A method and system for autonomous early preventive cleaning of a ship at sea

Abstract

The application discloses a kind of ship in-transit autonomous early preventive cleaning method and system, it is related to ship in-transit cleaning technical field, comprising: obtaining the environmental parameter of in-transit ship in current water area, determining EIS impedance reference value in EIS reference database;Carrying out current time's electrochemical impedance spectroscopy detection, obtain current EIS impedance value;According to current EIS impedance value and EIS impedance reference value control start cleaning operation, according to the positioning result of water and underwater respectively obtained from the multi-source observation data of water and underwater;According to the position deviation of water positioning result and underwater positioning result, the joint positioning result is obtained by weighting water positioning result and underwater positioning result according to soft switching weight;After compensating joint positioning result according to the position deviation of water positioning result and underwater positioning result, navigation control of cleaning operation is carried out according to final positioning result. Improve the accuracy of early biofilm fouling degree judgment, the stability and navigation reliability of cleaning operation under complex water environment.

Description

Technical Field

[0001] This invention relates to the field of ship in-transit cleaning technology, and in particular to a method and system for autonomous early preventative cleaning of ships in transit. Background Technology

[0002] Current ship cleaning methods are divided into dock cleaning, freshwater navigation, and underwater cleaning. All three methods are post-cleaning, which are carried out after the ship's hull is already covered with barnacles and mussels, and have many limitations.

[0003] Dry dock cleaning: Ships need to be docked at dry docks or dedicated piers for cleaning. This method is costly and time-consuming (usually taking several days to several weeks, with a single cleaning costing over US$100,000). During this period, the ship cannot operate, resulting in huge economic losses, and the paint on the hull surface is easily damaged.

[0004] Freshwater navigation: It kills marine organisms attached to the hull by disrupting their living environment, but it cannot remove the dead marine organisms attached to the hull, so the cleaning effect is limited.

[0005] Underwater cleaning: Underwater robots operated by divers or remotely controlled clean ships while they are anchored or in port. Although more flexible than dry-dock cleaning, it still requires the ship to stop sailing. These robots are expensive and can easily cause biological and heavy metal pollution in ports. Furthermore, most existing underwater cleaning robots are rigid structures, which have defects such as poor adaptability to curved surfaces, low adsorption reliability, and weak obstacle crossing ability.

[0006] In summary, most existing cleaning methods are post-cleaning methods, which have many problems such as high cleaning costs, expensive equipment, limited cleaning effect, and environmental pollution.

[0007] Some studies have proposed methods for dynamically monitoring ship hull cleanliness during navigation, determining the degree of fouling on the ship's surface using environmental data from the voyage, such as route speed, temperature, salinity, and pH. However, reflecting fouling status through navigation parameters like speed is highly susceptible to factors such as ocean currents and atmospheric circulation. Directly reflecting fouling status through sensor data such as temperature, salinity, and pH is easily affected by abiotic factors, and parameters related to chlorophyll content in the aquatic environment only reflect fouling caused by marine organisms requiring photosynthesis, and cannot provide a comprehensive assessment. Therefore, current methods of directly deriving fouling status from environmental parameters are prone to data inaccuracies.

[0008] In addition, when existing ship cleaning robots perform cleaning operations in complex waters (such as the hull of a ship, waterline areas, and near the propeller), they typically rely on a single positioning source (such as an inertial measurement unit or odometer) for attitude estimation and path positioning and navigation. However, when alternating between surface and underwater operations, directly switching the positioning source without transition processing results in abrupt changes or discontinuities in the output pose at the moment of switching, affecting the accuracy of path tracking and cleaning trajectory. Summary of the Invention

[0009] To address the aforementioned issues, this invention proposes a method and system for autonomous early preventative cleaning of ships en route. By eliminating the influence of environmental parameter changes on impedance spectrum measurements, it improves the accuracy of judging the early degree of biofilm fouling on the ship's surface. Through dynamic soft-switching positioning during the transition between above-water and underwater environments, it enhances the stability of cleaning operations and the reliability of navigation in complex water environments.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides a method for autonomous early preventative cleaning of ships en route, comprising:

[0012] The environmental parameters of the vessel in transit in the current waters are obtained. When the change of the environmental parameters within a first set time period or the time interval between the current moment and the last electrochemical impedance spectroscopy detection moment meets the set requirements, the electrochemical impedance spectroscopy detection at the current moment is performed to obtain the current EIS impedance value.

[0013] Based on the current environmental parameters, determine the EIS impedance reference value in the pre-built EIS reference database, and control the start of the cleaning operation based on the current EIS impedance value and the impedance spectrum deviation rate determined by the EIS impedance reference value.

[0014] After the cleaning operation is started, the above-water positioning results and underwater positioning results are obtained based on multi-source observation data above and below water.

[0015] The soft handover weight is obtained based on the current environmental state parameters, water depth, and water level calibration value. The surface positioning result and the underwater positioning result are weighted according to the soft handover weight to obtain the joint positioning result.

[0016] The final positioning result is obtained by compensating for the positional deviation between the surface and underwater positioning results and the linear time factor. The final positioning result is then used for navigation control of the cleaning operation.

[0017] As an alternative implementation method, the process of constructing the EIS benchmark database includes:

[0018] Construct a three-dimensional typical environmental parameter library covering temperature, salinity, and pH values ​​of different water bodies;

[0019] Based on the three-dimensional typical environmental parameter library, a reference solution library reflecting the three-dimensional environmental parameter characteristics of different water areas is configured. Each reference solution in the reference solution library is used as the test solution to test the EIS impedance value of ship hull material samples without biofilm attachment. Thus, an EIS reference database of ship hull surface without biofilm attachment reflecting typical environmental parameters of different water areas is obtained.

[0020] As an alternative implementation method, based on the current EIS impedance value and EIS impedance reference value Determined impedance spectrum deviation for: ,when When the threshold value is greater than or equal to the set critical cleaning threshold, the cleaning operation is initiated.

[0021] After the hull is fully cleaned, the impedance spectrum deviation rate on the hull surface is sampled again and calculated. If the impedance spectrum deviation rate is less than the critical cleaning threshold, the cleaning operation is considered complete.

[0022] As an alternative implementation method, the process of detecting the electrochemical impedance spectroscopy at the current moment includes: performing electrochemical impedance spectroscopy detection once on each of the predefined n typical regions of the ship's surface, and averaging the obtained EIS impedance values ​​to obtain the current EIS impedance value.

[0023] As an alternative implementation method, the process of obtaining the final positioning result includes:

[0024] The environmental state parameter S is:

[0025] ;

[0026] ;

[0027] Soft handover weight for;

[0028] ; ;

[0029] The joint positioning result is ;

[0030] Position deviation is ;

[0031] The final location result is ;

[0032] in, Here, h is the weighting coefficient, and h0 is the water level calibration value. For the short window variance of IMU sensor data, The maximum dynamic threshold of the IMU. The maximum depth deviation threshold; Let k be the water depth. It is the signal-to-noise ratio of the UWB measurement signal at time k; These are the maximum signal-to-noise ratio and the minimum signal-to-noise ratio, respectively. This is the coefficient for the steepness of the curve; Base steepness; For adaptive gain; It is the average deviation at multiple moments within the anchor window A; The result of the water positioning at time k; The underwater positioning result at time k; Let be the linear time factor at time t. T represents the total time.

[0033] As an optional implementation, before weighting the surface positioning results and underwater positioning results, the method further includes: using the surface positioning results as anchor points and the current environmental state parameters as judgment conditions to adjust the underwater multi-source observation data, thereby updating the underwater positioning results; wherein, the process of adjusting the underwater multi-source observation data includes:

[0034] ;

[0035] ;

[0036] in, To adjust the step size; The true angular velocity and true acceleration are derived from the water positioning results; It refers to the zero bias of the IMU gyroscope and the zero bias of the acceleration. This is the current Odom scaling factor; This is due to scale bias. To fuse the true distance increment extracted from the pose within the anchor point window, The distance measured by the odometer within the same window.

[0037] Secondly, the present invention provides a ship's autonomous early preventative cleaning system, comprising:

[0038] The detection module is configured to acquire the environmental parameters of the vessel in the current waters. When the change in the environmental parameters within a first set time period or the time interval between the current moment and the last electrochemical impedance spectroscopy detection moment meets the set requirements, the current electrochemical impedance spectroscopy is detected to obtain the current EIS impedance value.

[0039] The start control module is configured to determine the EIS impedance reference value from a pre-built EIS reference database based on the current environmental parameters, and control the start of the cleaning operation based on the current EIS impedance value and the impedance spectrum deviation rate determined by the EIS impedance reference value.

[0040] The first positioning module is configured to obtain the above-water positioning result and the underwater positioning result respectively based on the multi-source observation data above and below water after the cleaning operation is started.

[0041] The second positioning module is configured to obtain soft handover weights based on current environmental state parameters, water depth, and water level calibration values, and to weight the surface positioning results and underwater positioning results based on the soft handover weights to obtain joint positioning results.

[0042] The navigation control module is configured to compensate the joint positioning result based on the positional deviation between the surface positioning result and the underwater positioning result, as well as the linear time factor, to obtain the final positioning result, and then perform navigation control for the cleaning operation based on the final positioning result.

[0043] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0044] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0045] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention proposes an autonomous early preventative cleaning method and system for ships en route. During ship navigation, real-time online measurement of the impedance spectrum of the ship's hull surface enables real-time perception of the early degree of fouling on the hull, thereby constructing a critical cleaning criterion for early fouling on the ship's surface. This provides the necessary condition for autonomous perception for the unmanned and autonomous operation of ship cleaning robots en route. In particular, an online calibration method for the impedance spectrum measurement sensor is designed to eliminate the influence of changes in environmental parameters such as temperature, salinity, and pH value on the impedance spectrum measurement by introducing online calibration, thereby eliminating the influence of changes in environmental parameters in different sea areas during the ship's journey on the judgment of the early degree of biofilm fouling on the ship's hull surface.

[0048] This invention employs separate surface and underwater positioning, and based on the identification of environmental state parameters, achieves adaptive switching between surface and underwater modes. This ensures continuous pose during the switching segment without abrupt changes, significantly reducing switching errors and enabling continuous positioning across media. It addresses the problem of frequent peak fluctuations in positioning errors inherent in traditional hard-switching methods. Furthermore, to address the systematic errors between surface and underwater positioning, a progressive compensation and zero-bias feedback mechanism based on the anchor point deviation between surface and underwater positioning results is proposed. Using the surface segment positioning result as the anchor point, the underwater odometer undergoes gentle correction, allowing the trajectory to quickly converge and stabilize after entering the underwater environment, achieving trajectory consistency. Moreover, multiple surfacing maneuvers can form periodic calibrations, effectively suppressing long-term drift.

[0049] This invention enables simultaneous preventative cleaning of ships while they are in motion, eliminating the need for navigation and saving significant time and berth costs, thus reducing operating costs. Furthermore, because it targets the early biofilm stage of cleaning, it eliminates the need for high-powered cleaning methods such as cavitation water jets or steel brushes, resulting in a substantial reduction in cost. It can be deployed on board any ship.

[0050] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 This is a flowchart of the ship's autonomous early preventive cleaning method provided in Embodiment 1 of the present invention;

[0053] Figure 2 This is a schematic diagram of the electrochemical sensing of dirt on the robot hull provided in Embodiment 1 of the present invention;

[0054] Figure 3 The Nyquist plot of electrochemical impedance spectroscopy provided in Example 1 of this invention;

[0055] Figure 4 The Bode plot of the amplitude-frequency characteristics of the electrochemical impedance spectroscopy provided in Embodiment 1 of the present invention;

[0056] Figure 5 The Bode plot of the phase-frequency characteristics of the electrochemical impedance spectroscopy provided in Embodiment 1 of the present invention;

[0057] Figure 6This is the curve showing the relationship between the rate of change of impedance modulus and the fouling value provided in Embodiment 1 of the present invention;

[0058] Figure 7 Bode plots of hull surface samples under different experimental conditions provided in Embodiment 1 of the present invention;

[0059] Figure 8 This is a flowchart of the combined above-water and underwater positioning and navigation method for a ship cleaning robot provided in Embodiment 1 of the present invention. Detailed Implementation

[0060] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0061] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0062] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0063] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0064] Example 1

[0065] like Figure 1 As shown in the figure, this embodiment proposes a method for autonomous early preventative cleaning of ships en route, including:

[0066] The environmental parameters of the vessel in transit in the current waters are obtained. When the change of the environmental parameters within a first set time period or the time interval between the current moment and the last electrochemical impedance spectroscopy detection moment meets the set requirements, the electrochemical impedance spectroscopy detection at the current moment is performed to obtain the current EIS impedance value.

[0067] Based on the current environmental parameters, determine the EIS impedance reference value in the pre-built EIS reference database, and control the start of the cleaning operation based on the current EIS impedance value and the impedance spectrum deviation rate determined by the EIS impedance reference value.

[0068] After the cleaning operation is started, the above-water positioning results and underwater positioning results are obtained based on multi-source observation data above and below water.

[0069] The soft handover weight is obtained based on the current environmental state parameters, water depth, and water level calibration value. The surface positioning result and the underwater positioning result are weighted according to the soft handover weight to obtain the joint positioning result.

[0070] The final positioning result is obtained by compensating for the positional deviation between the surface and underwater positioning results and the linear time factor. The final positioning result is then used for navigation control of the cleaning operation.

[0071] The method described above in this embodiment can be integrated into a ship hull cleaning robot (hereinafter referred to as the robot). Figure 2 As shown, the robot is located on a moving ship. Environmental sensors and an electrochemical impedance spectroscopy (EIS) sensor are integrated at the bottom of the robot. The environmental sensors include temperature, salinity, and pH sensors, used to monitor environmental parameters of the ship in its current waters in real time. The EIS sensor is used to detect the formation of early biofouling, periodically detecting the degree of dirt on the hull surface to determine whether cleaning operations need to be initiated or to assess the cleaning effect after the robot has cleaned the hull.

[0072] like Figure 2 As shown, the electrochemical impedance spectroscopy (EIS) sensor has three electrodes: a working electrode (WE), a reference electrode (RE), and a counter electrode (CE). After the robot reaches the detection position, it contacts the working electrode probe against the ship's hull wall to form the working electrode (WE). At this point, the working electrode (WE) and the reference electrode (RE) form a voltage loop, and the working electrode (WE) and the counter electrode (CE) form a current loop. After completing the open-circuit potential measurement, a small-amplitude AC voltage is applied to the counter electrode via a control amplifier and a transimpedance amplifier to achieve electrochemical detection.

[0073] Plotting the imaginary and real parts of the AC impedance at different frequencies yields the curves (Nyquist plot) of the imaginary and real impedances (corresponding to the capacitance and resistance of the electrode, respectively) as a function of frequency, i.e., electrochemical impedance spectroscopy (EIS). Figure 3 As shown. Figure 3 It is an ideal Nyquist plot, with the horizontal axis being the real part of the impedance Z, representing the resistive component, and the vertical axis being the negative value of the imaginary part of the impedance Z, representing the capacitive reactance in an electrochemical system. Figure 3The semi-circular curve in the diagram represents the trajectory of impedance Z as a function of frequency, with each point corresponding to a specific frequency f. The arrows indicate that the detection scans from the high-frequency region b (the starting point of the curve) to the low-frequency region a (the ending point of the curve). In the high-frequency region, resistor R1 corresponds to ohmic impedance, represented by a point on the Nyquist plot; in the mid-frequency region, resistor R2 and capacitor C2 are connected in parallel, forming a semi-circular feature; in the low-frequency region, the diffusion process introduces impedance, represented by a 45° oblique line.

[0074] Figure 3 Through Equivalent to Figure 4 The Bode plot of the amplitude-frequency response shown is plotted with the horizontal axis representing the logarithm of frequency f (logf) and the vertical axis representing the impedance modulus. logarithm Similarly, Figure 7 Bode plots of the amplitude-frequency characteristics of samples from the hull surface measured under different experimental conditions; Figure 3 Through Equivalent to Figure 5 The Bode plot of the phase-frequency response shown is plotted with the horizontal axis representing the logarithm of frequency f (logf) and the vertical axis representing the impedance phase angle. .

[0075] Experiments show that in the early stages of biofilm growth, the surface impedance of the hull, as shown by electrochemical impedance spectroscopy, increases, with the upward trend being particularly pronounced in the low-frequency range.

[0076] Ten samples from the ship's hull surface were manually cleaned (to remove biofilm), then suspended and immersed in a body of water. Electrochemical measurements were performed after 12, 24, 36, 48, 60, 72, 84, 96, 108, and 120 hours of standing. The fouling value was defined as increasing by 0.1 every 12 hours. Figure 6 The curve shown is the relationship between the rate of change of impedance modulus and the fouling value. It can be seen that after the biofilm covers the surface of the hull (i.e., the working electrode WE), the rate of change of impedance modulus ΔZ obtained by electrochemical measurement increases significantly.

[0077] However, in the natural environment, various conditions change drastically, and changes in environmental parameters such as temperature, salinity, and pH will cause changes in the characteristics of electrochemical impedance spectroscopy. Figure 7 These are electrochemical detection results of ship hull surface samples after artificial cleaning (removal of biofilm) under different experimental conditions. The horizontal axis represents the logarithm of the frequency f, and the vertical axis represents the impedance mode. The logarithm of . From Figure 7 It can be seen that an increase in temperature, an increase in salinity, and a decrease in pH will all lead to a decrease in the electrochemical impedance modulus of the hull surface without biofilm coverage.

[0078] In other words, changes in temperature, salinity, and pH will lead to changes in EIS impedance values. However, changes in temperature, salinity, and pH do not necessarily change the development state of the biofilm. Since changes in EIS impedance values ​​characterize the development state of the biofilm, when judging the degree of biofilm development based on impedance spectra, it is necessary to establish environmental benchmarks for temperature, salinity, and pH values. This can be achieved through online calibration to eliminate the influence of changes in environmental parameters such as temperature, salinity, and pH values ​​on impedance spectrum measurements, thereby eliminating the influence of changes in environmental parameters on the assessment of the early fouling degree of biofilm on the ship's surface.

[0079] Furthermore, even if temperature, salinity, and pH remain constant over a certain period of time, biofilms will continue to develop and change over time.

[0080] Therefore, in order to more accurately achieve autonomous pollution detection of ships en route, this embodiment first selects ship hull material samples without biofilm adhesion for EIS testing under different typical environmental parameters (i.e., different salinity, temperature, and pH), records the results, and forms an EIS benchmark database under different typical environmental parameters.

[0081] The specific construction method is as follows:

[0082] One study published a dataset of global ocean salinity and average monthly temperatures, with salinity ranging from 30‰ to 38‰ (in 1‰ increments) and temperature from 0°C to 30°C (in 5°C increments). Another study published a monthly average global ocean three-dimensional pH grid dataset, with a global ocean resolution of 1°×1°, covering the ocean from 0 to 2000 meters (41 layers), and pH data ranging from 7.5 to 8.2 (in 0.1 increments).

[0083] Based on the two datasets mentioned above, a complete three-dimensional typical environmental parameter library covering temperature, salinity, and pH values ​​of different waters in tropical, temperate, and cold waters of the global ocean is constructed. Furthermore, based on this library, a reference solution library reflecting the three-dimensional environmental parameter characteristics of different sea areas worldwide is configured. Using each reference solution in the library as a test solution, the EIS impedance values ​​of ship hull material samples without biofilm attachment are measured. This yields the EIS reference database Z of ship hull surfaces without biofilm attachment, reflecting typical environmental parameters measured in different regions of the global ocean. base .

[0084] When a ship sails to different waters, the temperature, salinity, and pH value of the ship in the current waters are detected in real time. Then, the typical environmental parameters of the temperature, salinity, and pH value of the current sea area of ​​the ship and robot are determined in the three-dimensional typical environmental parameter library. It is assumed that the typical environmental parameter is the i-th typical environmental parameter.

[0085] When the change in environmental parameters within a first set time period is greater than or equal to a set change threshold, or when the time interval between the current moment and the last electrochemical impedance spectroscopy (EIS) detection moment is greater than or equal to a second set time period, the EIS sensor is calibrated. This involves performing an EIS detection on each of n predefined typical areas of the ship's surface, and averaging the obtained EIS impedance values ​​to obtain the current EIS impedance value. .

[0086] Based on the current EIS impedance value EIS impedance reference value Obtain the impedance spectrum deviation rate: , in order to pass Assess the degree of biofilm development on the ship's hull surface; Compared with the set critical cleaning threshold, when When the threshold is greater than or equal to the critical cleaning threshold, the robot is controlled to start cleaning the hull.

[0087] In this embodiment, the electrochemical impedance spectroscopy (EIS) sensor uses an integrated chip, which can be modified to a non-standard PCB board depending on the available space in different devices. Data such as EIS, temperature, salinity, and pH are transmitted via UART serial port to the robot's internal processor for further processing, featuring online, real-time, miniaturized, and modular characteristics.

[0088] In this embodiment, after initiating the hull cleaning operation, the robot autonomously begins to map, locate, and clean the hull. For example... Figure 8 As shown, specifically:

[0089] S1: Use a unified clock for timestamp synchronization to acquire multi-source observation data above and below water, including IMU (Inertial Measurement Unit) sensor data, Odom (Odometer) sensor data, UWB (Ultra Wide Band) sensor data, and depth sensor data;

[0090] The UWB sensor data includes the robot's planar coordinates. ;

[0091] Odom sensor data includes robot pose. ; For the robot's position, For heading angle;

[0092] IMU sensor data includes acceleration and angular velocity. ; These are the x and y axis components of the acceleration, respectively. Angular velocity;

[0093] Depth sensing data includes water depth This value is used to identify whether the robot is in an underwater environment at the current time k. If the robot is not underwater, this value is zero, indicating that there is no valid depth data.

[0094] S2: Noise modeling and measurement quality are improved through methods such as zero-bias estimation, scale correction, variance mapping and Kalman filtering. On this basis, an extended Kalman filter (EKF) framework is established to dynamically adjust process noise / measurement noise with reliability. Two EKF positioning models are constructed for surface and underwater environments, and independent estimation of the two sources is achieved through state equations and measurement equations.

[0095] First, filtering and error modeling are performed on the sensor data. The IMU sensor data is processed by zero bias compensation and low-pass filtering; the Odom sensor data is processed by moving average and scale factor correction; the UWB sensor data is processed by determining the measurement variance based on the RSSI (Received Signal Strength Indicator) intensity and removing multipath outliers; the depth sensor data is processed by first-order Kalman filter (KF) smoothing.

[0096] Specifically:

[0097] (1) IMU zero bias and filtering.

[0098] angular velocity at time k and linear acceleration A sliding window static estimation with zero bias compensation and low-pass filtering are employed.

[0099] ;

[0100] ;

[0101] ;

[0102] in, The zero bias of angular velocity and zero bias of linear acceleration are estimated after filtering, which correspond to the zero bias of the gyroscope and the zero bias of the accelerometer, respectively. The time window is defined as starting from the current moment. Heading back A time window at time t is used to estimate the zero bias; It is a smoothing factor that controls the degree of filtering; It is the filtered value of the acceleration (or angular velocity) at time k in the world coordinate system; It is the filtered value of the acceleration (or angular velocity) at time k-1 in the world coordinate system; It is a zero-bias estimate of the IMU sensing data at time k; The acceleration (or angular velocity) at time k is the measured value in the world coordinate system.

[0103] (2) Odom scale and slip detection.

[0104] First, the scale factor of the Odom sensor data is corrected using the short-term arc length of the UWB sensor data:

[0105] ;

[0106] in, This represents the odometry scale factor at a given time. This is the scaling factor adjustment amount; This represents the UWB sensor data collected at time i; This represents the UWB sensor data collected at time i-1; This represents the Odom sensor data collected at time i, including position and heading; This represents the Odom sensor data collected at time i-1; Indicates the length of the historical time window; To adjust the step size.

[0107] Subsequently, slippage is detected by speed residual detection; if the speed residual... The difference between the instantaneous linear velocity calculated from the odometer and the predicted velocity predicted from the previous cycle exceeds a set threshold. This indicates that Odom is experiencing significant slippage or wheel speed distortion, which necessitates increasing the Odom measurement noise covariance. This reduces the credibility of Odom in subsequent fusions, and the arrows in the formula indicate that it is amplified.

[0108] ;

[0109] ;

[0110] in, Linear velocity obtained from pose difference (dedicated to velocity residual detection). This represents the velocity residual at time k, i.e., Odom's actual linear velocity. and prediction speed The difference between them; It is a preset speed tolerance threshold used to determine whether slippage has occurred; The Odom sensor data at time k; The data is the Odom sensor data at time k-1; For time intervals.

[0111] (3) UWB variance mapping.

[0112] Based on the real-time signal quality, i.e., the signal-to-noise ratio (SNR), provided by the UWB module, which reflects the reliability of the UWB ranging signal, the measurement covariance is adaptively adjusted.

[0113] ;

[0114] in, It is the noise covariance measured by UWB at time k. It is the initial noise covariance. It is the signal-to-noise ratio of the UWB measured signal at time k, and is an indicator of signal quality; These are the maximum signal-to-noise ratio (SNR) and the minimum signal-to-noise ratio (SNR), which fall within the UWB factory parameter range. Specifically, a high SNR leads to a decrease in covariance and an increase in weight, while a low SNR leads to an increase in covariance and a decrease in weight. The adjustment factor determines the relationship between signal quality and noise covariance.

[0115] (4) Water depth measured by depth sensor A first-order Kalman filter is used to obtain the smoothed result:

[0116] ;

[0117] in, For the variance of depth measurement; The predicted water depth (prior estimate) is calculated at time k-1. The Kalman gain parameter determines the degree to which we "trust the sensor" or "trust the prediction." The updated water depth at time k (posterior estimate) incorporates water depth measurements from depth sensors. water depth predicted by KF ; The prediction error covariance matrix represents the reliability of the prediction. The larger the value, the less reliable the prediction; the smaller the value, the more reliable the prediction.

[0118] Establish a noise model:

[0119] ;

[0120] in, This is the process noise increment matrix, which includes uncertainties in system dynamics, IMU drift, odometer cumulative error, etc. The measurement noise increment matrix includes sensor reading errors, UWB signal jumps, depth measurement drift, etc. The measurement noise covariance matrix is ​​composed of the diagonals of each measurement channel block. That is, IMU / Odom / UWB / depth; process noise covariance matrix These are position process noise, heading angle process noise, linear velocity process noise, angular velocity process noise, gyro zero-bias drift noise, acceleration zero-bias drift noise, and Odom scale factor process noise, each... This represents the noise variance of the corresponding sensor along its respective axis.

[0121] Secondly, the surface model integrates Odom, IMU, and UWB, and uses extended Kalman filtering to estimate the global pose; the underwater model integrates Odom and IMU, relying solely on inertial and odometry data to achieve relative positioning. A noise model based on the measurement noise covariance matrix and the process noise covariance matrix is ​​constructed; multi-source observation data is used as the state vector, and this is combined with underwater measurement noise to define the measurement vector; then, based on the measurement vector and the noise model, extended Kalman filtering is used for state updates, and the planar pose component is taken as the surface and underwater positioning results.

[0122] Specifically:

[0123] (1) Whether above or below water, EKF is based on the same state vector, which is defined as:

[0124] ;

[0125] in, Let k be the plane coordinates at time k; Let be the heading angle at time k; Let K be the linear velocity and angular velocity at time k. The IMU gyroscope and accelerometer have zero bias at time k; Let be the Odom scaling factor at time k.

[0126] Constructing system motion equations based on state vectors (combined with IMU calculations) and using this to predict motion is the foundation for joint prediction of surface and underwater positioning.

[0127] ;

[0128] ;

[0129] The right side of the first equation forms the state vector at time k+1, which will not be elaborated further. The output of the IMU accelerometer at time k; The IMU angular velocity output at time k; The process noise of the IMU gyroscope zero bias, accelerometer zero bias, and scale factor all satisfy a Gaussian distribution. , The process noise covariance matrix; Let k be the state vector at time k-1. The prediction error covariance matrix; For time intervals.

[0130] (2) Water positioning.

[0131] Based on Odom sensor data at time k and UWB sensor data Define a waterborne measurement vector, where, For the robot's position, For heading angle; For the robot's planar coordinates;

[0132] ;

[0133] in, This is a waterborne measurement function used to convert the state vector... Mapped to the sensor measurement space; Reflecting the actual measurement noise of Odom and UWB in a water surface environment, it follows a zero-mean Gaussian distribution, and the measurement noise covariance is... The output is dynamically adjusted according to the reliability of the signal. That is, the UWB noise is adjusted by the environmental reliability S. The larger S is, the better the UWB signal and the higher the UWB weight. The smaller S is, the lower the UWB weight.

[0134] Filtered update output: Each This represents the noise variance of the corresponding sensor along its respective axis; Then from the updated Extracting planar pose components as the result on water .

[0135] (3) Underwater positioning.

[0136] Based on Odom sensor data at time k Define underwater measurement vectors:

[0137] ;

[0138] Among these, underwater positioning lacks UWB, and Odom is the only external constraint. For underwater measurement functions, This refers to underwater measurement noise, which includes Odom noise and scale drift error.

[0139] Filtered output: Similarly, the pose components are extracted as the underwater positioning result. .

[0140] Therefore, surface EKF positioning and underwater EKF positioning share the same set of state vectors and the same motion model, only using different measurements during the update phase; the prediction steps are completely identical for both; the update steps are updated using surface measurements and underwater measurements respectively, thus obtaining two soft-switching positioning results. Both originate from the same state model, but differences arise after updates from different measurement sources (with or without UWB). Subsequent soft handover fusion and feedback correction are used to gradually bring them into agreement.

[0141] S3: In order to automatically select the appropriate model when crossing the water surface, an environmental state parameter S is introduced to realize real-time discrimination of the above-water / underwater environment; and a soft handover weight is introduced to make the positioning results smoothly transition near the water surface and avoid abrupt changes.

[0142] Specifically:

[0143] The environmental state parameter S is:

[0144] ;

[0145] ;

[0146] in, Here, h is the weighting coefficient, and h0 is the water level calibration value. For the short window variance of IMU acceleration / angular velocity, The maximum dynamic threshold of the IMU is set. The set depth deviation allowable range, i.e. the maximum depth deviation threshold, is used for water depth changes, not the maximum water depth value; the final output is the current environmental state parameter S∈[0,1], the larger the value, the closer it is to the reliable water environment; It is the signal-to-noise ratio of the UWB measurement signal at time k; These are the maximum signal-to-noise ratio and the minimum signal-to-noise ratio, respectively. Let k be the water depth at time k.

[0147] Soft handover weight for;

[0148] ; .

[0149] The joint positioning result is .

[0150] Although soft handover achieves smoothness, small residual accumulation may still occur due to differences in cross-domain noise. Therefore, the results from the waterborne positioning system are utilized. Underwater positioning results Perform feedback corrections to achieve cross-domain consistency.

[0151] Therefore, the corrected position deviation is ;

[0152] The final location result is .

[0153] in, Let the water depth be at time k. For water level calibration values; This is a coefficient representing the steepness of the curve; the harsher the environment, the smoother the weight switching. Base steepness; For adaptive gain based on changes in environmental conditions, underwater, Increased switching speed, especially on water. Reduced switching speed for smoother transitions; It is the average deviation of multiple moments within the anchor window A, in order to reduce the impact of instantaneous noise; The result of the water positioning at time k; The underwater positioning result at time k; Let be the linear time factor at time t. T represents the total time.

[0154] Considering that soft handover may still introduce slight cumulative deviations, step S3 uses the high-reliability output from the surface to fine-tune the underwater model and suppress drift through continuous compensation and anchor feedback mechanisms, ensuring the continuity and consistency of the overall attitude output.

[0155] In this embodiment, the positioning results of the water surface section are used as anchor points to perform gentle correction on the underwater IMU+Odom calculation, suppress long-term drift, and enable the diving trajectory to quickly return to the true position.

[0156] When the environmental state parameter S is stable (the change in value between different time points is less than the set threshold) and close to 1, it indicates that the system is in a stable state above water. If it starts to change, the system collects the above-water and underwater pose data to estimate the anchor point deviation. The anchor point window is then opened. That is, before entering the underwater environment, the above-water positioning results are used as anchor points, and the difference between the two positioning results is estimated, i.e., the anchor point deviation estimation.

[0157] ;

[0158] in, This is due to scale bias. To fuse the true distance increment extracted from the pose within the anchor point window, The distance measured by the odometer within the same window.

[0159] Parameter fine-tuning:

[0160] ;

[0161] in, To adjust the step size; The true angular velocity and true acceleration are derived from the above-water positioning results, and the zero bias of the IMU's gyroscope and acceleration is automatically corrected to make the underwater calculation more stable. It refers to the zero bias of the IMU gyroscope and the zero bias of the acceleration. This is the current Odom scale factor, which aligns the scale of the underwater Odom with the actual movement distance, preventing it from drifting further and further away.

[0162] When the environmental state parameter S is stable, the robot is considered to be in a reliable water environment. At this point, the waterborne positioning result is used as an anchor point to estimate the deviation of the underwater positioning result. The system measures scale error and IMU zero bias error, and fine-tunes the parameters of the underwater positioning model to gradually align it with the true pose, thereby improving the overall positioning accuracy.

[0163] S4: The process of navigation control based on the final positioning result after soft handover fusion and deviation correction includes:

[0164] Based on the final positioning results Reference Path Calculate the target point With current location Linear velocity per unit time control quantity With angular velocity ;

[0165] ;

[0166] ;

[0167] ;

[0168] in, Forward search distance; Let i be the i-th reference point on the reference path; For angular error, The function limits the angle to between; It is a saturation function; This is the acceleration proportionality coefficient; The initial minimum speed is set to ensure the speed is not zero and to avoid stagnation. For planar position components, This represents the heading angle component.

[0169] At this point, the robot begins autonomous cleaning. During the cleaning process, pressure sensors monitor changes in external water pressure in real time. When water resistance increases, the pressure signal output by the sensors fluctuates or rises. Upon receiving this signal, the lower-level computer analyzes it and compares it to a threshold to identify abnormal water resistance or increased load. When increased water resistance leads to greater walking resistance, the lower-level computer immediately issues a control command to activate the supercapacitor module, providing instantaneous additional energy output to the walking motor to compensate for the power loss caused by increased water resistance. At this time, the battery output power and the supercapacitor's released power are combined to form a new input power for the walking motor, ensuring that the robot maintains a stable speed and posture even under varying resistance conditions.

[0170] After the robot completes a full cleaning of the ship's hull, it samples and calculates the surface area of ​​the hull again. ,if If the temperature drops below the critical cleaning threshold, it indicates that the ship has been cleaned and the cleaning task is complete.

[0171] The early preventative cleaning scheme described in this embodiment can accurately identify the optimal cleaning window and establish a long-term cleaning mechanism for the ship's surface. This significantly inhibits the attachment of marine organisms such as barnacles and mussels to the main body of the ship, greatly reducing the amount of cleaning work and fuel consumption caused by biological attachment during the port phase, while effectively avoiding hull corrosion problems caused by biological attachment and ensuring the service life of the ship.

[0172] Example 2

[0173] This embodiment provides a ship's autonomous early preventative cleaning system, including:

[0174] The detection module is configured to acquire the environmental parameters of the vessel in the current waters. When the change in the environmental parameters within a first set time period or the time interval between the current moment and the last electrochemical impedance spectroscopy detection moment meets the set requirements, the current electrochemical impedance spectroscopy is detected to obtain the current EIS impedance value.

[0175] The start control module is configured to determine the EIS impedance reference value from a pre-built EIS reference database based on the current environmental parameters, and control the start of the cleaning operation based on the current EIS impedance value and the impedance spectrum deviation rate determined by the EIS impedance reference value.

[0176] The first positioning module is configured to obtain the above-water positioning result and the underwater positioning result respectively based on the multi-source observation data above and below water after the cleaning operation is started.

[0177] The second positioning module is configured to obtain soft handover weights based on current environmental state parameters, water depth, and water level calibration values, and to weight the surface positioning results and underwater positioning results based on the soft handover weights to obtain joint positioning results.

[0178] The navigation control module is configured to compensate the joint positioning result based on the positional deviation between the surface positioning result and the underwater positioning result, as well as the linear time factor, to obtain the final positioning result, and then perform navigation control for the cleaning operation based on the final positioning result.

[0179] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0180] In further embodiments, the following is also provided:

[0181] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0182] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0183] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0184] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0185] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0186] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0187] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0188] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0189] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0190] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0191] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for autonomous early preventative cleaning of ships en route, characterized in that, include: The environmental parameters of the vessel in transit in the current waters are obtained. When the change of the environmental parameters within a first set time period or the time interval between the current moment and the last electrochemical impedance spectroscopy detection moment meets the set requirements, the electrochemical impedance spectroscopy detection at the current moment is performed to obtain the current EIS impedance value. Based on the current environmental parameters, determine the EIS impedance reference value in the pre-built EIS reference database, and control the start of the cleaning operation based on the current EIS impedance value and the impedance spectrum deviation rate determined by the EIS impedance reference value. After the cleaning operation is started, the above-water positioning results and underwater positioning results are obtained based on multi-source observation data above and below water. The soft handover weight is obtained based on the current environmental state parameters, water depth, and water level calibration value. The surface positioning result and the underwater positioning result are weighted according to the soft handover weight to obtain the joint positioning result. The final positioning result is obtained by compensating for the positional deviation between the surface and underwater positioning results and the linear time factor. The final positioning result is then used for navigation control of the cleaning operation.

2. The method for autonomous early preventative cleaning of ships en route as described in claim 1, characterized in that, The process of building the EIS benchmark database includes: Construct a three-dimensional typical environmental parameter library covering temperature, salinity, and pH values ​​of different water bodies; Based on the three-dimensional typical environmental parameter library, a reference solution library reflecting the three-dimensional environmental parameter characteristics of different water areas is configured. Each reference solution in the reference solution library is used as the test solution to test the EIS impedance value of ship hull material samples without biofilm attachment. Thus, an EIS reference database of ship hull surface without biofilm attachment reflecting typical environmental parameters of different water areas is obtained.

3. The method for autonomous early preventative cleaning of ships en route as described in claim 1, characterized in that, Based on the current EIS impedance value and EIS impedance reference value Determined impedance spectrum deviation for: ,when When the threshold value is greater than or equal to the set critical cleaning threshold, the cleaning operation is initiated. After the hull is fully cleaned, the impedance spectrum deviation rate on the hull surface is sampled again and calculated. If the impedance spectrum deviation rate is less than the critical cleaning threshold, the cleaning operation is considered complete.

4. A method for autonomous early preventative cleaning of ships en route as described in claim 1, characterized in that, The process of electrochemical impedance spectroscopy detection at the current moment includes: performing electrochemical impedance spectroscopy detection once on each of the predefined n typical regions of the ship's surface, and averaging the obtained EIS impedance values ​​to obtain the current EIS impedance value.

5. A method for autonomous early preventative cleaning of ships en route as described in claim 1, characterized in that, The process of obtaining the final location result includes: The environmental state parameter S is: ； ； Soft handover weight for; ； ； The joint positioning result is ; Position deviation is ; The final location result is ; in, Here, h is the weighting coefficient, and h0 is the water level calibration value. For the short window variance of IMU sensor data, The maximum dynamic threshold of the IMU. The maximum depth deviation threshold; Let k be the water depth. It is the signal-to-noise ratio of the UWB measurement signal at time k; These are the maximum signal-to-noise ratio and the minimum signal-to-noise ratio, respectively. This is the coefficient for the steepness of the curve; Base steepness; For adaptive gain; It is the average deviation at multiple moments within the anchor window A; The result of the water positioning at time k; The underwater positioning result at time k; Let be the linear time factor at time t. T represents the total time.

6. A method for autonomous early preventative cleaning of ships en route as described in claim 1, characterized in that, Before weighting the surface and underwater positioning results, the process includes: using the surface positioning results as anchor points and the current environmental state parameters as criteria to adjust the underwater multi-source observation data, thereby updating the underwater positioning results; the process of adjusting the underwater multi-source observation data includes: ； ； in, To adjust the step size; The true angular velocity and true acceleration are derived from the water positioning results; It refers to the zero bias of the IMU gyroscope and the zero bias of the acceleration. This is the current Odom scaling factor; This is due to scale bias. To fuse the true distance increment extracted from the pose within the anchor point window, The distance measured by the odometer within the same window.

7. A ship's autonomous early preventative cleaning system, characterized in that, include: The detection module is configured to acquire the environmental parameters of the vessel in the current waters. When the change in the environmental parameters within a first set time period or the time interval between the current moment and the last electrochemical impedance spectroscopy detection moment meets the set requirements, the current electrochemical impedance spectroscopy is detected to obtain the current EIS impedance value. The start control module is configured to determine the EIS impedance reference value from a pre-built EIS reference database based on the current environmental parameters, and control the start of the cleaning operation based on the current EIS impedance value and the impedance spectrum deviation rate determined by the EIS impedance reference value. The first positioning module is configured to obtain the above-water positioning result and the underwater positioning result respectively based on the multi-source observation data above and below water after the cleaning operation is started. The second positioning module is configured to obtain soft handover weights based on current environmental state parameters, water depth, and water level calibration values, and to weight the surface positioning results and underwater positioning results based on the soft handover weights to obtain joint positioning results. The navigation control module is configured to compensate the joint positioning result based on the positional deviation between the surface positioning result and the underwater positioning result, as well as the linear time factor, to obtain the final positioning result, and then perform navigation control for the cleaning operation based on the final positioning result.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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