Underwater robot optical communication method and system

Abstract

The invention discloses an optical communication method and system for an underwater robot, and relates to the technical field of optical communication, and the method comprises the steps: carrying out the closed-loop data processing flow among an environment perception data set, an optical channel state parameter set, a multi-beam light emission parameter, image frame data, multi-pixel fusion processing, communication session data and a link configuration strategy; in a multi-robot cooperation scene of offshore oil and gas pipeline inspection, high-bandwidth and low-interruption-rate underwater optical communication transmission under the conditions of obvious scattering and frequent posture change is realized, and the problem that communication reliability and service bearing capacity are difficult to consider at the same time in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and in particular to an optical communication method and system for underwater robots. Background Technology

[0002] With the increasing number of nearshore oil and gas pipelines, large offshore wind power foundations, and underwater port structures, underwater robots are being used more and more in pipeline inspection, structural defect identification, and detailed mapping. These tasks often require underwater robots to transmit high-definition video, acoustic detection data, and data from various sensors in real time, while simultaneously receiving motion control commands and safety policy commands from higher-level nodes. In the complex nearshore water environment, relying solely on acoustic communication is prone to problems such as insufficient bandwidth, high latency, and unstable links, making it difficult to simultaneously meet the transmission requirements of high-bandwidth business data and real-time control data.

[0003] Current underwater optical communication technologies mostly employ single-beam blue-green lasers or high-power LEDs, transmitting data to a single receiver with a fixed divergence angle and modulation scheme. The receiver typically uses only a single photodetector, relying on a strong directional, narrow field of view to achieve a high signal-to-noise ratio. This approach performs well for short-range communication in static or weakly disturbed environments. However, in scenarios like subsea pipeline inspection, underwater robots are constantly in motion, their postures constantly changing. Turbulence caused by suspended sediment, air bubbles, and ship activity leads to variations in scattering and absorption coefficients over time. Single-beam directional optical links are highly susceptible to interruption due to slight deflection or partial obstruction, making continuous communication difficult. Furthermore, existing technologies often separate visual perception and optical communication, with environmental cameras only handling image acquisition. The optical communication link lacks proactive adjustment capabilities based on environmental perception, and there is a lack of relay and self-organizing mechanisms for optical communication among multiple robots.

[0004] Currently, Chinese invention patent application number 202411815938.9 discloses an optical communication device suitable for deep-sea underwater robots, which includes: a signal modulation unit that loads the data signal to be transmitted onto a Gaussian laser generated by a laser and applies a modulation signal to the Gaussian laser; a spatial light modulation unit that converts the modulated Gaussian laser into a non-diffraction beam; an optical emission unit that aligns and directs the non-diffraction beam through a PID control algorithm; an optical receiving unit that collects the non-diffraction beam transmitted from the optical emission unit and performs preliminary focusing; an optical noise reduction unit that uses a filter module to remove scattered light of different wavelengths and uses a light-shielding module to further eliminate noise in the region near the phase singularity of the non-diffraction beam; and a signal demodulation unit that demodulates the modulation signal detected by the detector.

[0005] The aforementioned technologies are difficult to simultaneously achieve high bandwidth, low interruption rate, and adaptively optimized optical communication transmission in nearshore underwater environments with significant scattering, frequent attitude changes, and multi-robot relay collaboration. Summary of the Invention

[0006] The technical problem solved by this invention is that existing technologies are difficult to achieve high bandwidth, low interruption rate and adaptive optimization of optical communication transmission in nearshore underwater environments with significant scattering, frequent attitude changes and multi-robot relay cooperation.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: An optical communication method for underwater robots includes the following steps: Step S1: Collect environmental perception data and establish a set of optical channel state parameters; Step S2: Configure the transmission parameters of multiple beams according to the optical channel state parameter set and complete beam alignment, then output the transmission configuration result; Step S3: Acquire image frame data and perform multi-pixel fusion demodulation, output demodulated bitstream data; Step S4: Perform spatiotemporal joint coding on the service data to be transmitted and map it to multiple beams for transmission, and output communication session data; Step S5: Calculate link quality index data based on demodulated bit stream data and communication session data, perform correlation analysis between link quality index data and optical channel state parameter set, output link configuration strategy and update multi-beam optical transmission parameters.

[0008] Preferably, step S1 includes the following sub-steps: Step S101: Collect environmental image data, background light intensity data, robot posture data, and target node coarse localization data to form raw environmental perception data; Step S102: Denoise, distortion correction and time alignment are performed on the original environmental perception data to obtain the environmental perception dataset; Step S103: Calculate the optical channel state parameter set based on the environmental perception dataset. The optical channel state parameter set includes the estimated scattering coefficient, the estimated absorption coefficient, the background noise light intensity index, and the estimated available propagation distance, and output the first channel analysis result.

[0009] Preferably, the calculation logic for the optical channel state parameter set and the first channel analysis result is as follows: Based on the contrast changes and color distribution of environmental image data, the concentration of suspended particles in water is estimated and the scattering coefficient is obtained. Based on background light intensity data and depth information, the water absorption is inferred and the estimated absorption coefficient is obtained. The background noise intensity index is obtained based on the fluctuation range of the background light intensity data within a preset time window. The estimated scattering coefficient, estimated absorption coefficient, background noise intensity index, and estimated available propagation distance are input into a preset optical channel prediction model to calculate the estimated available bandwidth, recommended modulation scheme, and recommended transmit power range. The estimated available bandwidth, recommended modulation scheme, and recommended transmit power range are then correlated with the estimated scattering coefficient, estimated absorption coefficient, and background noise intensity index, and the output is the first channel analysis result.

[0010] Preferably, step S2 includes the following sub-steps: Step S201: Obtain the first channel analysis results and parse the available bandwidth estimate, recommended modulation scheme, and recommended transmit power range: If the available bandwidth estimate is greater than or equal to the first bandwidth threshold and the background noise light intensity index is less than or equal to the first noise threshold, then select the higher-order modulation method and narrow divergence angle configuration. If the available bandwidth estimate is less than the first bandwidth threshold or the background noise light intensity index is greater than the first noise threshold, then select a low-order modulation method, increase the divergence angle and increase the number of emitted beams; Step S202: Generate multiple light emission parameters for each beam of emitted light based on the recommended modulation method and recommended emission power range. The multiple light emission parameters include wavelength, emission power, divergence angle and modulation method. Step S203: Control the optical emitting array to perform micro-angle sweep according to the multi-beam optical emission parameters, and gradually adjust the emission direction within the preset angle range. The receiving end generates optical intensity feedback data based on the received light intensity and transmits it back. Step S204: Select the transmission direction with the highest received light intensity as the target direction based on the light intensity feedback data, lock the corresponding multi-beam light transmission parameters, and output the transmission configuration result.

[0011] Preferably, step S3 includes the following sub-steps: Step S301: At the receiving end, image frame data is acquired using an image sensor. The image frame data is an image of multiple light spots that contains only the emission wavelength range after optical filtering. Step S302: Perform background estimation and background subtraction on the image frame data to generate candidate spot region data, and lock the spot region corresponding to each beam according to the expected position of the multi-beam emission parameters in the emission configuration result. Step S303: Aggregate the pixels in each spot area according to their spatial positions to form multiple spot pixel sequences, wherein the spot pixel sequence is a light intensity sampling sequence arranged on the time axis; Step S304: Perform multi-pixel fusion processing on the light spot pixel sequence, assign weights to pixels in the same light spot area according to pixel stability and noise level, obtain the fused light intensity time series of each beam, and perform time demodulation according to the modulation method corresponding to the emission configuration result, and output demodulated bit stream data.

[0012] Preferably, step S4 includes the following sub-steps: Step S401: Based on the optical channel state parameter set, the first channel analysis result, the multi-beam light transmission parameters, the transmission configuration result, the image frame data and the demodulated bit stream data, determine the data to be transmitted through the optical communication link, output the data to be transmitted, divide the data to be transmitted into control data and service data, and obtain the connection relationship between the current underwater robot and the neighboring underwater robots and the upper node to form relay topology data. Step S402: The control data is encoded into a control codeword sequence by using a strong error correction coding and redundant transmission strategy, and multiple transmission beams are allocated to the control codeword sequence in the multi-beam light transmission parameters. Step S403: The service data is segmented and parallel encoded. The service data is divided into multiple service segments, and error correction encoding is performed on each segment to obtain the service codeword sequence. Different transmission beams are assigned to the service codeword sequence in the multi-beam light transmission parameters. Step S404: Assemble the control codeword sequence and service codeword sequence into a transmission codeword sequence according to the multi-beam light transmission parameters, and generate a relay forwarding instruction based on the relay topology data to control the relay underwater robot to re-encode and re-transmit the demodulated bit stream data, and output communication session data.

[0013] Preferably, step S5 includes the following sub-steps: Step S501: At the receiving end, calculate the estimated bit error rate, estimated frame loss rate and average received light intensity based on the demodulated bit stream data, and calculate the end-to-end delay based on the time stamp in the communication session data to form link quality index data. Step S502: Time-align the link quality index data with the optical channel state parameter set and transmission configuration results to form a link operation dataset; Step S503: Establish a link configuration strategy based on the link operation dataset. The link configuration strategy includes recommended modulation methods, recommended transmission power ranges, and recommended divergence angles for different scattering coefficient estimates, absorption coefficient estimates, and background noise light intensity indices. Step S504: Feed back the link configuration strategy to the transmitter, update the recommended modulation scheme and recommended transmit power range in the first channel analysis results, and update the wavelength, transmit power and divergence angle in the multi-beam transmit parameters.

[0014] Preferably, the multi-pixel fusion processing logic of the light spot pixel sequence is as follows: Within the light spot area of ​​each frame image, the light intensity variance of each pixel within a preset time window is calculated. Pixels with light intensity variance less than the first variance threshold are marked as stable pixels, and pixels with light intensity variance greater than the first variance threshold are marked as high noise pixels. A larger weight is assigned to stable pixels and a smaller weight is assigned to high-noise pixels. The pixels in the same spot area are weighted and summed according to their weights to obtain the fused light intensity of the spot area in the frame. Arrange the fused light intensity of each frame on the time axis to form a fused light intensity time sequence. Input the fused light intensity time sequence into the demodulation algorithm corresponding to the modulation mode to obtain the bit stream segment corresponding to the light spot. Then, splice the bit stream segments corresponding to all the light spots in a preset order to form the demodulated bit stream data.

[0015] Preferably, the logic for establishing the link configuration strategy is as follows: The link operation dataset is divided into multiple environmental state intervals, which are divided according to the combined range of scattering coefficient estimate, absorption coefficient estimate and background noise light intensity index. Within each environmental state interval, the link quality index data corresponding to different modulation methods, different transmit powers and different divergence angle combinations are statistically analyzed, and the comprehensive score of bit error rate estimate, frame loss rate estimate and end-to-end latency is calculated. Within each environmental state interval, the modulation scheme, transmit power, and divergence angle combination with the highest comprehensive score are selected as the recommended modulation scheme, recommended transmit power range, and recommended divergence angle for that environmental state interval, forming a link configuration strategy. During the execution of subsequent steps S1 to S5, the first channel analysis results and multi-beam optical transmission parameters are self-learned and updated according to the link configuration strategy.

[0016] An underwater robot optical communication system includes an environmental data acquisition module, a parameter configuration and alignment module, an image data processing module, a data joint encoding module, and a correlation analysis and feedback module. The environmental data acquisition module is used to collect environmental sensing data and establish a set of optical channel state parameters. The parameter configuration alignment module is used to configure the transmission parameters of multiple beams according to the optical channel state parameter set and complete beam alignment, and output the transmission configuration result. The image data processing module is used to acquire image frame data and perform multi-pixel fusion demodulation, and output demodulated bitstream data; The data joint coding module is used to perform spatiotemporal joint coding on the service data to be transmitted and map it to multiple beam transmissions to output communication session data. The correlation analysis feedback module is used to calculate link quality index data based on demodulated bit stream data and communication session data, perform correlation analysis between the link quality index data and the optical channel state parameter set, output link configuration strategy, and update multi-beam optical transmission parameters.

[0017] The beneficial effects of this invention are as follows: This invention achieves high-bandwidth, low-interruption-rate underwater optical communication transmission under conditions of significant scattering and frequent attitude changes in the multi-robot collaborative scenario of nearshore oil and gas pipeline inspection through a closed-loop data processing flow between environmental perception dataset, optical channel state parameter set, multi-beam light emission parameters, image frame data, multi-pixel fusion processing, communication session data, and link configuration strategy. This solves the problem that existing technologies cannot balance communication reliability and service carrying capacity. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the steps of an underwater robot optical communication method according to an embodiment of the present invention; Figure 2 This is a basic flowchart of an underwater robot optical communication system provided in one embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Example 1, referring to Figure 1 This paper provides an optical communication method for underwater robots, comprising the following steps: Step S1: Collect environmental perception data and establish a set of optical channel state parameters.

[0021] Step S2: Configure the transmission parameters of multiple beams according to the optical channel state parameter set and complete beam alignment, and output the transmission configuration result.

[0022] Step S3: Acquire image frame data and perform multi-pixel fusion demodulation to output demodulated bitstream data.

[0023] Step S4: Perform spatiotemporal joint coding on the service data to be transmitted and map it to multiple beams for transmission, and output communication session data.

[0024] Step S5: Calculate link quality index data based on demodulated bit stream data and communication session data, perform correlation analysis between link quality index data and optical channel state parameter set, output link configuration strategy and update multi-beam optical transmission parameters.

[0025] Step S1 includes the following sub-steps: Step S101: Collect environmental image data, background light intensity data, robot posture data, and target node coarse localization data to form raw environmental perception data.

[0026] Step S101 involves data collection based on an underwater robot. The underwater robot is equipped with an environmental perception module at its front end, which includes an environmental camera, a photosensitive detector, and an attitude sensor.

[0027] The environmental camera is used to collect environmental image data. The environmental image data is a color image with a resolution of 1280×720 and a frame rate of 120 frames per second.

[0028] A photosensitive detector is used to collect background light intensity data and output an ambient light intensity sequence within a preset time interval.

[0029] Attitude sensors are used to collect robot attitude data, which includes pitch angle, roll angle, and yaw angle.

[0030] By combining the underwater robot's inertial navigation information and the known coordinates of the host node, coarse positioning data of the target node relative to the host node is calculated. The coarse positioning data of the target node includes relative distance estimates and relative azimuth estimates.

[0031] Environmental cameras, photosensitive detectors, and attitude sensors acquire data synchronously under a unified time reference, forming raw environmental perception data.

[0032] Step S102: Denoise, distortion correction and time alignment are performed on the original environmental perception data to obtain the environmental perception dataset.

[0033] Step S102: Preprocess the raw environmental perception data: The environmental image data is denoised and distortion corrected, and the images are geometrically corrected and brightness equalized using camera calibration parameters.

[0034] The background light intensity data is interpolated according to a unified timestamp, aligning the background light intensity data and the environmental image data on the same time axis.

[0035] The robot posture data and target node coarse localization data are filtered and interpolated to make them consistent with the sampling time of the environmental image data.

[0036] After the above processing, an environmental perception dataset is obtained, which includes environmental image data, background light intensity data, robot posture data, and coarse localization data of target nodes.

[0037] Step S103: Calculate the optical channel state parameter set based on the environmental perception dataset. The optical channel state parameter set includes the estimated scattering coefficient, the estimated absorption coefficient, the background noise light intensity index, and the estimated available propagation distance, and output the first channel analysis result.

[0038] The calculation logic for the optical channel state parameter set and the first channel analysis results is as follows: Based on the contrast changes and color distribution of environmental image data, the concentration of suspended particles in the water body is estimated and the scattering coefficient is obtained.

[0039] The absorption of water is estimated based on background light intensity data and depth information, and the absorption coefficient is obtained.

[0040] The background noise intensity index is obtained based on the fluctuation range of the background light intensity data within a preset time window.

[0041] The estimated scattering coefficient, estimated absorption coefficient, background noise intensity index, and estimated available propagation distance are input into a preset optical channel prediction model to calculate the estimated available bandwidth, recommended modulation scheme, and recommended transmit power range. The estimated available bandwidth, recommended modulation scheme, and recommended transmit power range are then correlated with the estimated scattering coefficient, estimated absorption coefficient, and background noise intensity index, and the output is the first channel analysis result.

[0042] Step S103 analyzes the environmental perception dataset, calculates the optical channel state parameter set, and outputs the first channel analysis result, specifically including: For environmental image data, select image regions with the same or similar directions as the upper-level node, calculate the average brightness and brightness contrast of the region, and combine them with the "correspondence table between brightness contrast and suspended particle concentration in water" established in advance in the experimental water tank to obtain the estimated value of the scattering coefficient.

[0043] Based on the brightness attenuation of environmental image data at different shooting distances, and combined with preset standard reflective target data, the estimated absorption coefficient is obtained by looking up a table.

[0044] The background noise intensity index is calculated based on the difference between the maximum and minimum values ​​of the background light intensity data within a preset time window. The background noise intensity index is used to characterize the degree of ambient light interference.

[0045] Based on the relative distance estimate in the coarse positioning data of the target node, and combined with the scattering coefficient estimate and absorption coefficient estimate, the available propagation distance estimate is obtained through a pre-calibrated empirical mapping relationship.

[0046] The estimated scattering coefficient, absorption coefficient, background noise intensity index, and available propagation distance are input into the optical channel prediction model. The optical channel prediction model is a regression model trained based on experimental data, and it outputs the available bandwidth estimate, recommended modulation scheme, and recommended transmit power range.

[0047] The control unit establishes a correspondence between the available bandwidth estimate, recommended modulation scheme, and recommended transmit power range and the scattering coefficient estimate, absorption coefficient estimate, and background noise light intensity index to obtain the optical channel state parameter set. The control unit then outputs the optical channel state parameter set and the aforementioned available bandwidth estimate, recommended modulation scheme, and recommended transmit power range as the first channel analysis result.

[0048] Step S2 includes the following sub-steps: Step S201: Obtain the first channel analysis results and parse the available bandwidth estimate, recommended modulation scheme, and recommended transmit power range.

[0049] A first bandwidth threshold and a first noise threshold are pre-set in the system. The first bandwidth threshold is, for example, a lower limit of several megabits per second, and the first noise threshold is, for example, an upper limit of ambient noise power.

[0050] If the available bandwidth estimate is greater than or equal to the first bandwidth threshold and the background noise light intensity index is less than or equal to the first noise threshold, then the higher-order modulation method is selected as the recommended modulation method. The recommended modulation method can be multi-level pulse amplitude modulation, and the divergence angle is set to a smaller level.

[0051] If the available bandwidth estimate is less than the first bandwidth threshold or the background noise intensity index is greater than the first noise threshold, then a low-order modulation method is selected as the recommended modulation method. The recommended modulation method can be on / off keying or low-order pulse amplitude modulation, and the divergence angle is set to a larger level, while increasing the number of emitted beams.

[0052] Step S202: Based on the recommended modulation scheme and recommended transmission power range, control the multi-beam optical transmission array to generate multi-beam optical transmission parameters: Several discrete wavelengths in the blue-green band suitable for underwater transmission are selected as wavelengths in the multi-beam light emission parameters.

[0053] Within the recommended transmit power range, assign an initial transmit power value to each beam, with the initial value allocated based on the ratio of control data to service data.

[0054] Select the divergence angle for each beam of light from the recommended divergence angle settings.

[0055] The recommended modulation scheme is recorded in the multi-beam emission parameters so that subsequent steps modulate the data according to the recommended modulation scheme.

[0056] Step S203: Control the optical emitting array to perform micro-angle sweep according to the multi-beam optical emission parameters, and gradually adjust the emission direction within the preset angle range. The receiving end generates optical intensity feedback data based on the received light intensity and transmits it back.

[0057] Step S204: Select the transmission direction with the highest received light intensity as the target direction based on the light intensity feedback data, lock the corresponding multi-beam light transmission parameters, and output the transmission configuration result.

[0058] After setting the multi-beam emission parameters, steps S203 and S204 control the multi-beam emission array to perform micro-angle scanning: Centered on the estimated relative azimuth angle in the coarse positioning data of the target node, the launch direction is gradually adjusted within a preset angle range according to a preset angle step size.

[0059] In each transmission direction, the receiver uses a photodetector or image sensor to measure the received light intensity and returns the light intensity feedback data corresponding to each transmission direction to the transmitter.

[0060] The transmitter selects the transmission direction with the highest received light intensity based on the light intensity feedback data, and locks the multi-beam transmission parameters corresponding to that transmission direction as the current transmission configuration.

[0061] The locked multi-beam emission parameters are used as the emission configuration result to provide a basis for subsequent image frame data acquisition and data transmission.

[0062] Step S3 includes the following sub-steps: In step S301, at the receiving end, image frame data is acquired using an image sensor. An optical filter is set at the front end of the image sensor to allow only light within the wavelength range specified in the multi-beam emission parameters to enter, thereby suppressing irrelevant background light. The image frame data is a high-frame-rate image sequence, with the frame rate matching the recommended modulation scheme in the transmission configuration results to ensure the temporal resolution of the demodulated bitstream data.

[0063] Step S302 performs background estimation and background subtraction on the image frame data to generate candidate region data for light spots: In each frame, the global or local background brightness is calculated, and the image is subtracted from the background brightness to obtain the difference image.

[0064] Thresholding is performed on the difference image to extract regions with brightness higher than the threshold as initial candidate light spots.

[0065] Based on the expected positions of the multiple light emission parameters in the emission configuration results, a search window is set near the expected positions, and the area within the search window whose area, shape, and brightness distribution meet the preset conditions is determined as the candidate area data for light spots.

[0066] Based on the expected spatial position of each beam in the emission parameters of multiple beams, the corresponding spot region is locked from the spot candidate region data to ensure that each beam has a unique spot region corresponding to it on the image plane.

[0067] Step S303: For each spot area, collect the brightness values ​​of all pixels in the area on the time axis according to its spatial position in the image frame data to form a spot pixel sequence. The spot pixel sequence consists of pixel brightness sample values ​​arranged in chronological order within a preset number of consecutive frames, used to characterize the light intensity change of the corresponding beam of light in the spot area.

[0068] Step S304 performs multi-pixel fusion processing on the spot pixel sequence: Within a preset time window, the variance of the brightness value of each pixel is calculated. Pixels with a variance less than the first variance threshold are marked as stable pixels, and pixels with a variance greater than the first variance threshold are marked as high-noise pixels. A larger weight is assigned to stable pixels, and a smaller weight is assigned to high-noise pixels. The weight can be set in reverse according to the magnitude of the variance, so that the smaller the variance, the larger the weight.

[0069] In each frame of the image, the pixels in the same spot area are weighted and summed according to their weights to obtain the fused light intensity of the spot area in that frame.

[0070] Arrange the fused light intensity of each frame in chronological order to form a fused light intensity time series. Input the fused light intensity time series into the demodulation algorithm corresponding to the recommended modulation method in the transmission configuration result to obtain the bit stream segment corresponding to the light spot area.

[0071] All bitstream segments corresponding to the light spot regions are spliced ​​together in a pre-agreed order to form demodulated bitstream data, providing the basis for subsequent spatiotemporal joint coding and relay forwarding.

[0072] The multi-pixel fusion processing logic for the light spot pixel sequence is as follows: Within the light spot area of ​​each frame image, the light intensity variance of each pixel within a preset time window is calculated. Pixels with a light intensity variance less than a first variance threshold are marked as stable pixels, and pixels with a light intensity variance greater than the first variance threshold are marked as high-noise pixels.

[0073] A larger weight is assigned to stable pixels and a smaller weight is assigned to high-noise pixels. The pixels in the same spot area are weighted and summed to obtain the fused light intensity of the spot area in the frame.

[0074] Arrange the fused light intensity of each frame on the time axis to form a fused light intensity time sequence. Input the fused light intensity time sequence into the demodulation algorithm corresponding to the modulation mode to obtain the bit stream segment corresponding to the light spot. Then, splice the bit stream segments corresponding to all the light spots in a preset order to form the demodulated bit stream data.

[0075] Step S4 divides the service data to be transmitted into control data and service data according to data type: The data to be transmitted is a set of data that the underwater robot needs to send through the optical communication link during the inspection of offshore oil and gas pipelines. The data to be transmitted includes at least control data and business data. The control data refers to control command data used to update relay topology data, configure multi-beam light emission parameters, and adjust the underwater robot's operating status. The control command data includes command data for modifying the node connection relationship in the relay topology data, command data for switching the working mode corresponding to the optical channel state parameter set, and command data for adjusting the underwater robot's operating posture and motion trajectory. The business data is inspection task data formed based on environmental perception dataset and image frame data. The business data includes environmental state description data extracted from environmental perception dataset, pipeline surface image sequence generated from image frame data, and inspection result record data corresponding to demodulated bit stream data. In step S4, the business data is mapped to the emission beam corresponding to the multi-beam emission parameters for transmission through a segmented parallel encoding method.

[0076] Based on the physical connection relationships between the current underwater robot and neighboring underwater robots and the host node, as well as historical link quality index data, relay topology data is generated. The relay topology data indicates the relay role of each underwater robot in the optical communication link.

[0077] Strong error correction coding is performed on control data to generate control codeword sequences. Multiple transmission beams are allocated to the control codeword sequences in the multi-beam transmission parameters, so that control data is redundantly transmitted on multiple beams.

[0078] The business data is segmented and encoded in parallel. The business data is divided into multiple business segments of fixed length. Error correction coding is performed on each business segment to obtain a business codeword sequence. Different transmission beams are allocated to different business codeword sequences in the multi-beam optical transmission parameters to realize the parallel transmission of business data.

[0079] The transmitting end assembles the control codeword sequence and service codeword sequence into a transmission codeword sequence based on the multi-beam light transmission parameters. After receiving the demodulated bitstream data, the relay underwater robot determines whether forwarding is necessary based on the relay topology data. If forwarding is required, it decodes the demodulated bitstream data, re-encodes it with error correction, and reassembles it into a new control codeword sequence and service codeword sequence. Then, following steps S2 and S3 in this embodiment, it reconfigures the multi-beam light transmission parameters and acquires image frame data, thereby outputting new communication session data and realizing multi-hop optical relay transmission.

[0080] Step S5 calculates link quality index data based on demodulated bitstream data and communication session data: By comparing the actual received demodulated bitstream data with the transmitted codeword sequence at the transmitting end, the estimated bit error rate and frame loss rate are calculated.

[0081] Based on the time stamp in the communication session data, the transmission time difference from the transmitter to the receiver is statistically analyzed to obtain the end-to-end delay.

[0082] The bit error rate estimate, frame loss rate estimate, average received optical intensity, and end-to-end delay are combined to form link quality index data.

[0083] The link quality index data is aligned with the optical channel state parameter set and transmission configuration results by timestamp to form a link operation dataset.

[0084] In the link operation dataset, the data is divided into multiple environmental state intervals based on the estimated scattering coefficient, estimated absorption coefficient, and background noise light intensity. Each environmental state interval corresponds to a specific water environment condition. For each environmental state interval, link quality index data under different modulation methods, different transmit powers, and different divergence angle combinations are statistically analyzed. The estimated bit error rate, estimated frame loss rate, and end-to-end latency are calculated as a comprehensive score. The comprehensive score can be obtained by weighting and summing the various indicators according to preset weights.

[0085] Within each environmental state interval, the modulation scheme, transmit power, and divergence angle combination with the highest comprehensive score are selected as the recommended modulation scheme, recommended transmit power range, and recommended divergence angle for that environmental state interval, forming a link configuration strategy.

[0086] In subsequent steps S1 to S5, the corresponding environmental state interval is searched in the link configuration strategy according to the current optical channel state parameter set, and the recommended modulation method, recommended transmit power range and recommended divergence angle corresponding to the environmental state interval are directly used to update the first channel analysis results and multi-beam transmit parameters, so as to realize self-learning adaptation to different underwater environments.

[0087] In a typical application scenario, the underwater robot moves near a subsea pipeline at a depth of about 30 meters and a horizontal distance of about 20 meters. The environmental turbidity is at a moderate level, and the background light intensity mainly comes from natural light from the water surface.

[0088] In this scenario, the estimated values ​​of the scattering coefficient and absorption coefficient obtained in step S1 of the optical channel state parameter set are in a medium range, the background noise light intensity index is low, the estimated available bandwidth output by the optical channel prediction model is greater than the first bandwidth threshold, the recommended modulation method is a high-order pulse amplitude modulation method, and the recommended transmit power range is in the medium power range allowed by the device.

[0089] In step S2, several wavelengths in the blue-green band are selected for the multi-beam light emission parameters, the divergence angle is set to a smaller level, and the emission direction with the maximum received light intensity is locked after micro-angle sweeping to obtain the emission configuration result.

[0090] In step S3, the receiver uses an image sensor to acquire image frame data at a high frame rate, obtains a stable fused light intensity time series through the detection of candidate light spot regions and multi-pixel fusion processing, and recovers the demodulated bit stream data using a demodulation algorithm that matches the recommended modulation method.

[0091] In step S4, control data is mapped to multiple beams for redundant transmission, while service data is fragmented into different beams for parallel transmission. The data is forwarded by one or more relay underwater robots, ensuring low latency for control data and high bandwidth for service data transmission.

[0092] In step S5, the link configuration strategy is updated based on the link quality index data. When the same or similar optical channel state parameter sets reappear, the multi-beam light transmission parameters with higher comprehensive scores can be directly selected, enabling the underwater robot optical communication link to have continuous optimization capabilities during long-term operation.

[0093] Example 2, refer to Figure 2 This paper provides an underwater robot optical communication system, which includes an environmental data acquisition module, a parameter configuration and alignment module, an image data processing module, a data joint encoding module, and a correlation analysis and feedback module.

[0094] The environmental data acquisition module is used to collect environmental sensing data and establish a set of optical channel state parameters.

[0095] The parameter configuration alignment module is used to configure the transmission parameters of multiple beams according to the optical channel state parameter set and complete the beam alignment, and output the transmission configuration result.

[0096] The image data processing module is used to acquire image frame data and perform multi-pixel fusion demodulation, outputting demodulated bitstream data.

[0097] The data joint coding module is used to perform spatiotemporal joint coding on the service data to be transmitted and map it to multiple beams for transmission, outputting communication session data.

[0098] The correlation analysis feedback module is used to calculate link quality index data based on demodulated bit stream data and communication session data, perform correlation analysis on the link quality index data and optical channel state parameter set, output link configuration strategy and update multi-beam optical transmission parameters.

[0099] This invention achieves high-bandwidth, low-interruption-rate underwater optical communication transmission in a multi-robot collaborative scenario of nearshore oil and gas pipeline inspection, through a closed-loop data processing flow between environmental perception datasets, optical channel state parameter sets, multi-beam light emission parameters, image frame data, multi-pixel fusion processing, communication session data, and link configuration strategies. This solves the problem that existing technologies struggle to balance communication reliability and service carrying capacity.

[0100] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optical communication method for underwater robots, characterized in that, Includes the following steps: Step S1: Collect environmental perception data and establish a set of optical channel state parameters; Step S2: Configure the transmission parameters of multiple beams according to the optical channel state parameter set and complete beam alignment, then output the transmission configuration result; Step S3: Acquire image frame data and perform multi-pixel fusion demodulation, output demodulated bitstream data; Step S4: Perform spatiotemporal joint coding on the service data to be transmitted and map it to multiple beams for transmission, and output communication session data; Step S5: Calculate link quality index data based on demodulated bit stream data and communication session data, perform correlation analysis between link quality index data and optical channel state parameter set, output link configuration strategy and update multi-beam optical transmission parameters.

2. The underwater robot optical communication method as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Collect environmental image data, background light intensity data, robot posture data, and target node coarse localization data to form raw environmental perception data; Step S102: Denoise, distortion correction and time alignment are performed on the original environmental perception data to obtain the environmental perception dataset; Step S103: Calculate the optical channel state parameter set based on the environmental perception dataset. The optical channel state parameter set includes the estimated scattering coefficient, the estimated absorption coefficient, the background noise light intensity index, and the estimated available propagation distance, and output the first channel analysis result.

3. The underwater robot optical communication method as described in claim 2, characterized in that, The calculation logic for the optical channel state parameter set and the first channel analysis result is as follows: Based on the contrast changes and color distribution of environmental image data, the concentration of suspended particles in water is estimated and the scattering coefficient is obtained. Based on background light intensity data and depth information, the water absorption is inferred and the estimated absorption coefficient is obtained. The background noise intensity index is obtained based on the fluctuation range of the background light intensity data within a preset time window. The estimated scattering coefficient, estimated absorption coefficient, background noise intensity index, and estimated available propagation distance are input into a preset optical channel prediction model to calculate the estimated available bandwidth, recommended modulation scheme, and recommended transmit power range. The estimated available bandwidth, recommended modulation scheme, and recommended transmit power range are then correlated with the estimated scattering coefficient, estimated absorption coefficient, and background noise intensity index, and the output is the first channel analysis result.

4. The underwater robot optical communication method as described in claim 1, characterized in that, Step S2 includes the following sub-steps: Step S201: Obtain the first channel analysis results and parse the available bandwidth estimate, recommended modulation scheme, and recommended transmit power range: If the available bandwidth estimate is greater than or equal to the first bandwidth threshold and the background noise light intensity index is less than or equal to the first noise threshold, then select the higher-order modulation method and narrow divergence angle configuration. If the available bandwidth estimate is less than the first bandwidth threshold or the background noise light intensity index is greater than the first noise threshold, then select a low-order modulation method, increase the divergence angle and increase the number of emitted beams; Step S202: Generate multiple light emission parameters for each beam of emitted light based on the recommended modulation method and recommended emission power range. The multiple light emission parameters include wavelength, emission power, divergence angle and modulation method. Step S203: Control the optical emitting array to perform micro-angle sweep according to the multi-beam optical emission parameters, and gradually adjust the emission direction within the preset angle range. The receiving end generates optical intensity feedback data based on the received light intensity and transmits it back. Step S204: Select the transmission direction with the highest received light intensity as the target direction based on the light intensity feedback data, lock the corresponding multi-beam light transmission parameters, and output the transmission configuration result.

5. The underwater robot optical communication method as described in claim 1, characterized in that, Step S3 includes the following sub-steps: Step S301: At the receiving end, image frame data is acquired using an image sensor. The image frame data is an image of multiple light spots that contains only the emission wavelength range after optical filtering. Step S302: Perform background estimation and background subtraction on the image frame data to generate candidate spot region data, and lock the spot region corresponding to each beam according to the expected position of the multi-beam emission parameters in the emission configuration result. Step S303: Aggregate the pixels in each spot area according to their spatial positions to form multiple spot pixel sequences, wherein the spot pixel sequence is a light intensity sampling sequence arranged on the time axis; Step S304: Perform multi-pixel fusion processing on the light spot pixel sequence, assign weights to pixels in the same light spot area according to pixel stability and noise level, obtain the fused light intensity time series of each beam, and perform time demodulation according to the modulation method corresponding to the emission configuration result, and output demodulated bit stream data.

6. The underwater robot optical communication method as described in claim 1, characterized in that, Step S4 includes the following sub-steps: Step S401: Based on the optical channel state parameter set, the first channel analysis result, the multi-beam light transmission parameters, the transmission configuration result, the image frame data and the demodulated bit stream data, determine the data to be transmitted through the optical communication link, output the data to be transmitted, divide the data to be transmitted into control data and service data, and obtain the connection relationship between the current underwater robot and the neighboring underwater robots and the upper node to form relay topology data. Step S402: The control data is encoded into a control codeword sequence by using a strong error correction coding and redundant transmission strategy, and multiple transmission beams are allocated to the control codeword sequence in the multi-beam light transmission parameters. Step S403: The service data is segmented and parallel encoded. The service data is divided into multiple service segments, and error correction encoding is performed on each segment to obtain the service codeword sequence. Different transmission beams are assigned to the service codeword sequence in the multi-beam light transmission parameters. Step S404: Assemble the control codeword sequence and service codeword sequence into a transmission codeword sequence according to the multi-beam light transmission parameters, and generate a relay forwarding instruction based on the relay topology data to control the relay underwater robot to re-encode and re-transmit the demodulated bit stream data, and output communication session data.

7. The underwater robot optical communication method as described in claim 1, characterized in that, Step S5 includes the following sub-steps: Step S501: At the receiving end, calculate the estimated bit error rate, estimated frame loss rate and average received light intensity based on the demodulated bit stream data, and calculate the end-to-end delay based on the time stamp in the communication session data to form link quality index data. Step S502: Time-align the link quality index data with the optical channel state parameter set and transmission configuration results to form a link operation dataset; Step S503: Establish a link configuration strategy based on the link operation dataset. The link configuration strategy includes recommended modulation methods, recommended transmission power ranges, and recommended divergence angles for different scattering coefficient estimates, absorption coefficient estimates, and background noise light intensity indices. Step S504: Feed back the link configuration strategy to the transmitter, update the recommended modulation scheme and recommended transmit power range in the first channel analysis results, and update the wavelength, transmit power and divergence angle in the multi-beam transmit parameters.

8. The underwater robot optical communication method as described in claim 5, characterized in that, The multi-pixel fusion processing logic for the light spot pixel sequence is as follows: Within the light spot area of ​​each frame image, the light intensity variance of each pixel within a preset time window is calculated. Pixels with light intensity variance less than the first variance threshold are marked as stable pixels, and pixels with light intensity variance greater than the first variance threshold are marked as high noise pixels. A larger weight is assigned to stable pixels and a smaller weight is assigned to high-noise pixels. The pixels in the same spot area are weighted and summed according to their weights to obtain the fused light intensity of the spot area in the frame. Arrange the fused light intensity of each frame on the time axis to form a fused light intensity time sequence. Input the fused light intensity time sequence into the demodulation algorithm corresponding to the modulation mode to obtain the bit stream segment corresponding to the light spot. Then, splice the bit stream segments corresponding to all the light spots in a preset order to form the demodulated bit stream data.

9. The underwater robot optical communication method as described in claim 7, characterized in that, The logic for establishing the link configuration policy is as follows: The link operation dataset is divided into multiple environmental state intervals, which are divided according to the combined range of scattering coefficient estimate, absorption coefficient estimate and background noise light intensity index. Within each environmental state interval, the link quality index data corresponding to different modulation methods, different transmit powers and different divergence angle combinations are statistically analyzed, and the comprehensive score of bit error rate estimate, frame loss rate estimate and end-to-end latency is calculated. Within each environmental state interval, the modulation scheme, transmit power, and divergence angle combination with the highest comprehensive score are selected as the recommended modulation scheme, recommended transmit power range, and recommended divergence angle for that environmental state interval, forming a link configuration strategy. During the execution of subsequent steps S1 to S5, the first channel analysis results and multi-beam optical transmission parameters are self-learned and updated according to the link configuration strategy.

10. An underwater robot optical communication system, applied in an underwater robot optical communication method as described in any one of claims 1-9, characterized in that, It includes an environmental data acquisition module, a parameter configuration and alignment module, an image data processing module, a data joint encoding module, and a correlation analysis and feedback module; Step S1: Collect environmental perception data and establish a set of optical channel state parameters; Step S2: Configure the transmission parameters of multiple beams according to the optical channel state parameter set and complete beam alignment, then output the transmission configuration result; Step S3: Acquire image frame data and perform multi-pixel fusion demodulation, output demodulated bitstream data; Step S4: Perform spatiotemporal joint coding on the service data to be transmitted and map it to multiple beams for transmission, and output communication session data; Step S5: Calculate link quality index data based on demodulated bit stream data and communication session data, perform correlation analysis between link quality index data and optical channel state parameter set, output link configuration strategy and update multi-beam optical transmission parameters.

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