordinate set is counted to obtain the number of safety coordinates.

[0186] The number of hazard-sensing coordinates in the hazard-sensing coordinate set is counted to obtain the number of hazard coordinates.

[0187] If the number of dangerous coordinates is 0, then the first monitored area is the safe area;

[0188] If the number of safe coordinates is greater than the number of dangerous coordinates, then the first monitoring area will be the warning area;

[0189] If the number of safe coordinates is less than or equal to the number of dangerous coordinates, then the first monitoring area is the dangerous area;

[0190] The target monitoring area is the aforementioned safe area, warning area, or danger area.

[0191] It is clear that the safe distance threshold refers to the minimum distance at which surrounding obstacles will not cause damage to the unmanned surface vessel (USV). Obtaining the first monitoring area based on the USV and the preset safe distance threshold means dividing the USV into a circular area with the safe distance threshold as its radius. The first monitoring area refers to the area where surrounding obstacles need to be monitored.

[0192] Understandably, if the situational awareness coordinates are located within the first monitoring area, it indicates that these coordinates may affect the unmanned vessel. Therefore, further confirmation is required. The situational awareness coordinates are then used as initial perception coordinates, and the presence of obstacles is determined based on the target environment data corresponding to these initial perception coordinates. Initial perception coordinates refer to situational awareness coordinates located within the first monitoring area. If the target point cloud and initial distance set in the corresponding target environment data are not zero, it indicates that the outline and distance information of an obstacle have been detected at the initial perception coordinates. Therefore, it is determined that an obstacle exists at these initial perception coordinates. If the target point cloud and initial distance set in the corresponding target environment data are zero, it indicates that no obstacle-related data information has been detected at the initial perception coordinates. Therefore, it is determined that no obstacle exists at these initial perception coordinates. Danger perception coordinates refer to initial perception coordinates where obstacles exist. Safety perception coordinates refer to initial perception coordinates where no obstacles exist.

[0193] It should be explained that if the target point cloud and initial distance set in the corresponding target environment data are 0, it means that the target sensor set did not detect the target point cloud and initial distance set at this situational awareness coordinate. The target point cloud refers to the contour dataset of the obstacle, and the initial distance set refers to the distance set between the obstacle and the unmanned vessel. If the target point cloud and initial distance set are not detected, then the target point cloud and initial distance set are 0, and it means that there is no obstacle at this situational awareness node. If the target point cloud and initial distance set are not detected, then the target point cloud and initial distance set are not 0, and it means that there is an obstacle at this situational awareness node.

[0194] It is clear that the number of safe coordinates refers to the number of safe-sensing coordinates in the safe-sensing coordinate set. The number of dangerous coordinates refers to the number of dangerous-sensing coordinates in the dangerous-sensing coordinate set. If the number of dangerous coordinates is 0, it means there are no obstacles in the first monitoring area; therefore, the first monitoring area is considered a safe area. If the number of safe coordinates is greater than the number of dangerous coordinates, it means there are relatively few obstacles in the first monitoring area; therefore, the first monitoring area is considered a warning area. If the number of safe coordinates is less than or equal to the number of dangerous coordinates, it means there are relatively many obstacles in the first monitoring area, affecting the berthing of the unmanned vessel; therefore, the first monitoring area is considered a dangerous area. The target monitoring area refers to the first monitoring area that is determined to be a safe area, a warning area, or a dangerous area.

[0195] S7. Receive anti-interference control instructions from the anti-interference control unit, and control the unmanned vessel based on the anti-interference control instructions and multiple monitoring areas to obtain the target berthing unmanned vessel.

[0196] It should be explained that the anti-interference control unit is a functional unit in the autonomous control system that performs anti-interference control on the berthing of unmanned vessels.

[0197] Furthermore, the step of controlling the unmanned vessel based on the anti-interference control command and the target monitoring area to obtain the target berthing unmanned vessel includes:

[0198] The berthing path is determined based on the aforementioned anti-interference control command;

[0199] By controlling environmental disturbances, a stable unmanned surface vessel can be obtained.

[0200] If the target monitoring area is a safe area, the stable unmanned vessel is docked using the docking path to obtain the target docked unmanned vessel;

[0201] If the target monitoring area is a warning area, then an obstacle information set is obtained based on the danger perception coordinate set and the stable unmanned vessel. The obstacle information set includes multiple obstacle information sets, and the multiple obstacle information sets contain obstacle types, obstacle volumes and obstacle distances and orientations.

[0202] Based on the obstacle information set and the berthing path, an updated berthing path is obtained, and the stable unmanned vessel is berthed using the updated berthing path to obtain the target berthed unmanned vessel.

[0203] If the target monitoring area is a dangerous area, the stable unmanned vessel is braked urgently to stop the unmanned vessel, and the target docking area is reset to obtain an updated docking area. Based on the updated docking area and the stopped unmanned vessel, the target docked unmanned vessel is obtained.

[0204] It is clear that the anti-interference control command refers to the operational command issued by the anti-interference control system to begin controlling the berthing of the unmanned vessel. Determining the berthing path based on the aforementioned anti-interference control command means determining the pre-set berthing path of the unmanned vessel before initiating anti-interference control. For example, on the electronic nautical chart of the area where the unmanned vessel is located, the berthing path is obtained by using the A* algorithm to perform global path planning on the electronic nautical chart based on the location of the unmanned vessel and the location of the target berthing area. The A* algorithm is a path planning algorithm, and the method of obtaining the berthing path using the A* algorithm is existing technology and will not be elaborated further here. The berthing path refers to the path taken by the unmanned vessel to berth.

[0205] Furthermore, the environmental disturbance control of the unmanned vessel to obtain a stable unmanned vessel includes:

[0206] The unmanned vessel is monitored using pre-built wind speed and direction sensors to obtain the target wind speed and relative wind direction;

[0207] Seawater velocity and direction are obtained using a pre-built Doppler log and an unmanned surface vessel;

[0208] The wind interference force is calculated based on the target wind speed and relative wind direction, and the formula for calculating the wind interference force is as follows: in, Indicates wind interference force. Indicates air density, Indicates the wind pressure coefficient. Indicates relative wind direction. Indicates the target wind speed. This represents the projected area of ​​the hull above the waterline, both front and side.

[0209] The flow disturbance force is calculated based on the seawater flow velocity and direction, and the calculation method for the flow disturbance force is as follows: in, Indicates flow interference force. This indicates the density of water. This represents the area of ​​the ship's hull surface below the waterline. Indicates the fluid dynamics coefficient. Indicates the direction of seawater flow. Indicates the velocity of seawater flow;

[0210] The environmental disturbance force is obtained by vector synthesis of the wind disturbance force and the flow disturbance force.

[0211] The feedforward compensation force is obtained based on the environmental interference force, wherein the feedforward compensation force is equal in magnitude and opposite in direction to the environmental interference force;

[0212] The unmanned vessel is stabilized by adjusting the feedforward compensation force and the pre-built controller.

[0213] It is clear that the target wind speed refers to the wind speed in the environment surrounding the unmanned surface vessel (USV). The relative wind direction refers to the direction of the wind relative to the USV. Obtaining seawater flow velocity and direction based on a pre-built Doppler log and the USV involves monitoring the underwater portion of the USV using a Doppler log to obtain these parameters. Seawater flow velocity refers to the speed at which seawater flows, and seawater flow direction refers to the direction of seawater flow. Wind disturbance force refers to the aerodynamic force acting on the USV's hull above the waterline. Flow disturbance force refers to the hydrodynamic force acting on the USV's hull below the waterline. Wind pressure coefficient is a dimensionless dynamic parameter obtained from wind tunnel experiments. Flow coefficient is a dimensionless hydrodynamic parameter.

[0214] Understandably, environmental disturbance force refers to the force that interferes with the berthing of the unmanned vessel, and is obtained by vector synthesis of wind disturbance force and current disturbance force. Feedforward compensation force refers to a force equal in magnitude and opposite in direction to the environmental disturbance force. Optionally, a PID controller is used as a pre-built controller. Controlling the unmanned vessel based on the aforementioned feedforward compensation force and the pre-built controller means using the PID controller to generate a feedforward compensation force to counteract the interference of environmental disturbance force on the unmanned vessel. A stable unmanned vessel refers to an unmanned vessel that navigates stably.

[0215] It is clear that if the target monitoring area is a safe area, it means there are no obstacles around the unmanned surface vessel (USV). Therefore, the USV is docked using the designated docking path to obtain the target docked USV. The target docked USV refers to the USV docked in the target docking area. If the target monitoring area is a warning area, it means there are a few obstacles in the target monitoring area. By adjusting the USV's docking path, it can still reach the target docking area. Therefore, based on the hazard perception coordinate set and the stable USV, an obstacle information set is obtained, and an updated docking path is obtained based on the obstacle information set and the docking path. Obtaining the obstacle information set based on the hazard perception coordinate set and the stable USV means using the target environmental data corresponding to the hazard perception coordinate set to identify the type, location, and size information of the obstacles. Obtaining an updated berthing path based on the aforementioned obstacle information set and berthing path refers to replanning a berthing path that avoids obstacles by considering their positions and dimensions. For example, the berthing path can be used as a global reference direction. Then, a series of feasible short-term trajectories are dynamically sampled on the electronic nautical chart where the UAV is located, using a dynamic window method combined with the UAV's current speed, position, and obstacle information set. The optimal collision-free local path is then selected in real time using an evaluation function built into the dynamic window method (which comprehensively considers the trajectory direction, distance from obstacles, and current speed). This dynamically corrects the global path, ultimately generating a safe and smooth updated berthing path that avoids real-time obstacles and leads to the target berthing area. The dynamic window method is a local path planning algorithm, and the method of obtaining an updated berthing path based on the dynamic window method is existing technology and will not be elaborated further here. An updated berthing path refers to a path without obstacles. If the target monitoring area is a danger zone, it indicates that there are numerous obstacles around the unmanned surface vessel (USV) and the target docking area, preventing the USV from docking there. Therefore, an emergency braking is applied to the stable USV, bringing it to a stop. The target docking area is then reset to obtain an updated docking area. "Stopped USV" refers to a USV that has stopped docking. "Updated docking area" refers to a newly set target docking area that the USV can dock in. Obtaining the target docking USV based on the updated docking area and the stopped USV involves using the updated docking area as the target docking area and the stopped USV as the USV, returning to the step of obtaining the docking three-dimensional coordinate system based on the USV and the preset target docking area, until the target docking USV is obtained.

[0216] S8. Based on the target, the unmanned vessel can achieve anti-interference autonomous berthing control.

[0217] Furthermore, the anti-interference autonomous berthing control based on the target berthing unmanned vessel includes:

[0218] A spatial reference is constructed based on the aforementioned three-dimensional coordinate system for berthing;

[0219] Based on the aforementioned spatial reference, multiple environmental sensing nodes are deployed, and environmental data is collected from these nodes to obtain an environmental sensing layer.

[0220] Multiple environmental perception nodes in the environmental perception layer and their corresponding target environmental data are fused and identified to obtain a situational perception coordinate set. The situational perception coordinate set is then used to conduct a risk assessment to obtain the target monitoring area. Based on the situational perception coordinate set and the target monitoring area, a situational understanding and risk assessment layer is identified.

[0221] Based on the situational understanding and risk assessment layer, the corresponding anti-interference control strategy is matched with the target monitoring area to obtain the decision execution layer;

[0222] Based on the aforementioned spatial reference, environmental perception layer, situational understanding and risk assessment layer, and decision execution layer, a closed-loop data linkage and feedback mechanism is established to obtain an autonomous berthing control loop.

[0223] The autonomous berthing control loop continuously regulates the berthing process of the unmanned vessel, enabling interference-resistant autonomous berthing control.

[0224] It is clear that constructing a unified spatial reference based on the aforementioned berthing three-dimensional coordinate system refers to providing a unique and consistent spatial reference framework for the spatial positioning of all subsequent environmental sensing nodes, the acquisition of target environmental data, and the fusion and identification of situational awareness coordinates. Deploying multiple environmental sensing nodes based on this spatial reference and acquiring environmental data from these nodes refers to setting up multiple environmental sensing nodes in the berthing three-dimensional coordinate system and acquiring data from these nodes to obtain target environmental data. The environmental sensing layer refers to the functional structural layer in the autonomous control system, composed of multiple environmental sensing nodes, the target sensor set, and the target environmental data acquired and preprocessed, used for real-time acquisition, fusion, and characterization of information about the environment surrounding the unmanned vessel.

[0225] It should be explained that fusing and identifying multiple environmental perception nodes and their corresponding target environment data in the environmental perception layer means performing feature matching between the spatial coordinates of the target point cloud and the corresponding pixel regions in the target environment image set to identify the three-dimensional outline and category of the obstacle, and performing spatial position calibration between the distance information in the initial distance set and the three-dimensional outline to determine the distance and orientation of the obstacle. Then, based on the three-dimensional outline and category of the obstacle and the precise distance and orientation of the obstacle, each environmental perception node is assigned a multi-dimensional attribute label containing the obstacle type, volume, distance and orientation.

[0226] It should be explained that risk assessment of the situational awareness coordinate set refers to dividing a first monitoring area around the unmanned vessel and determining the situational awareness coordinates located within the first monitoring area. By using obstacle information in the situational awareness coordinates, the number of obstacles in the first monitoring area is determined. If the number of situational awareness coordinates containing obstacles in the first monitoring area is greater than the number of situational awareness coordinates not containing obstacles, the current first monitoring area is assessed as a danger zone. If the number of situational awareness coordinates containing obstacles in the first monitoring area is less than the number of situational awareness coordinates not containing obstacles, the current first monitoring area is assessed as a warning zone. If none of the situational awareness coordinates in the first monitoring area contain obstacle information, the current first monitoring area is assessed as a safe zone.

[0227] Understandably, identifying the situation understanding and risk assessment layer based on the situational awareness coordinate set and target monitoring area refers to using the method and process of acquiring the situational awareness coordinate set and target monitoring area as the risk assessment layer in the autonomous berthing system. The situational awareness and risk assessment layer refers to the functional structure layer in the autonomous berthing system that, through quantitative analysis of the obstacle information and its spatial distribution represented by each situational awareness coordinate in the situational awareness coordinate set, and combined with preset safety rules, assesses the threat level, thereby transforming the target environment data and situational awareness coordinate set into a monitoring area classification with clear safety semantics, and thus forming a quantifiable and decision-making functional structure layer for the overall safety situation of the berthing environment.

[0228] It is clear that matching the anti-interference control strategy corresponding to the target monitoring area in the situation understanding and risk assessment layer refers to matching the corresponding anti-interference control strategy with the specific area type corresponding to the target monitoring area, and the method for matching the anti-interference control strategy has been given in the above embodiments. The decision execution layer refers to the functional structure layer in the autonomous berthing system that automatically selects and executes the anti-interference control strategy corresponding to the risk level based on the classification result of the target monitoring area output by the situation understanding and risk assessment layer, and transforms the risk assessment result into specific control commands through coordinated path planning and feedforward compensation control. The autonomous berthing control loop refers to the closed-loop control system used for anti-interference control of unmanned vessel berthing in the autonomous control system, which is composed of the spatial reference, environmental perception layer, situation understanding and risk assessment layer, and decision execution layer.

[0229] For example, when Xiao Zhao issues the autonomous control command, he sets a target docking area for the unmanned vessel to dock. Through the collaborative cooperation of various units in the autonomous control system, the unmanned vessel docks in the target docking area, thus obtaining the target docked unmanned vessel, thereby realizing the control of the unmanned vessel's anti-interference autonomous docking.

[0230] To address the problems described in the background art, this invention receives autonomous control commands and, based on these commands, identifies the autonomous control environment. This environment includes an autonomous control system and an unmanned surface vessel (USV) to be berthed and controlled. The autonomous control system comprises an environmental perception unit, a situational awareness unit, and an anti-interference control unit. Therefore, before performing anti-interference control on the USV's berthing, this invention considers the USV's operating conditions under different environments or circumstances. Thus, it identifies the autonomous control system and the USV to be berthed and controlled, and then obtains a berthing three-dimensional coordinate system based on the USV and a preset target berthing area. This demonstrates that this invention, when performing anti-interference control on the USV's berthing, also considers… To address the issue of setting multiple environmental sensing nodes around an unmanned surface vessel (USV), a three-dimensional spatial coordinate system is established to make the setting of environmental sensing nodes more accurate. This lays the foundation for subsequent data monitoring of the environmental sensing nodes. The system confirms receipt of environmental sensing commands from the environmental sensing unit, obtains the sensing distance for setting the environmental sensing nodes based on the commands, and identifies multiple environmental sensing nodes in the berthing three-dimensional coordinate system based on the sensing distance. It is evident that this embodiment of the invention also considers the issue of ensuring that the distance between different environmental sensing nodes is not too close or too far when setting them, and determines the location of each environmental sensing node by setting the sensing distance. Knowing the distance between nodes enables the determination of environmental sensing nodes, thereby improving the accuracy of monitoring environmental data around the unmanned vessel. Based on the environmental sensing command, a target sensor set is identified, and target environmental data is acquired based on the target sensor set and the environmental sensing nodes. The target sensor set includes multiple target sensors, namely visual sensors, lidar, and millimeter-wave radar. It is evident that this embodiment of the invention, when monitoring data from environmental sensing nodes, also considers the problems of anomalies in the monitored point cloud data and unclear environmental images. Therefore, by preprocessing the initial monitoring dataset, target environmental data is obtained, and further processing is performed... When performing anti-interference control, considering the potential changes in environmental factors, the system receives situational awareness commands from the situational awareness unit, obtains the node coordinates of the environmental awareness nodes based on these commands, performs an identification operation on the node coordinates using target environmental data to obtain situational awareness coordinates, summarizes these coordinates to obtain a situational awareness coordinate set, and then obtains the target monitoring area based on the situational awareness coordinate set. This target monitoring area can be a safe area, a warning area, or a danger area. The system also receives anti-interference control commands from the anti-interference control unit, and adjusts the unmanned vessel based on these commands and the target monitoring area to achieve target berthing of the unmanned vessel. Therefore, this invention can achieve anti-interference control for unmanned vessel berthing.

[0231] Figure 2 shows a functional block diagram of an anti-interference autonomous berthing control system for unmanned vessels provided in an embodiment of the present invention.

[0232] The anti-interference autonomous berthing control system 100 for unmanned vessels described in this invention can be installed in an electronic device. Depending on the functions implemented, the anti-interference autonomous berthing control system 100 for unmanned vessels may include a berthing environment confirmation module 101, an environment perception module 102, a monitoring area confirmation module 103, and an anti-interference control module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0233] The berthing environment confirmation module 101 is used to receive autonomous control instructions and confirm the autonomous control environment based on the autonomous control instructions. The autonomous control environment includes an autonomous control system and an unmanned vessel to be berthed and controlled. The autonomous control system includes an environmental perception unit, a situational perception unit, and an anti-interference control unit.

[0234] The environmental perception module 102 is used to obtain a three-dimensional coordinate system for docking based on the unmanned vessel and the preset target docking area.

[0235] Confirm receipt of environmental sensing instructions from the environmental sensing unit, obtain the sensing distance for setting the environmental sensing nodes based on the environmental sensing instructions, and identify multiple environmental sensing nodes in the berthing three-dimensional coordinate system according to the sensing distance.

[0236] Perform the following operation on each of the multiple environment sensing nodes:

[0237] The target sensor set is identified based on the environmental perception command, and the target environment data is obtained based on the target sensor set and the environmental perception node. The target sensor set includes multiple target sensors, which are a visual sensor, a lidar and a millimeter-wave radar, respectively.

[0238] The monitoring area confirmation module 103 is used to receive a situational awareness instruction from the situational awareness unit, obtain the node coordinates of the environmental awareness node based on the situational awareness instruction, perform an identification operation on the node coordinates using the target environmental data to obtain the situational awareness coordinates, and summarize the situational awareness coordinates to obtain a situational awareness coordinate set.

[0239] The target monitoring area is obtained based on the situational awareness coordinate set, wherein the target monitoring area is a safe area, an early warning area, or a dangerous area;

[0240] The anti-interference control module 104 is used to receive anti-interference control instructions from the anti-interference control unit, and control the unmanned vessel based on the anti-interference control instructions and the target monitoring area to obtain the target berthing unmanned vessel.

[0241] Based on the target berthing, the unmanned vessel can achieve anti-interference autonomous berthing control.

[0242] In detail, each module in the anti-interference autonomous berthing control system 100 for unmanned vessels described in this embodiment of the invention employs the same technical means as the anti-interference autonomous berthing control method for unmanned vessels described in Figure 1 above, and can produce the same technical effects, which will not be repeated here.

[0243] Figure 3 shows a schematic diagram of an electronic device for implementing an anti-interference autonomous berthing control method for unmanned vessels, according to an embodiment of the present invention.

[0244] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as an anti-interference autonomous berthing control method program for unmanned vessels.

[0245] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as code for an anti-interference autonomous berthing control method program for unmanned vessels, but also to temporarily store data that has been output or will be output.

[0246] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., anti-interference autonomous berthing control methods for unmanned vessels) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0247] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0248] Figure 3 only shows an electronic device with components. Those skilled in the art will understand that the structure shown in Figure 3 does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0249] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0250] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0251] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0252] The memory 11 in the electronic device 1 stores a program for an anti-interference autonomous berthing control method for unmanned vessels, which is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0253] The autonomous control system receives autonomous control commands and confirms the autonomous control environment based on the autonomous control commands. The autonomous control environment includes an autonomous control system and an unmanned vessel to be docked and controlled. The autonomous control system includes an environmental perception unit, a situational perception unit, and an anti-interference control unit.

[0254] A three-dimensional coordinate system for docking is obtained based on the unmanned vessel and the preset target docking area;

[0255] Confirm receipt of environmental sensing instructions from the environmental sensing unit, obtain the sensing distance for setting the environmental sensing nodes based on the environmental sensing instructions, and identify multiple environmental sensing nodes in the berthing three-dimensional coordinate system according to the sensing distance.

[0256] Perform the following operation on each of the multiple environment sensing nodes:

[0257] The target sensor set is identified based on the environmental perception command, and the target environment data is obtained based on the target sensor set and the environmental perception node. The target sensor set includes multiple target sensors, which are a visual sensor, a lidar and a millimeter-wave radar, respectively.

[0258] Receive situational awareness instructions from the situational awareness unit, obtain the node coordinates of the environmental awareness node based on the situational awareness instructions, perform an identification operation on the node coordinates using target environmental data to obtain situational awareness coordinates, and summarize the situational awareness coordinates to obtain a situational awareness coordinate set.

[0259] The target monitoring area is obtained based on the situational awareness coordinate set, wherein the target monitoring area is a safe area, an early warning area, or a dangerous area;

[0260] The system receives anti-interference control instructions from the anti-interference control unit, and controls the unmanned vessel based on the anti-interference control instructions and the target monitoring area to obtain the target berthing unmanned vessel.

[0261] Based on the target berthing, the unmanned vessel can achieve anti-interference autonomous berthing control.

[0262] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiments of Figures 1 to 3, which will not be repeated here.

[0263] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0264] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0265] The autonomous control system receives autonomous control commands and confirms the autonomous control environment based on the autonomous control commands. The autonomous control environment includes an autonomous control system and an unmanned vessel to be docked and controlled. The autonomous control system includes an environmental perception unit, a situational perception unit, and an anti-interference control unit.

[0266] A three-dimensional coordinate system for docking is obtained based on the unmanned vessel and the preset target docking area;

[0267] Confirm receipt of environmental sensing instructions from the environmental sensing unit, obtain the sensing distance for setting the environmental sensing nodes based on the environmental sensing instructions, and identify multiple environmental sensing nodes in the berthing three-dimensional coordinate system according to the sensing distance.

[0268] Perform the following operation on each of the multiple environment sensing nodes:

[0269] The target sensor set is identified based on the environmental perception command, and the target environment data is obtained based on the target sensor set and the environmental perception node. The target sensor set includes multiple target sensors, which are a visual sensor, a lidar and a millimeter-wave radar, respectively.

[0270] Receive situational awareness instructions from the situational awareness unit, obtain the node coordinates of the environmental awareness node based on the situational awareness instructions, perform an identification operation on the node coordinates using target environmental data to obtain situational awareness coordinates, and summarize the situational awareness coordinates to obtain a situational awareness coordinate set.

[0271] The target monitoring area is obtained based on the situational awareness coordinate set, wherein the target monitoring area is a safe area, an early warning area, or a dangerous area;

[0272] The system receives anti-interference control instructions from the anti-interference control unit, and controls the unmanned vessel based on the anti-interference control instructions and the target monitoring area to obtain the target berthing unmanned vessel.

[0273] Based on the target berthing, the unmanned vessel can achieve anti-interference autonomous berthing control.

[0274] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0275] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0276] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0277] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0278] Finally, it should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for anti-interference autonomous berthing control of unmanned vessels, characterized in that, The method includes: receiving an autonomous control command; confirming the autonomous control environment based on the autonomous control command, wherein the autonomous control environment includes an autonomous control system and an unmanned surface vessel (USV) to be docked and controlled, and the autonomous control system includes an environmental perception unit, a situational awareness unit, and an anti-interference control unit; obtaining a docking three-dimensional coordinate system based on the USV and a preset target docking area; confirming the receipt of an environmental perception command from the environmental perception unit; obtaining a sensing distance for setting environmental perception nodes based on the environmental perception command; confirming multiple environmental perception nodes in the docking three-dimensional coordinate system based on the sensing distance; and performing the following operation on each of the multiple environmental perception nodes: confirming a target sensor set based on the environmental perception command. The system acquires target environmental data from the target sensor set and environmental sensing nodes. The target sensor set includes multiple target sensors, such as a vision sensor, a lidar, and a millimeter-wave radar. It receives situational awareness commands from the situational awareness unit, acquires the node coordinates of the environmental sensing nodes based on these commands, performs an identification operation on the node coordinates using the target environmental data, obtains the situational awareness coordinates, and summarizes these coordinates to obtain a situational awareness coordinate set. Acquiring the node coordinates of the environmental sensing nodes based on the situational awareness commands means that before the environmental sensing nodes begin fusion and understanding of target environmental data, the three-dimensional coordinates of the environmental sensing nodes are determined in the berthing three-dimensional coordinate system, and the target environmental data is used to... Performing a labeling operation on node coordinates refers to determining the type and volume of obstacles present at the node coordinates based on the environmental image set and target point cloud in the target environment data, and confirming the distance between the obstacle and the unmanned vessel and the obstacle's moving speed based on the initial distance set. Situational awareness coordinates refer to node coordinates containing information on obstacle type, volume, distance, and moving speed. Based on the situational awareness coordinate set, a target monitoring area is obtained, wherein the target monitoring area is a safe area, a warning area, or a danger area. Anti-interference control commands are received from the anti-interference control unit, and the unmanned vessel is controlled based on the anti-interference control commands and the target monitoring area to obtain a target berthing unmanned vessel. The process involves using the anti-interference control commands and the target monitoring area... The process of controlling the unmanned surface vessel (USV) to obtain a target USV berthed includes: confirming a berth path based on the anti-interference control command; controlling environmental interference to obtain a stable USV; if the target monitoring area is a safe area, berthing the stable USV using the berth path to obtain the target USV berthed; if the target monitoring area is a warning area, acquiring an obstacle information set based on the hazard perception coordinate set and the stable USV, wherein the obstacle information set includes multiple obstacle information sets, and the multiple obstacle information sets contain obstacle types, obstacle volumes, and obstacle distances and orientations; obtaining an updated berth path based on the obstacle information set and the berth path, and berthing the stable USV using the updated berth path to obtain the target USV berthed.If the target monitoring area is a dangerous area, the stable unmanned surface vessel (USV) is brought to an emergency stop, and the target berthing area is reset to obtain an updated berthing area. Based on the updated berthing area and the stopped USV, the target berthing USV is obtained; based on the target berthing USV, anti-interference autonomous berthing control is achieved.

2. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 1, characterized in that, The step of obtaining a berthing three-dimensional coordinate system based on the unmanned vessel and a preset target berthing area includes: identifying the berthing center point based on the target berthing area; identifying the center point of the unmanned vessel based on the unmanned vessel; obtaining a distance value based on the center point of the unmanned vessel and the berthing center point; comparing the distance value with a preset distance threshold; if the distance value is less than or equal to the distance threshold, establishing a three-dimensional spatial coordinate system with the center point of the unmanned vessel as the origin to obtain the berthing three-dimensional coordinate system, wherein the x-axis direction in the berthing three-dimensional coordinate system is the same as the movement direction of the unmanned vessel, and the z-axis direction is perpendicular to the sea level where the unmanned vessel is located; if the distance value is greater than the distance threshold, calculating the difference between the distance value and the distance threshold to obtain a distance adjustment value, and obtaining the current sailing speed of the unmanned vessel; based on the distance adjustment value, adjusting the sailing speed using a pre-constructed fuzzy control algorithm to obtain an updated sailing speed; optimizing the unmanned vessel with the updated sailing speed to obtain an updated unmanned vessel; using the updated unmanned vessel as the unmanned vessel, returning to the step of identifying the center point of the unmanned vessel based on the unmanned vessel, until the berthing three-dimensional coordinate system is obtained.

3. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 2, characterized in that, The step of acquiring target environment data based on the target sensor set and environmental sensing nodes includes: acquiring a target sampling frequency based on the target sensor set; optimizing the target sensor set with the target sampling frequency to obtain an optimized sensor set, wherein the optimized sensor set includes multiple optimized sensors, and the optimized sensors are an optimized visual sensor, an optimized lidar, and an optimized millimeter-wave radar; performing the following operations on each optimized sensor in the optimized sensor set: monitoring the environmental sensing nodes using the optimized sensors based on a preset monitoring start time point to obtain an initial monitoring dataset; summarizing the initial monitoring dataset to obtain an initial monitoring dataset group, wherein the initial monitoring dataset group includes multiple initial monitoring datasets, and the multiple initial monitoring datasets are an environmental image set, an initial point cloud set, and an initial distance set, and the initial point cloud set includes multiple spatial coordinate points; preprocessing the environmental image set to obtain a target environment image set; denoising the initial point cloud set to obtain a target point cloud set; summarizing the target environment image set, the target point cloud set, and the initial distance set to obtain target environment data.

4. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 3, characterized in that, The step of obtaining the target sampling frequency based on the target sensor set includes: performing the following operations on each target sensor in the target sensor set: obtaining the sampling frequency range of the target sensor, wherein the sampling frequency range includes a minimum sampling frequency and a maximum sampling frequency; summarizing the minimum sampling frequency and the maximum sampling frequency to obtain a minimum sampling frequency set and a maximum sampling frequency set; obtaining a lower limit sampling frequency based on the minimum sampling frequency set, wherein the lower limit sampling frequency is the largest minimum sampling frequency in the minimum sampling frequency set; obtaining an upper limit sampling frequency based on the maximum sampling frequency set, wherein the upper limit sampling frequency is the smallest maximum sampling frequency in the maximum sampling frequency set; comparing the lower limit sampling frequency and the upper limit sampling frequency; if the lower limit sampling frequency is less than or equal to the upper limit sampling frequency, then obtaining a target sampling frequency based on the lower limit sampling frequency and the upper limit sampling frequency, wherein the target sampling frequency is the average of the lower limit sampling frequency and the upper limit sampling frequency; if the lower limit sampling frequency is greater than the upper limit sampling frequency, then replacing the target sensor corresponding to the lower limit sampling frequency to obtain an updated target sensor, using the updated target sensor as the target sensor, and returning to the step of obtaining the sampling frequency range of the target sensor, until the target sampling frequency is obtained.

5. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 4, characterized in that, The preprocessing of the environmental image set to obtain the target environmental image set includes: performing the following operations on each environmental image in the environmental image set: performing a grayscale conversion operation on the environmental image to obtain a grayscale image; performing a filtering operation on the grayscale image using a pre-constructed filter to obtain an initial image; obtaining a pixel set based on the initial image, wherein the pixel set includes multiple pixels; obtaining a grayscale value set based on the pixel set, wherein the grayscale value set includes multiple grayscale values, and the pixels in the pixel set correspond one-to-one with the grayscale values; performing the following operations on each pixel in the pixel set: obtaining a local neighborhood based on the pixel, wherein the local neighborhood is defined by the number of pixels... A square region with a preset side length is defined, centered on a local neighborhood. A local grayscale mean is obtained based on this local neighborhood, where the local grayscale mean is the average grayscale value of each pixel within the local neighborhood. An initial grayscale value is obtained based on the pixels and the set of grayscale values, where the initial grayscale value is the grayscale value corresponding to the pixel. The initial grayscale value and the local grayscale mean are compared. If the initial grayscale value is greater than the local grayscale mean, the initial grayscale value is assigned the value 255; if the initial grayscale value is less than or equal to the local grayscale mean, the initial grayscale value is assigned the value 0. A target image is obtained based on the initial image corresponding to the assigned initial grayscale value. The target images are then aggregated to obtain a target environment image set.

6. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 5, characterized in that, The step of denoising the initial point cloud to obtain the target point cloud includes: sequentially extracting spatial coordinate points from the initial point cloud, and performing the following operations on the extracted spatial coordinate points: confirming the target neighborhood range based on the extracted spatial coordinate points and a preset neighborhood spatial range; counting the number of spatial coordinate points in the target neighborhood range to obtain the number of neighborhood points; summing the number of neighborhood points to obtain a neighborhood point count set; obtaining a neighborhood point count threshold based on the neighborhood point count set, wherein the neighborhood point count threshold is the sum of the number of neighborhood points in the neighborhood point count set multiplied by a preset percentage; comparing the number of neighborhood points with the neighborhood point count threshold, and if the number of neighborhood points is less than the neighborhood point count threshold, then the extracted spatial coordinate points are used as noise coordinate points; summing the noise coordinate points to obtain a noise coordinate point set, and removing the noise coordinate point set from the initial point cloud to obtain the target point cloud.

7. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 6, characterized in that, The step of obtaining the target monitoring area based on the situational awareness coordinate set includes: obtaining a first monitoring area based on the unmanned vessel and a preset safe distance threshold; performing the following operations on each situational awareness coordinate in the situational awareness coordinate set: determining the positional relationship between the situational awareness coordinate and the first monitoring area; if the situational awareness coordinate is located within the first monitoring area, then using the situational awareness coordinate as the initial sensing coordinate, and determining whether there is a preset obstacle in the initial sensing coordinate based on the target environment data corresponding to the initial sensing coordinate; if the target point cloud and initial distance set in the corresponding target environment data are not 0, then there is an obstacle; if the target point cloud and initial distance set in the corresponding target environment data are 0, then there is no obstacle. The initial sensing coordinates of obstacles are designated as hazard sensing coordinates, while the initial sensing coordinates of the absence of obstacles are designated as safe sensing coordinates. The safe sensing coordinates and hazard sensing coordinates are summarized to obtain a safe sensing coordinate set and a hazard sensing coordinate set, respectively. The number of safe sensing coordinates in the safe sensing coordinate set is counted to obtain the number of safe coordinates. The number of hazard sensing coordinates in the hazard sensing coordinate set is counted to obtain the number of hazard coordinates. If the number of hazard coordinates is 0, the first monitoring area is designated as a safe area. If the number of safe coordinates is greater than the number of hazard coordinates, the first monitoring area is designated as a warning area. If the number of safe coordinates is less than or equal to the number of hazard coordinates, the first monitoring area is designated as a hazard area. The safe area, warning area, or hazard area is designated as the target monitoring area.

8. The anti-interference autonomous berthing control method for unmanned vessels as described in claim 7, characterized in that, The method of controlling environmental disturbances to obtain a stable unmanned surface vessel (USV) includes: monitoring the USV using pre-built wind speed and direction sensors to obtain the target wind speed and relative wind direction; acquiring seawater flow velocity and direction based on a pre-built Doppler log and the USV; and calculating the wind disturbance force based on the target wind speed and relative wind direction, wherein the calculation formula for the wind disturbance force is as follows: in, Indicates wind interference force. Indicates air density, Indicates the wind pressure coefficient. Indicates relative wind direction. Indicates the target wind speed. This represents the projected area of ​​the hull's front and sides above the waterline; the current disturbance force is calculated based on the seawater flow velocity and direction, and the calculation method for the current disturbance force is as follows: in, Indicates flow interference force. This indicates the density of water. This represents the area of ​​the ship's hull surface below the waterline. Indicates the fluid dynamics coefficient. Indicates the direction of seawater flow. The wind and current interference forces are vector-synthesized to obtain the environmental interference force; a feedforward compensation force is obtained based on the environmental interference force, wherein the feedforward compensation force is equal in magnitude and opposite in direction to the environmental interference force; the unmanned vessel is regulated based on the feedforward compensation force and a pre-built controller to obtain a stable unmanned vessel.

9. An anti-interference autonomous berthing control system for unmanned vessels, characterized in that, The system includes: a berthing environment confirmation module, used to receive autonomous control commands and confirm the autonomous control environment based on the autonomous control commands, wherein the autonomous control environment includes an autonomous control system and an unmanned vessel to be berthed and controlled, and the autonomous control system includes an environmental perception unit, a situational awareness unit, and an anti-interference control unit; an environmental perception module, used to obtain a berthing three-dimensional coordinate system based on the unmanned vessel and a preset target berthing area; confirm the receipt of environmental perception commands from the environmental perception unit, obtain the perception distance for setting environmental perception nodes based on the environmental perception commands, and confirm multiple environmental perception nodes in the berthing three-dimensional coordinate system according to the perception distance; and perform the following operations on each of the multiple environmental perception nodes. The process involves: identifying a target sensor set based on the environmental perception command; acquiring target environmental data based on the target sensor set and environmental perception nodes; wherein the target sensor set includes multiple target sensors, namely a visual sensor, a lidar, and a millimeter-wave radar; and a monitoring area confirmation module, which receives a situational perception command from the situational perception unit, acquires the node coordinates of the environmental perception nodes based on the situational perception command, performs an identification operation on the node coordinates using the target environmental data to obtain situational perception coordinates, and summarizes the situational perception coordinates to obtain a situational perception coordinate set. The acquisition of the node coordinates of the environmental perception nodes based on the situational perception command refers to the process when the environmental perception nodes begin fusion of target environmental data. Before understanding, the three-dimensional coordinates of the environmental perception nodes are confirmed in the berthing three-dimensional coordinate system. Using target environmental data to identify the node coordinates involves determining the type and volume of obstacles at the node coordinates based on the environmental image set and target point cloud in the target environmental data, and determining the distance between the obstacle and the unmanned vessel and the obstacle's movement speed based on the initial distance set. Situational perception coordinates refer to node coordinates containing information on obstacle type, volume, distance, and movement speed. Based on the situational perception coordinate set, the target monitoring area is obtained, where the target monitoring area is a safe area, a warning area, or a danger area. The anti-interference control module is used to receive anti-interference control commands from the anti-interference control unit and, based on the anti-interference control commands... The unmanned surface vessel (USV) is regulated based on the target monitoring area to obtain a target berthing USV. The regulation of the USV based on the anti-interference control command and the target monitoring area to obtain a target berthing USV includes: confirming the berthing path based on the anti-interference control command; performing environmental interference control on the USV to obtain a stable USV; if the target monitoring area is a safe area, berthing the stable USV using the berthing path to obtain the target berthing USV; if the target monitoring area is a warning area, obtaining an obstacle information set based on the hazard perception coordinate set and the stable USV, wherein the obstacle information set includes multiple obstacle information sets, and the multiple obstacle information sets contain obstacle types, obstacle volumes, and obstacle distances and orientations.Based on the obstacle information set and the berthing path, an updated berthing path is obtained. The stable unmanned surface vessel (USV) is then berthed using this updated path to obtain the target USV. If the target monitoring area is a danger zone, the stable USV is given emergency braking to stop it, and the target berthing area is reset to obtain an updated berthing area. The target USV is then obtained based on the updated berthing area and the stopped USV. Based on the target USV, anti-interference autonomous berthing control is implemented.

Images