Underwater robot autonomous navigation system and method based on multimode optical communication

Abstract

The invention provides an underwater robot autonomous navigation system and method based on multimode optical communication. The method comprises the following steps: arranging a plurality of optical beacons in an underwater preset navigation area; when the underwater robot enters the effective communication range of any optical beacon, receiving the composite vortex light beam of the corresponding optical beacon and collecting a corresponding distortion light spot image sequence; constructing a stability criterion of the variation trend of the relative sight angle between the optical beacon and the underwater robot through the time sequence variation of the modal purity of the composite vortex light beam calculated from the distorted light spot image sequence and the angular velocity information of the underwater robot; obtaining a corrected course angle of the underwater robot in the current navigation route based on a stability criterion; and calculating the confidence navigation position of the underwater robot by combining the corresponding identity code in the optical beacon with the corrected course angle, and performing route guidance. According to the technical scheme provided by the invention, accurate positioning of the navigation route in the autonomous navigation process of the underwater robot can be realized under multimode light field distortion interference.

Description

Technical Field

[0001] This application relates to the field of robot navigation technology, and more specifically, to an autonomous navigation system and method for underwater robots based on multimode optical communication. Background Technology

[0002] Robot navigation is a core supporting technology for robots to achieve autonomous operation. Its development stems from the urgent needs of many fields such as industrial automation, intelligent logistics, medical services, and exploration and rescue. Early navigation relied on preset paths and simple sensors, which could only adapt to structured scenarios and could not meet the needs of complex environments. With the advancement of sensor technology and artificial intelligence, navigation technology has achieved leapfrog development. Today, navigation technology is evolving towards low energy consumption and high robustness. Innovative solutions such as brain-like perception and multimodal information fusion have further expanded the application boundaries of robots in signal blind spots and dynamic complex scenarios.

[0003] In existing robot navigation, the robot first uses LiDAR, cameras, and inertial measurement units to perceive the environment and perform self-localization. Then, based on the target point, the navigation system uses a global path planning algorithm to plan an optimal or suboptimal macroscopic path on the map. Finally, during execution, the local path planner combines real-time perceived obstacle information to perform dynamic obstacle avoidance and local trajectory adjustment, driving the robot to smoothly follow the path until reaching the target point, thus completing autonomous navigation in a dynamic environment. However, in the autonomous navigation of underwater robots based on multimode optical communication, turbulence and suspended objects can cause severe distortion of the multimode optical field, making it difficult for the underwater robot to stably and accurately extract the orientation information for navigation from the severely degraded light spot, thus causing navigation and positioning failure in the autonomous navigation of underwater robots. Therefore, how to achieve accurate positioning of the navigation route during the autonomous navigation of underwater robots under the interference of multimode optical field distortion has become a difficult problem for the industry. Summary of the Invention

[0004] This application provides an autonomous navigation system and method for underwater robots based on multimode optical communication, which can achieve accurate positioning of the navigation route during the autonomous navigation process of underwater robots under the interference of multimode optical field distortion.

[0005] In a first aspect, this application provides an autonomous navigation method for underwater robots based on multimode optical communication, comprising the following steps: Multiple optical beacons with known global coordinate systems are deployed within a pre-defined underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code. When the underwater robot enters the effective communication range of any optical beacon, it receives the composite vortex beam of the corresponding optical beacon through the optical antenna array and acquires the image sequence of distorted light spots from the optical beacon. The modal purity of the composite vortex beam is calculated from the distorted spot image sequence, and a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot is constructed by the temporal variation of the modal purity and the angular velocity information of the underwater robot. Based on the stability criterion, the accumulated heading angle error of the inertial measurement unit in the underwater robot is fed back and corrected, thereby generating the corrected heading angle of the underwater robot in the current navigation route; The underwater robot's confident navigation position in the global coordinate system is calculated by combining the corresponding identity code in the optical beacon with the corrected heading angle, and then the underwater robot is guided to its route based on the confident navigation position.

[0006] In some embodiments, deploying multiple optical beacons with known global coordinate systems within a predetermined underwater navigation area specifically includes: Calibrate the global coordinate system parameters of the preset underwater navigation area; The deployment locations of multiple optical beacons are determined based on the global coordinate system parameters; At each deployment point, a light beacon carrier is fixed, and each light beacon carrier is assigned a unique identification code; A composite vortex beam is configured for each optical beacon carrier with a unique identification code to complete the deployment and activation of the optical beacon.

[0007] In some embodiments, when the underwater robot enters the effective communication range of any optical beacon, receiving the composite vortex beam of the corresponding optical beacon through an optical antenna array and acquiring a sequence of distorted light spot images from the optical beacon specifically includes: The underwater robot monitors the light intensity of the emitted signals from surrounding optical beacons in real time through a light intensity detection module to determine whether it has entered the effective communication range of any optical beacon. When the underwater robot enters the effective communication range of any optical beacon, the optical antenna array on the underwater robot is activated to capture and receive the composite vortex beam in the direction of the optical beacon. Using an image acquisition unit coupled to an optical antenna array, the distorted light spot formed during the reception of the composite vortex beam is continuously sampled to generate a sequence of distorted light spot images of the optical beacon.

[0008] In some embodiments, calculating the modal purity of the composite vortex beam from the distorted spot image sequence specifically includes: The distorted spot image sequence is subjected to grayscale normalization processing to obtain a standardized spot image sequence; The angular spectral distribution corresponding to each standardized spot image in the standardized spot image sequence is extracted by Fourier transform; Calculate the normalized correlation coefficients between the angular spectral distributions of each direction and the standard angular spectral distributions of the ideal modes corresponding to the composite vortex beams; The modal purity of the composite vortex beam is calculated based on all normalized correlation coefficients.

[0009] In some embodiments, the stability criterion for constructing the relative line-of-sight angle change trend between the optical beacon and the underwater robot by using the temporal variation of the modal purity and the angular velocity information of the underwater robot specifically includes: Perform time-series difference operation on the time-series data of the modal purity to obtain the time-series change of the modal purity; The angular velocity data of the underwater robot is collected and then processed by adaptive noise suppression to obtain effective angular velocity information; A coupled model of the temporal variation of the modal purity and the effective angular velocity information is constructed, and the coupling coefficient of the model is determined by least squares fitting; Based on the coupling model and the coupling coefficient, a stable boundary threshold for the relative line-of-sight angle change is set to form a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot.

[0010] In some embodiments, the feedback correction of the accumulated heading angle error of the inertial measurement unit in the underwater robot based on the stability criterion, and the generation of the corrected heading angle of the underwater robot in the current navigation route, specifically includes: The stability criterion is used to determine the stable state of the relative line-of-sight angle change trend between the optical beacon and the underwater robot; When the steady state is an unstable state, the accumulated heading angle error of the inertial measurement unit is extracted; Based on the determination result of the stability criterion, the error correction gain corresponding to the heading angle of the underwater robot is determined; The accumulated heading angle error is corrected by using the error correction gain to generate the corrected heading angle of the underwater robot in the current navigation route.

[0011] In some embodiments, the image acquisition unit consists of an area array image sensor and a timing control circuit.

[0012] Secondly, this application provides an autonomous navigation system for underwater robots based on multimode optical communication, used to execute an autonomous navigation method for underwater robots based on multimode optical communication. The system includes: The deployment module is used to deploy multiple optical beacons with known global coordinate systems within a preset underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code. The processing module is used to receive the composite vortex beam of the corresponding optical beacon through an optical antenna array and acquire the distorted light spot image sequence from the optical beacon when the underwater robot enters the effective communication range of any optical beacon. The processing module is also used to calculate the modal purity of the composite vortex beam from the distorted spot image sequence, and to construct a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot by using the temporal change of the modal purity and the angular velocity information of the underwater robot. The processing module is also used to perform feedback correction on the heading angle error accumulated by the inertial measurement unit in the underwater robot based on the stability criterion, thereby generating the corrected heading angle of the underwater robot in the current navigation route; The execution module is used to calculate the confidence navigation position of the underwater robot in the global coordinate system by combining the identity code corresponding to the optical beacon with the corrected heading angle, and then guide the underwater robot to a route based on the confidence navigation position.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described autonomous navigation method for underwater robots based on multimode optical communication.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described autonomous navigation method for underwater robots based on multimode optical communication.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The underwater robot autonomous navigation system and method based on multimode optical communication provided in this application firstly deploys multiple optical beacons with known global coordinate systems within a preset underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code. Secondly, when the underwater robot enters the effective communication range of any optical beacon, it receives the composite vortex beam of the corresponding optical beacon through an optical antenna array and acquires a sequence of distorted light spot images from the optical beacon. Further, the modal purity of the composite vortex beam is calculated from the distorted light spot image sequence, and the modal purity is then analyzed using time... The stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot is constructed by combining the sequence change amount with the angular velocity information of the underwater robot. Then, based on the stability criterion, the accumulated heading angle error of the inertial measurement unit in the underwater robot is corrected by feedback, thereby generating the corrected heading angle of the underwater robot in the current navigation route. Finally, the confidence navigation position of the underwater robot in the global coordinate system is calculated by combining the corresponding identity code in the optical beacon with the corrected heading angle, and then the underwater robot is guided to the route based on the confidence navigation position.

[0016] Therefore, this application demonstrates that it can achieve precise positioning of the navigation route during the autonomous navigation process of an underwater robot under multimode optical field distortion interference. Firstly, by deploying composite vortex beam optical beacons with unique identification codes in a predetermined underwater area and calibrating their global coordinate system coordinates, a distributed and identifiable navigation reference source is provided for the underwater robot. Secondly, the underwater robot receives the composite vortex beam and acquires a sequence of distorted light spot images through an optical antenna array. The optical antenna array ensures effective capture of weak underwater light signals, while the distorted light spot image sequence, as intuitive visual data of the beam transmission state, provides a continuous and effective data source for subsequent modal purity calculation, realizing the transformation of the optical beacon signal from reception to data. Furthermore, modal purity is calculated from the distorted light spot image sequence, and a stability criterion for the relative line-of-sight angle change trend is constructed by combining the robot's angular velocity information. Modal purity achieves a quantitative characterization of the degree of distortion in the transmission of the composite vortex beam, while the construction of the stability criterion correlates the beam transmission characteristics with the robot's motion state. A quantitative standard for evaluating the stability of the relative position changes between the optical beacon and the robot has been established, avoiding the limitations of traditional underwater navigation that relies solely on single-state evaluation using inertial sensors, and providing a precise basis for heading error correction. Then, based on stability criteria, the heading angle error of the inertial measurement unit (IMU) is corrected by feedback, and a corrected heading angle is generated to effectively eliminate the cumulative drift error caused by the IMU's long-term operation. The corrected heading angle more closely matches the robot's actual navigation direction, significantly improving the heading accuracy of underwater robot navigation and solving the core drift problem of underwater inertial navigation. Finally, the confidence navigation position is calculated by combining the optical beacon identification code with the corrected heading angle to accurately locate the navigation route during the underwater robot's autonomous navigation process, and autonomous navigation is achieved based on the confidence navigation position, effectively improving the robustness of the underwater robot's autonomous navigation in complex environments. In summary, the technical solution provided in this application can achieve accurate positioning of the navigation route during the autonomous navigation process of an underwater robot under multimode optical field distortion interference. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of an autonomous navigation method for underwater robots based on multimode optical communication, according to some embodiments of this application. Figure 2 This is an exemplary flowchart illustrating the determination of modal purity according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of an underwater robot autonomous navigation system based on multimode optical communication, according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing an autonomous navigation method for underwater robots based on multimode optical communication, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 This figure is an exemplary flowchart of an autonomous navigation method for underwater robots based on multimode optical communication, according to some embodiments of this application. The figure mainly includes the following steps: In step S101, multiple optical beacons with known global coordinate systems are deployed in a preset underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code.

[0020] In some embodiments, deploying multiple optical beacons with known global coordinate systems within a predetermined underwater navigation area is achieved through the following steps: Calibrate the global coordinate system parameters of the preset underwater navigation area; The deployment locations of multiple optical beacons are determined based on the global coordinate system parameters; At each deployment point, a light beacon carrier is fixed, and each light beacon carrier is assigned a unique identification code; A composite vortex beam is configured for each optical beacon carrier with a unique identification code to complete the deployment and activation of the optical beacon.

[0021] In practice, firstly, acoustic beacons with known initial positions are deployed around the pre-defined underwater navigation area. Distance data between the acoustic beacons is obtained through acoustic ranging. Combined with geodetic reference data obtained from GPS, the acoustic beacon coordinates are unified to the geodetic coordinate system using existing spatial rectangular coordinate system transformation. This allows for the calibration of the global coordinate system parameters of the pre-defined underwater navigation area. These global coordinate system parameters refer to the parameters used to describe the reference coordinate system of the pre-defined underwater navigation area, including the origin position and coordinate axis orientation. Secondly, using the calibrated global coordinate system parameters as input, and combining the pre-defined effective communication distance of the optical beacons (which can be obtained from the specifications of the corresponding optical module), the Voronoi diagram partitioning algorithm is used to divide the navigation area into multiple non-overlapping and completely covered sub-regions. The center of each sub-region is used as the deployment point, thereby determining the deployment points of multiple optical beacons. (Details omitted here.) The Voronoi diagram partitioning algorithm refers to the algorithm based on the distance of a spatial point set. The algorithm utilizes a known method to divide three-dimensional space into specific sub-regions. Within each sub-region, the distance from any point to a corresponding candidate point is less than the distance to other candidate points. In this embodiment, the deployment points refer to the spatial coordinates used to fix the optical beacon carriers in the global coordinate system. Then, underwater equipment is used to deploy the optical beacon carriers to the corresponding deployment points. Simultaneously, a unique, non-repeating binary sequence is assigned to each optical beacon carrier using binary encoding (i.e., converting the marker values ​​of each optical beacon carrier into binary codes of equal length), forming a unique identification code for each optical beacon carrier. This unique identification code is a specific binary sequence set to distinguish different optical beacons, used by the underwater robot to identify the corresponding optical beacon. Finally, a composite vortex beam is configured for each optical beacon carrier with a unique identification code to complete the deployment and activation of the optical beacon. The composite vortex beam is formed by superimposing multiple vortex beams with different topological charge numbers emitted by a computer-controlled spatial light modulator, which will not be elaborated here.

[0022] It should be noted that, in this application, a composite vortex beam refers to a structured beam formed by superimposing two or more single-mode vortex beams with different topological charges through optical modulation. In underwater multimode optical communication, the combination mode of the topological charge of the composite vortex beam and its phase distribution are used to map the unique identity code of the optical beacon. By determining the composite vortex beam, the underwater robot can receive the beam and identify the identity of the corresponding optical beacon, thereby obtaining the known global coordinate system coordinates of the optical beacon and providing basic reference information for subsequent navigation position calculation.

[0023] In step S102, when the underwater robot enters the effective communication range of any optical beacon, it receives the composite vortex beam of the corresponding optical beacon through the optical antenna array and acquires the distorted light spot image sequence from the optical beacon.

[0024] In some implementations, when an underwater robot enters the effective communication range of any optical beacon, the following steps are used to receive the composite vortex beam from the corresponding optical beacon via an optical antenna array and acquire a sequence of distorted light spot images from the beacon: The underwater robot monitors the light intensity of the emitted signals from surrounding optical beacons in real time through a light intensity detection module to determine whether it has entered the effective communication range of any optical beacon. When the underwater robot enters the effective communication range of any optical beacon, the optical antenna array on the underwater robot is activated to capture and receive the composite vortex beam in the direction of the optical beacon. Using an image acquisition unit coupled to an optical antenna array, the distorted light spot formed during the reception of the composite vortex beam is continuously sampled to generate a sequence of distorted light spot images of the optical beacon.

[0025] In practice, the light intensity detection module on the underwater robot consists of a silicon photodiode-type photodetector and a low-noise signal conditioning circuit. The photodetector converts the received composite vortex beam light signal emitted by the optical beacon into an analog electrical signal. The signal conditioning circuit sequentially performs programmable amplification and bandpass filtering on this analog electrical signal to filter out noise introduced by underwater background light and electromagnetic interference. The processed analog electrical signal is then converted into a digital light intensity signal by an analog-to-digital converter and transmitted to the robot's main control unit. The main control unit uses a threshold comparison algorithm to compare the amplitude of the real-time digital light intensity signal with the effective communication light intensity threshold of the optical beacon, pre-calibrated through underwater light transmission experiments, frame by frame. If the amplitude of the digital light intensity signal is higher than the effective communication light intensity threshold of the optical beacon for multiple consecutive sampling periods, it is determined that the underwater robot has entered the effective communication range of the corresponding optical beacon. The effective communication range refers to the range within which the light intensity of the composite vortex beam emitted by the optical beacon can meet the requirements during underwater transmission. The system first determines the spatial range within which the underwater robot can reliably receive optical signals. Secondly, when the underwater robot enters the effective communication range of any optical beacon, the optical antenna array mounted on the underwater robot is activated to capture and receive the composite vortex beam from the optical beacon. Finally, an image acquisition unit coupled to the optical antenna array continuously samples the distorted light spots formed during the reception of the composite vortex beam, generating a sequence of distorted light spot images of the optical beacon. The image acquisition unit, coupled to the optical antenna array, consists of an area array image sensor and a timing control circuit. The timing control circuit sends an exposure trigger signal to the area array image sensor according to a preset sampling frequency. The area array image sensor continuously performs photoelectric conversion on the distorted light spots formed on the imaging focal plane by the composite vortex beam received by the optical antenna array, converting the spatial distribution of the light intensity of the light spots into distorted light spot images, ultimately generating a sequence of distorted light spot images of the optical beacon arranged in the order of sampling time.

[0026] It should be noted that the distorted spot image sequence in this application refers to a set of distorted spot images that are continuously recorded in the sampling sequence after the composite vortex beam has been distorted during underwater transmission. The distorted spot image refers to a digital image of the composite vortex beam deviating from the ideal mode spot shape after wavefront distortion caused by the underwater environment. During underwater transmission, the composite vortex beam will experience wavefront distortion due to environmental factors such as water turbulence, scattering of suspended particles, and temperature gradient refraction, causing the spot formed on the imaging focal plane of the optical antenna array to deviate from the ideal shape (i.e., distorted spot), and the degree of distortion will vary with the water level. The relative line-of-sight angle between the robot and the optical beacon changes dynamically. The modal purity and the trend of the relative line-of-sight angle change required for navigation cannot be directly extracted from the light intensity or phase electrical signal of the optical signal. Furthermore, since the degree of light spot distortion exhibits regular temporal fluctuations with the changes in the relative attitude (such as angular velocity and line-of-sight angle) between the robot and the optical beacon, determining the image sequence of distorted light spots can completely record this dynamic change. Combined with the robot's angular velocity information, a stability criterion for the trend of relative line-of-sight angle change can be constructed, providing core criterion support for the heading angle error correction of the inertial measurement unit.

[0027] In step S103, the modal purity of the composite vortex beam is calculated from the distorted light spot image sequence, and a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot is constructed by using the temporal variation of the modal purity and the angular velocity information of the underwater robot.

[0028] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flowchart of determining modal purity according to some embodiments of this application. In this embodiment, the modal purity of the composite vortex beam can be calculated from the distorted spot image sequence using the following steps: In step S1031, the distorted spot image sequence is subjected to grayscale normalization processing to obtain a standardized spot image sequence; In step S1032, the angular spectral distribution corresponding to each standardized spot image in the standardized spot image sequence is extracted by Fourier transform; In step S1033, the normalized correlation coefficient between the angular spectral distribution of each direction and the standard angular spectral distribution of the ideal mode corresponding to the composite vortex beam is calculated; In step S1034, the modal purity of the composite vortex beam is calculated based on all normalized correlation coefficients.

[0029] In specific implementation, firstly, for each frame of the distorted spot image sequence, a linear gray-level normalization algorithm is applied. This involves first statistically analyzing the maximum and minimum gray values ​​of each frame of the distorted spot image pixel by pixel. Then, a linear transformation is performed on the pixel gray values ​​using the gray-level normalization formula (i.e., subtracting the minimum value from the gray value of each pixel and dividing by the difference between the maximum and minimum values). This eliminates gray-level deviations caused by underwater background light intensity fluctuations and differences in image sensor response, resulting in a standardized spot image sequence where pixel gray values ​​are distributed within a uniform range. The standardized spot image sequence refers to the time-series set of distorted spot images after gray-level normalization. Secondly, for each frame of the standardized spot image sequence, a polar coordinate transformation algorithm is used to convert the spot image in the Cartesian coordinate system to an image in the polar coordinate system (i.e., mapping radial pixel columns to polar radius dimensions and angular pixel rows to polar angle dimensions, with the spot centroid as the pole and the spot radius as the polar axis). Then, the angular values ​​of the image in the polar coordinate system are... A one-dimensional fast Fourier transform is performed in the angular dimension. The frequency components and amplitude distributions corresponding to each standardized spot image are extracted through the frequency domain decomposition characteristics of the Fourier transform. This yields the angular spectral distributions corresponding to each standardized spot image in the standardized spot image sequence. The angular spectral distributions refer to the frequency domain feature distributions obtained after Fourier transform of the vortex beam spot image in the angular dimension, which reflect the change law of the beam's angular phase. Then, the standard angular spectral distributions of the ideal mode corresponding to the composite vortex beam, which are obtained in advance through theoretical simulation and laboratory calibration, are acquired. These standard angular spectral distributions are used as inputs, and the correlation between them is calculated using the Pearson correlation coefficient algorithm. Specifically, the mean and covariance of the angular spectral distributions and the standard angular spectral distributions are calculated separately. Then, the normalized correlation coefficient, which takes values ​​in the range of [-1, 1], is obtained according to the Pearson correlation coefficient formula (i.e., the covariance divided by the product of the standard deviations of the two spectral distributions). The normalized correlation coefficient is an indicator that measures the degree of linear correlation between the two spectral distributions.Finally, based on the normalized correlation coefficients corresponding to all normalized spot images in the normalized spot image sequence, the mean of all normalized correlation coefficients is calculated using an arithmetic mean algorithm. Then, combining the theoretical mapping relationship between the modal purity of the vortex beam and the normalized correlation coefficient (i.e., modal purity equals the square of the normalized correlation coefficient), the mean is squared to obtain the modal purity of the composite vortex beam. The essence of modal purity is the proportion of the energy of the target ideal mode in the actual transmitted composite vortex beam to the total beam energy, with a value range of [0,1]. The normalized correlation coefficient only measures the linear similarity between the actual angular spectrum and the ideal spectrum, and cannot directly reflect the energy proportion. According to basic signal processing theory, the square of the normalized correlation coefficient can characterize the variance explanation ratio between two signals. In the frequency domain analysis of the vortex beam, this squared value precisely corresponds to the proportion of the energy of the ideal mode in the total energy of the actual beam. Therefore, the square of the mean can be transformed into an energy proportion index that conforms to the definition of modal purity.

[0030] It should be noted that, in this application, modal purity refers to the degree of agreement between the actual transmitted composite vortex beam and the ideal modal beam in terms of modal characteristics. It is a core indicator for measuring the degree of modal distortion during beam transmission. In robot autonomous navigation (especially in underwater optical communication navigation scenarios), modal purity, as a core quantitative indicator characterizing the degree of agreement between the actual transmitted mode and the ideal mode of the composite vortex beam, is a key link connecting optical signal perception and accurate calculation of navigation parameters. By combining the temporal variation of modal purity with the underwater robot's angular velocity information, a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the robot can be constructed. This criterion is the core basis for feedback correction of the heading angle error accumulated by the inertial measurement unit, which can effectively eliminate the drift error of inertial navigation.

[0031] In some embodiments, the stability criterion for constructing the relative line-of-sight angle change trend between the optical beacon and the underwater robot by using the temporal variation of the modal purity and the angular velocity information of the underwater robot is achieved through the following steps: Perform time-series difference operation on the time-series data of the modal purity to obtain the time-series change of the modal purity; The angular velocity data of the underwater robot is collected and then processed by adaptive noise suppression to obtain effective angular velocity information; A coupled model of the temporal variation of the modal purity and the effective angular velocity information is constructed, and the coupling coefficient of the model is determined by least squares fitting; Based on the coupling model and the coupling coefficient, a stable boundary threshold for the relative line-of-sight angle change is set to form a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot.

[0032] In specific implementation, firstly, the temporal data of the modal purity is acquired. This temporal data refers to the set of modal purity values ​​at different sampling times. A first-order backward difference algorithm is used for temporal difference calculation, subtracting the modal purity of the previous sampling time from the current sampling time, and calculating the modal purity difference between adjacent times point by point. All modal purity differences are arranged according to the sampling time sequence to obtain the temporal variation of the modal purity. This temporal variation of modal purity refers to the sequence of differences in the modal purity of the composite vortex beam at different sampling times, reflecting the dynamic change of modal purity over time. Secondly, the micromechanical gyroscope in the inertial measurement unit carried by the underwater robot... A gyroscope collects angular velocity data from the underwater robot and inputs this data into an existing adaptive Wiener filtering algorithm for noise suppression to obtain effective angular velocity information. This effective angular velocity information refers to data that accurately reflects the underwater robot's motion angular velocity after noise suppression. Then, a coupled model is constructed with the temporal variation of modal purity as input and the effective angular velocity information as output. The temporal variation of modal purity and the effective angular velocity information are mapped one-to-one according to the sampling time sequence to form a fitting dataset. A least-squares fitting algorithm is used to fit this dataset, and by minimizing the sum of squared residuals between the fitted values ​​and the actual values, a solution is found that minimizes the sum of squared residuals. The model parameters that reach their minimum value are used as model coupling coefficients. These coupling coefficients are numerical parameters characterizing the linear correlation strength between the temporal variation of modal purity and the effective angular velocity information. The underwater robot's angular velocity reflects its rotational speed; a higher angular velocity leads to more drastic changes in the relative line-of-sight angle between the robot and the fixed optical beacon, causing the beam transmission path to frequently switch to different spatial locations in the water, resulting in a shift from static to dynamic distortion. Modal purity depends on the angular spectrum calculation of the distorted beam spot. Dynamic distortion causes drastic fluctuations in the degree of beam spot distortion at different times, ultimately increasing the temporal variation of modal purity. Therefore, a model coupling coefficient can be constructed... A linear coupling model of the temporal variation of modal purity and effective angular velocity information is described. Finally, based on the coupling model and the determined model coupling coefficient, the output results of the coupling model are statistically analyzed using the 3-standard-deviation statistical threshold method to determine the stable boundary threshold of the relative line-of-sight angle change (i.e., the threshold range determined by the 3-standard-deviation statistical threshold method). The rule that "when the output value of the coupling model is within the stable boundary threshold range, the relative line-of-sight angle change trend is determined to be stable, and when it exceeds the threshold range, the relative line-of-sight angle change trend is determined to be unstable" is used as the judgment basis, and finally the stability criterion of the relative line-of-sight angle change trend between the optical beacon and the underwater robot is formed.

[0033] It should be noted that the stability criterion in this application refers to the judgment rule used to determine whether the trend of the relative line-of-sight angle between the optical beacon and the underwater robot is stable. The underwater robot relies on the inertial measurement unit to obtain angular velocity information to calculate the heading. However, the inertial measurement unit is prone to cumulative drift error due to long-term operation. Moreover, when the composite vortex beam is transmitted underwater, it is affected by water turbulence, scattering of suspended particles and robot motion. The relative line-of-sight angle between the optical beacon and the robot is prone to irregular fluctuations. Without a quantitative judgment standard, it is impossible to distinguish whether the change in the line-of-sight angle is caused by environmental interference or error of the inertial measurement unit, and it is also impossible to predict the stability of the optical communication link in time. Therefore, by determining the stability criterion, it is possible to ensure that the underwater robot has accurate and reliable navigation basis in complex water environment and improve the robustness of the overall navigation system.

[0034] In step S104, the heading angle error accumulated by the inertial measurement unit in the underwater robot is corrected by feedback based on the stability criterion, thereby generating the corrected heading angle of the underwater robot in the current navigation route.

[0035] In some embodiments, the following steps are used to perform feedback correction on the accumulated heading angle error of the inertial measurement unit in the underwater robot based on the stability criterion, thereby generating the corrected heading angle of the underwater robot in the current navigation route: The stability criterion is used to determine the stable state of the relative line-of-sight angle change trend between the optical beacon and the underwater robot; When the steady state is an unstable state, the accumulated heading angle error of the inertial measurement unit is extracted; Based on the determination result of the stability criterion, the error correction gain corresponding to the heading angle of the underwater robot is determined; The accumulated heading angle error is corrected by using the error correction gain to generate the corrected heading angle of the underwater robot in the current navigation route.

[0036] In specific implementation, firstly, the current output value of the coupled model is compared with the stability boundary threshold of the stability criterion. If the current output value is within the stability boundary threshold range, the relative line-of-sight angle change trend is determined to be in a stable state; if the current output value exceeds the stability boundary threshold range, it is determined to be in an unstable state. The stable state refers to the determination result that the relative line-of-sight angle change trend between the optical beacon and the underwater robot conforms to the normal fluctuation law. Secondly, when the stable state is unstable, the error storage register of the inertial measurement unit is called to extract the recorded angular velocity sampling deviation data, and integral backtracking is used. The algorithm traces back the angular velocity sampling deviation data from the current moment, and calculates the accumulated heading angle error of the inertial measurement unit (IMU) from the last correction to the current moment by performing time integration on the angular velocity deviation. This accumulated heading angle error refers to the gradually accumulated heading angle calculation deviation value of the IMU during continuous operation. Then, it retrieves the stability criterion judgment results pre-calibrated through multiple sets of actual flight tests and the correction gain mapping table (e.g., deviation from the 3σ stability boundary threshold ≤ 0% (within the threshold range), corresponding to an error correction gain of 0, indicating a stable state requiring no compensation; deviation from the 3σ stability boundary threshold (0%)... The deviation from the 3σ stability boundary threshold is 5%, with a corresponding error correction gain of 0.2, indicating a weak disturbance. Deviations of 5% to 10% correspond to an error correction gain of 0.3, indicating a moderate to weak disturbance. Deviations of 10% to 20% correspond to an error correction gain of 0.5, indicating a moderate to strong disturbance. Deviations of more than 20% correspond to an error correction gain of 0.8, indicating a strong disturbance. A lookup table interpolation algorithm is used to find the corresponding error correction gain in the mapping table based on the specific determination result of the current stability criterion (e.g., the specific magnitude of the deviation from the threshold). The error correction gain refers to the proportional coefficient used to adjust the heading angle error correction amplitude, and its value is positively correlated with the instability of the relative line-of-sight angle change. Finally, a proportional-integral (PI) feedback control algorithm is adopted, using the determined error correction gain as the proportional term coefficient of the PI controller, and the accumulated heading angle error as the input deviation of the controller. The deviation is amplified by the proportional operation of the PI controller, and the steady-state error is eliminated by the integral operation. The corresponding error compensation value is output, and then the original heading angle currently output by the inertial measurement unit is subtracted from the error compensation value to finally generate the corrected heading angle of the underwater robot in the current navigation route.

[0037] It should be noted that the corrected heading angle in this application refers to the heading angle value that truly reflects the actual navigation direction of the underwater robot. In the prior art, the heading angle error correction of the inertial measurement unit relies on fixed time intervals and fixed error thresholds to trigger and determine the correction gain, which has technical limitations. This embodiment uses a dynamic correction mechanism with the stability criterion constructed by coupling the temporal change of the purity of the composite vortex beam mode with the effective angular velocity information of the underwater robot as the core. The stable state of the relative line-of-sight angle change trend between the optical beacon and the underwater robot is quantitatively determined by the 3σ statistical threshold method, replacing the traditional correction trigger logic. This effectively solves the cumulative problem of heading drift in the inertial navigation system and avoids the problems of over-correction or under-correction that are prone to occur in traditional correction methods.

[0038] In step S105, the confidence navigation position of the underwater robot in the global coordinate system is calculated by combining the identity code corresponding to the optical beacon with the corrected heading angle, and then the underwater robot is guided to a route based on the confidence navigation position.

[0039] In some embodiments, the following steps are used to calculate the confident navigation position of the underwater robot in the global coordinate system by combining the corresponding identity code in the optical beacon with the corrected heading angle: The global coordinates of the corresponding optical beacon are obtained based on the composite vortex beam of the optical beacon. By combining the corrected heading angle with the received azimuth data of the optical antenna array, the azimuth angle and distance information of the underwater robot relative to the optical beacon are calculated; Based on the global coordinate system coordinates, relative azimuth angle, and distance information of the optical beacon, a global coordinate transformation model is constructed; The confidence navigation position of the underwater robot in the global coordinate system is calculated using the global coordinate transformation model.

[0040] In specific implementation, firstly, the association database between the optical beacon identification code and the global coordinate system coordinates (i.e., the database storing the optical beacon identification code and the global coordinate system coordinates) is retrieved. The global coordinate system coordinates of the corresponding optical beacon in the global coordinate system are obtained from this database. The global coordinate system coordinates refer to the spatial position coordinates of the optical beacon in the geodetic coordinate system, serving as the reference point for navigation and positioning. Secondly, the error-corrected heading angle data is compared with the receiving azimuth data of the optical antenna array (i.e., the azimuth data obtained by adjusting the phase delay of each antenna element in the optical antenna array). Using the calculated azimuth as input, a spherical trigonometric algorithm is employed. With the optical beacon as a reference point, the underwater robot's carrier coordinate system is transformed into a local polar coordinate system centered on the optical beacon. The relative azimuth angle of the underwater robot relative to the optical beacon is obtained by calculating the angular rotation relationship between the carrier coordinate system and the local polar coordinate system. Simultaneously, based on the Lambertian attenuation model for underwater optical transmission, the measured intensity value of the composite vortex beam from the intensity detection module and the initial intensity value from the optical beacon's transmitter are substituted into the model. A nonlinear least squares inversion algorithm is used to calculate the optical path length of the beam transmission, yielding the underwater... The straight-line distance between the robot and the optical beacon (i.e., distance information) refers to the angular position and straight-line distance parameters of the underwater robot relative to the optical beacon in the local coordinate system. Then, based on the homogeneous coordinate transformation theory of spatial analytic geometry, a local polar coordinate system is established with the global coordinate system coordinates of the optical beacon as the origin of the local coordinate system and the direction from the optical beacon to the underwater robot as the polar axis. A transformation matrix is ​​constructed from the local polar coordinate system to the global geodetic coordinate system. This transformation matrix includes a rotation matrix and a translation vector. The rotation matrix... The relative azimuth angle and the attitude angle difference in the global coordinate system are used to determine the translation vector, which is the global coordinate system coordinate of the optical beacon. Matrix multiplication is used to fuse the rotation matrix and the translation vector to form a global coordinate transformation model that describes the mapping relationship between local and global coordinates. This global coordinate transformation model refers to the mathematical transformation model that realizes the mapping from the local coordinate system of the optical beacon to the global coordinate system of underwater navigation. Finally, the relative azimuth angle and distance information are substituted into the global coordinate transformation model, and the confident navigation position of the underwater robot in the global coordinate system is calculated through matrix multiplication.

[0041] It should be noted that the confidence navigation position in this application refers to the reliable spatial position of the underwater robot in the global coordinate system. Determining the confidence navigation position can provide accurate position calculation results for underwater robot navigation, avoid using erroneous positions affected by water turbulence and optical communication link distortion as navigation references, significantly reduce the risk of navigation errors caused by positioning deviations, and significantly improve the overall accuracy and robustness of multi-beacon fusion positioning.

[0042] In some embodiments, the underwater robot is guided to a route based on the confident navigation location using the following steps: Extract the coordinate deviation between the confident navigation position and the preset navigation target position, and generate real-time path planning instructions by combining the underwater robot motion constraint parameters; The real-time path planning instructions are converted into motion control instructions, and the actuators of the underwater robot are driven to move based on the motion control instructions to provide route guidance.

[0043] In specific implementation, firstly, the three-dimensional coordinates of the global coordinate system of the confident navigation position are subtracted component by component from the three-dimensional coordinates of the global coordinate system of the preset navigation target position to obtain the combination of position differences along the three axes of the global coordinate system. This yields the coordinate deviation between the confident navigation position and the preset navigation target position. The coordinate deviation refers to a quantitative parameter characterizing the positional difference between the underwater robot's current position and the target position in the three axes of the global coordinate system. Subsequently, the motion constraint parameters of the underwater robot (including the maximum propulsion speed calibrated by the power system performance, the minimum turning radius limited by the mechanical structure, and the maximum steering angular velocity determined by the attitude control system) are retrieved, and the coordinate deviation and the motion constraint parameters are input into the existing... The A* path planning algorithm performs real-time path planning, generating real-time path planning instructions consisting of a sequence of continuous path nodes. These instructions are control instruction sets containing the optimal motion path nodes from the underwater robot's current position to its target position. Then, the underwater robot's central processing unit converts these real-time path planning instructions into motion control instructions. These instructions specify the motion parameters of the underwater robot's actuators and are transmitted to the underwater robot's actuator controller via the CAN bus communication protocol. Upon receiving the instructions, the controller drives the thruster motors to propel the underwater robot along the planned path, completing route guidance based on the confidence navigation position.

[0044] It should be noted that, in this application, the actuator refers to the mechanical component in an underwater robot responsible for achieving motion propulsion and attitude adjustment, and is the power execution unit for the movement of the underwater robot.

[0045] Furthermore, in another aspect of this application, in some embodiments, this application provides an autonomous navigation system for underwater robots based on multimode optical communication, referencing... Figure 3 The figure is a schematic diagram of the structure of an underwater robot autonomous navigation system based on multimode optical communication according to some embodiments of this application. The underwater robot autonomous navigation system based on multimode optical communication includes: a deployment module 201, a processing module 202, and an execution module 203, which are described below: The deployment module 201 in this application is mainly used to deploy multiple optical beacons with known global coordinate systems in a preset underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code. The processing module 202 in this application is mainly used to receive the composite vortex beam of the corresponding optical beacon through an optical antenna array when the underwater robot enters the effective communication range of any optical beacon, and to collect the distorted light spot image sequence from the optical beacon. The processing module 202 is further configured to calculate the modal purity of the composite vortex beam from the distorted spot image sequence, and construct a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot by using the temporal change of the modal purity and the angular velocity information of the underwater robot. In addition, the processing module 202 is also used to perform feedback correction on the heading angle error accumulated by the inertial measurement unit in the underwater robot based on the stability criterion, thereby generating the corrected heading angle of the underwater robot in the current navigation route; The execution module 203 in this application is mainly used to calculate the confidence navigation position of the underwater robot in the global coordinate system by combining the identity code corresponding to the optical beacon with the corrected heading angle, and then guide the underwater robot to a route based on the confidence navigation position.

[0046] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described autonomous navigation method for underwater robots based on multimode optical communication.

[0047] In some embodiments, reference Figure 4 This figure is a schematic diagram of the structure of a computer device implementing an autonomous navigation method for an underwater robot based on multimode optical communication, according to some embodiments of this application. The autonomous navigation method for an underwater robot based on multimode optical communication in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0048] The processor 301 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the underwater robot autonomous navigation method based on multimode optical communication in this application.

[0049] The communication bus 302 can be used to transmit information between the aforementioned components.

[0050] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0051] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the autonomous navigation method for underwater robots based on multimode optical communication can be achieved by the processor 301 and one or more software modules in the program code in the memory 303.

[0052] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0053] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0054] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0055] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described autonomous navigation method for underwater robots based on multimode optical communication.

[0056] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An autonomous navigation method for underwater robots based on multimode optical communication, characterized in that, Includes the following steps: Multiple optical beacons with known global coordinate systems are deployed within a pre-defined underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code. When the underwater robot enters the effective communication range of any optical beacon, it receives the composite vortex beam of the corresponding optical beacon through the optical antenna array and acquires the image sequence of distorted light spots from the optical beacon. The modal purity of the composite vortex beam is calculated from the distorted spot image sequence, and a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot is constructed by the temporal variation of the modal purity and the angular velocity information of the underwater robot. Based on the stability criterion, the accumulated heading angle error of the inertial measurement unit in the underwater robot is fed back and corrected, thereby generating the corrected heading angle of the underwater robot in the current navigation route; The underwater robot's confident navigation position in the global coordinate system is calculated by combining the corresponding identity code in the optical beacon with the corrected heading angle, and then the underwater robot is guided to its route based on the confident navigation position.

2. The method as described in claim 1, characterized in that, The deployment of multiple optical beacons with known global coordinate systems within a pre-defined underwater navigation area specifically includes: Calibrate the global coordinate system parameters of the preset underwater navigation area; The deployment locations of multiple optical beacons are determined based on the global coordinate system parameters; At each deployment point, a light beacon carrier is fixed, and each light beacon carrier is assigned a unique identification code; A composite vortex beam is configured for each optical beacon carrier with a unique identification code to complete the deployment and activation of the optical beacon.

3. The method as described in claim 1, characterized in that, When the underwater robot enters the effective communication range of any optical beacon, it receives the composite vortex beam from the corresponding optical beacon through an optical antenna array and acquires a sequence of distorted light spot images from the optical beacon. Specifically, this includes: The underwater robot monitors the light intensity of the emitted signals from surrounding optical beacons in real time through a light intensity detection module to determine whether it has entered the effective communication range of any optical beacon. When the underwater robot enters the effective communication range of any optical beacon, the optical antenna array on the underwater robot is activated to capture and receive the composite vortex beam in the direction of the optical beacon. Using an image acquisition unit coupled to an optical antenna array, the distorted light spot formed during the reception of the composite vortex beam is continuously sampled to generate a sequence of distorted light spot images of the optical beacon.

4. The method as described in claim 1, characterized in that, The modal purity of the composite vortex beam is calculated from the distorted spot image sequence, specifically including: The distorted spot image sequence is subjected to grayscale normalization processing to obtain a standardized spot image sequence; The angular spectral distribution corresponding to each standardized spot image in the standardized spot image sequence is extracted by Fourier transform; Calculate the normalized correlation coefficients between the angular spectral distributions of each direction and the standard angular spectral distributions of the ideal modes corresponding to the composite vortex beams; The modal purity of the composite vortex beam is calculated based on all normalized correlation coefficients.

5. The method as described in claim 1, characterized in that, The stability criterion for constructing the relative line-of-sight angle change trend between the optical beacon and the underwater robot by using the temporal variation of the modal purity and the angular velocity information of the underwater robot specifically includes: Perform time-series difference operation on the time-series data of the modal purity to obtain the time-series change of the modal purity; The angular velocity data of the underwater robot is collected and then processed by adaptive noise suppression to obtain effective angular velocity information; A coupled model of the temporal variation of the modal purity and the effective angular velocity information is constructed, and the coupling coefficient of the model is determined by least squares fitting; Based on the coupling model and the coupling coefficient, a stable boundary threshold for the relative line-of-sight angle change is set to form a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot.

6. The method as described in claim 1, characterized in that, Based on the stability criterion, feedback correction is performed on the accumulated heading angle error of the inertial measurement unit in the underwater robot to generate the corrected heading angle of the underwater robot in the current navigation route. Specifically, this includes: The stability criterion is used to determine the stable state of the relative line-of-sight angle change trend between the optical beacon and the underwater robot; When the steady state is an unstable state, the accumulated heading angle error of the inertial measurement unit is extracted; Based on the determination result of the stability criterion, the error correction gain corresponding to the heading angle of the underwater robot is determined; The accumulated heading angle error is corrected by using the error correction gain to generate the corrected heading angle of the underwater robot in the current navigation route.

7. The method as described in claim 3, characterized in that, The image acquisition unit consists of an area array image sensor and a timing control circuit.

8. An autonomous navigation system for an underwater robot based on multimode optical communication, used to execute the autonomous navigation method for an underwater robot based on multimode optical communication as described in any one of claims 1 to 7, characterized in that, The system includes: The deployment module is used to deploy multiple optical beacons with known global coordinate systems within a preset underwater navigation area. Each optical beacon is configured as a composite vortex beam carrying a unique identification code. The processing module is used to receive the composite vortex beam of the corresponding optical beacon through an optical antenna array and acquire the distorted light spot image sequence from the optical beacon when the underwater robot enters the effective communication range of any optical beacon. The processing module is also used to calculate the modal purity of the composite vortex beam from the distorted spot image sequence, and to construct a stability criterion for the relative line-of-sight angle change trend between the optical beacon and the underwater robot by using the temporal change of the modal purity and the angular velocity information of the underwater robot. The processing module is also used to perform feedback correction on the heading angle error accumulated by the inertial measurement unit in the underwater robot based on the stability criterion, thereby generating the corrected heading angle of the underwater robot in the current navigation route; The execution module is used to calculate the confidence navigation position of the underwater robot in the global coordinate system by combining the identity code corresponding to the optical beacon with the corrected heading angle, and then guide the underwater robot to a route based on the confidence navigation position.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the underwater robot autonomous navigation method based on multimode optical communication as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the underwater robot autonomous navigation method based on multimode optical communication as described in any one of claims 1 to 7.

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