Underwater robot sample identifying and grabbing method and system

Abstract

The invention provides an underwater robot sample identifying and grabbing method and system, and the method comprises the steps that an underwater robot employs a binocular camera to obtain collected image information in real time, and employs a target detection model to detect the number and types of target samples in the image information in real time; according to the image information, the posture information of the identified target sample type under water is judged, and according to the target sample type and the posture information, an action instruction of a sampling mechanism corresponding to the target sample type is automatically generated; automatically detecting the depth information of the target sample of the corresponding type relative to the binocular camera underwater according to the binocular camera, and automatically adjusting the action posture of the corresponding sampling mechanism according to the depth information of the target sample and the posture information of the target sample; and if the sampling is successful, acquiring depth information of all samples identified in the image information, and selecting the sample with the minimum depth information as a new target sample for sampling operation.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater robots, and in particular to a method and system for identifying and grasping samples of underwater robots. Background Art

[0002] At present, underwater robots mainly use a single camera for image acquisition in sample identification and grasping, identify target samples through image recognition, and grasp them by manually driving the robotic arm. The above-mentioned existing technologies mainly have the following technical problems: 1. The grasping of the robotic arm requires manual participation and control, so that the underwater robot needs to ensure the stability of the remote control signal underwater. Since the signal will be interfered by aquatic organisms, water flow, water depth and the electric field of underwater equipment underwater, the stability of the remote control signal is poor. 2. The monocular camera cannot identify the spatial distance between the target sample and the underwater robot, so it is necessary to drive the robot engine and robotic arm and other related equipment multiple times for manual real-time adjustment to achieve accurate positioning and grasping. Summary of the invention

[0003] One of the inventive purposes of the present invention is to provide a method and system for identifying and grasping samples by an underwater robot. The method and system utilize the robot's target detection technology to identify and locate samples, and automatically generate drive codes for the robot's corresponding sampling mechanism based on the identification and positioning results, thereby realizing automatic sampling of samples by underwater robots using target detection technology. Compared with traditional manually controlled sampling methods of underwater robots, the present invention has better stability and reduces the impact of environmental factors on underwater sampling.

[0004] Another object of the present invention is to provide an underwater robot sample identification and grasping method and system, the method and system perform feature classification on the identified underwater sample types according to the target detection algorithm, and drive the corresponding type of sampling mechanism of the underwater robot to perform sampling of the corresponding target according to the result of the feature classification. Therefore, the present invention has a wide range of underwater sample sampling capabilities and can improve the comprehensive sampling capabilities of the underwater robot.

[0005] Another inventive object of the present invention is to provide an underwater robot sample identification and grasping method and system, wherein the method and system construct a sample posture recognition method, wherein the posture recognition method pre-stores different posture type images of corresponding sample types, matches the identified underwater target samples with the different posture images of corresponding sample types, obtains the posture type of the underwater target sample after matching analysis, and automatically generates a sampling strategy for the target sample based on the posture type of the underwater target sample, thereby improving the success rate of automated sampling of underwater samples.

[0006] Another inventive object of the present invention is to provide an underwater robot sample identification and grasping method and system, the method and system obtain the depth information of the identified target sample in the water in real time through a binocular camera, and drive the real-time adjustment of the movement and posture of the corresponding type of sampling mechanism in combination with the posture information of the sample, thereby improving the sampling success rate of the target sample underwater, and using the depth information obtained after the binocular camera image analysis to perform nearby sampling of the target sample, thereby improving the efficiency of underwater sampling.

[0007] In order to achieve at least one of the above-mentioned invention objects, the present invention further provides a method for identifying and grasping a sample by an underwater robot, the method comprising: The underwater robot uses a binocular camera to acquire collected image information in real time, and uses a target detection model to detect the number and type of target samples in the image information in real time; Determine the underwater posture information of the target sample type identified according to the image information, and automatically generate action instructions for a sampling mechanism corresponding to the target sample type according to the target sample type and the posture information; Automatically detecting the depth information of the target sample of the corresponding type relative to the binocular camera underwater according to the binocular camera, and automatically adjusting the action posture of the corresponding sampling mechanism according to the depth information of the target sample and the posture information of the target sample; Determine whether sampling is successful according to the corresponding type sensor of the sampling mechanism; if sampling fails, re-use the target detection model to track and identify the depth information and posture information of the target sample, and generate a new action posture of the corresponding sampling mechanism according to the re-identified depth information and posture information of the target sample; If the sampling is successful, the depth information of all samples identified in the image information is obtained, and the sample with the smallest depth information is selected as a new target sample for sampling operation.

[0008] According to one of the preferred embodiments of the present invention, image information collected by the binocular camera is obtained, and the collected image information is input into a trained YOLO model for target detection, and the sample image area existing in the image information is obtained by using the YOLO model, wherein the identified sample image area is rectangularly segmented according to the YOLO model to obtain a target sample block image; a corresponding gradient histogram is extracted from each segmented target sample block image using a KCF algorithm, a circulant matrix describing the area of ​​the sample image is constructed according to the corresponding gradient histogram, a sliding detection window of the circulant matrix is ​​constructed, and the similarity between the segmented target sample block image and the target sample is detected according to the sliding window and the trained classifier. If the similarity is greater than a preset threshold, it is used as a target sample for real-time tracking.

[0009] According to another preferred embodiment of the present invention, a driving instruction binding relationship corresponding to underwater sample types and sampling mechanism types is pre-constructed, wherein the sampling types include aquatic plants, aquatic animals, underwater rocks, and underwater mud, wherein target samples of different sampling types correspond to different sampling mechanisms, and after the sample type is identified by the YOLO model and used as the target sample, a driving instruction of the corresponding type of sampling mechanism is generated according to the driving instruction binding relationship to perform the sampling operation of the target sample.

[0010] According to another preferred embodiment of the present invention, the method for acquiring the posture information of the target sample includes: after obtaining the target sample block image in the image information according to the YOLO model recognition, edge detection is performed on the target sample block image using the Roberts operator to obtain the detected edge pixel features of the target sample in the target sample block image; the Roberts operator is used to pre-detect the standard edge pixel features of different postures of the standard samples of the corresponding sample type, the similarity of the detected edge pixel features and the standard edge pixel features is calculated, and the standard edge pixel features of the standard sample posture with the highest similarity are selected as the posture of the target sample.

[0011] According to another preferred embodiment of the present invention, the method for generating action instructions for the sampling mechanism includes: configuring at least one edge entry point in the standard edge pixel features of the standard sample posture of the corresponding sample type, and generating action instructions including rotation, movement, and clamping for different control joints of the sampling mechanism according to the edge entry points of the matched standard sample posture of the corresponding sample type, so that the action instructions will execute the sampling strategy of the corresponding target sample according to the edge entry points.

[0012] According to another preferred embodiment of the present invention, the method for acquiring the depth information of the target sample includes: the acquisition includes: using a binocular camera to respectively identify and acquire the target sample block image according to the YOLO model, and calculating the coordinate values ​​of the pixels of the target sample block image in the corresponding cameras, and respectively calculating the average coordinate value n of all pixels of the target sample block image in the two cameras. 1 and n 2 , according to the average coordinate value n of the two cameras 1 and n 2 Calculate the disparity d=(n 2 -n 1 ) / B, where B represents the baseline distance between the binocular cameras, and the depth information Z of the target sample relative to the binocular cameras is further calculated according to the disparity d.

[0013] According to another preferred embodiment of the present invention, the method for calculating the depth information Z of the target sample includes: obtaining the gray value information of the target sample block image, and filtering out the gray features of the target sample according to the gray value information, performing feature matching according to the gray features of the target sample to obtain the average coordinate value n of all pixels of the target sample in the two cameras 1 and n 2 , and calculate the disparity d, obtain the focal length f of the binocular camera, and calculate the depth information Z=f×B / d of the target sample block image relative to the binocular camera according to the calculated disparity d using the triangulation method.

[0014] According to another preferred embodiment of the present invention, after acquiring the depth information Z of the target sample block image and the posture information of the corresponding target sample, the robot body and / or the sampling mechanism are driven to approach the target sample according to the depth information Z of the block image, and when the depth information reaches an appropriate range, a sampling strategy of the corresponding sampling mechanism is further generated according to the edge entry point in the posture information of the target sample.

[0015] In order to achieve at least one of the above-mentioned invention purposes, the present invention further provides an underwater robot sample grasping and identifying system, and the system executes the above-mentioned underwater robot sample identifying and grasping method.

[0016] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned underwater robot sample identification and grasping method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic flow chart of a method for identifying and grasping samples by an underwater robot according to the present invention. DETAILED DESCRIPTION

[0018] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0019] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0020] Please combine Figure 1The present invention discloses a method and system for identifying and grasping samples of an underwater robot. The method mainly includes the following steps: first, a binocular camera is configured for the underwater robot. The binocular camera collects image information underwater respectively, and outputs the sample category and quantity in the image information according to the collected image information input into the trained target detection model. The sample category includes but is not limited to aquatic plants, aquatic animals, underwater rocks and mud, etc. The target detection model is obtained by pre-training. The target detection model in the present invention is preferably a YOLO model. The image of the target sample obtained by the target detection model is subjected to posture detection, and the posture information of the sample identified by the target detection model under water is judged. The present invention also recognizes the depth information of the target sample in water based on two images in the same time series collected by the binocular camera, and automatically generates a sampling strategy for the target sample in combination with the target sample type, the depth information and the posture information of the target sample. The sampling strategy is a sampling mechanism composed of a plurality of rotating, telescopic and moving mechanisms, such as an existing mechanical gripper. And because the types of different samples are different, it is necessary to configure sampling mechanisms corresponding to the sample types. For example, the sampling mechanism for seabed shellfish can be a mechanical gripper, while the sampling mechanism for marine plankton can be a filter composed of a stretching mechanism, etc. Since the sampling mechanisms corresponding to the sample types are all existing mechanical devices, the mechanical device structure and morphology of the sampling mechanism are designed according to different requirements. The above-mentioned mechanical device itself is not the core technical means of the present invention, and the mechanical device structure and morphology of the sampling mechanism are no longer provided with detailed technical details. After calculating the underwater depth and posture information of the target sample image recognized by the target detection model, the sampling strategy of the target sample drives the corresponding type of sampling mechanism to perform automated sampling operations. Therefore, the technical solution of the present invention can realize the automated sampling of target samples by robots underwater, and improve the sampling efficiency and success rate of the target samples.

[0021] Specifically, due to the different types of seabed samples, the difficulty of sampling will be different. In particular, the seabed environment is also affected by water flow, robot movement, and aquatic organisms. Therefore, the present invention requires real-time identification and tracking of identified target samples so that the sampling strategy can adapt to the seabed environment and sample properties and location. The present invention obtains the first image and the second image information in the same time series according to the binocular camera configured by the underwater robot, respectively, wherein the first image and the second image information are respectively input into the YOLO model for target sample detection for identification, and the sample type and quantity in the first image and the second image are respectively obtained, wherein the YOLO model is a multi-target detection model, after the corresponding type of target samples are identified by the YOLO model, the corresponding samples in the first image and the second image are rectangularly segmented according to the recognition mechanism of the YOLO model to obtain the corresponding target sample block image; the KCF algorithm is used to extract the gradient histogram of the corresponding target sample block image from each segmented target sample block image, wherein the method for constructing the gradient histogram includes: graying the segmented target sample block image, and automatically marking the target sample area pixels that meet the corresponding gray value range after graying, and performing convolution calculation using gradient operators in different directions to calculate the gray scale. The target sample block image after the calibration has a gradient component and gradient amplitude in the corresponding direction of each pixel, and the target sample block image is divided into cell units and cell blocks according to the preset pixel size, and the gradient features and gradient amplitudes calculated in the cell units and cell blocks are calculated to obtain the corresponding gradient histogram, which is used to describe the grayscale gradient change between different cell units, and to construct a circulant matrix of the annotated area; a classifier is pre-constructed to calculate the gradient histogram and circulant matrix in the target sample block image in the next frame, and the similarity is calculated according to the corresponding grayscale value in the target sample block image in the next frame obtained by the sliding window. The target sample image area gradient histogram is processed, wherein the circulant matrix is ​​used for Fourier transform calculation to reduce the matrix calculation amount of target tracking, and the sliding window coverage area with a similarity greater than a preset threshold is selected as the target sample tracking area annotated in the previous frame, thereby realizing efficient tracking of the target sample in different frame images. It should be noted that the target sample block image in the present invention is obtained by rectangular cutting of the YOLO model. Therefore, it contains a background image, and after being processed by the KCF algorithm, the influence of the background image on the regional tracking of the target sample image can be effectively reduced.

[0022] Further, the present invention presets a binding relationship between the corresponding sampling mechanism driving instruction and the corresponding sample type, and performs posture feature comparison and recognition according to the target sample block image identified by the YOLO model. The corresponding sampling mechanism may be composed of at least one of a rotating mechanism, a moving mechanism, a telescopic mechanism and a clamping mechanism, etc., including but not limited to a rotating mechanism, a moving mechanism, a telescopic mechanism and a clamping mechanism, etc., and its driving instruction includes a rotation instruction, a telescopic instruction, a moving instruction and a clamping instruction of the corresponding mechanism, etc. The rotation instruction includes a rotation angle and speed, the moving instruction includes a moving speed and distance, the telescopic instruction includes a telescopic length and speed, and the clamping instruction includes a clamping force angle and strength, etc. The above corresponding rotation instructions, telescopic instructions, moving instructions and clamping instructions are bound by the corresponding type of sampling mechanism and the corresponding sample type. When the YOLO model identifies the corresponding type of target sample, the corresponding type of sampling mechanism is started, and self-sampling is achieved through the corresponding type of driving instruction. For example, in one of the preferred embodiments of the present invention, the rotating mechanism can be a stepper motor, and the single chip microcomputer driving the stepper motor is equipped with a rotation direction control pin DIR_PIN, and automatically generates the following high pulse instruction digitalWrite (STEP_PIN, HIGH), digitalWrite () is a pulse function, and the high pulse instruction digitalWrite (STEP_PIN, HIGH) generates a pulse for driving rotation through the rotation direction control pin DIR_PIN, and further realizes the delay of the corresponding pulse step through the delay instruction delay_us (10mm) to control the rotation speed of the corresponding rotating mechanism. At the same time, the driving instruction also includes a pulse instruction digitalWrite (STEP_PIN, LOW) and a stepper motor speed step_motor (), respectively setting the duration parameters of the delay instruction for the generation of the driving instruction. It should be noted that the driving instruction can be a single driving instruction or multiple combined driving instructions as exemplified above, which are used to control the automatic sampling of the sampling mechanism. The present invention automatically drives the corresponding sampling mechanism to execute the corresponding sampling drive instruction based on the YOLO model to identify the position of the target sample block image of the corresponding type, the underwater posture of the target sample and the depth information of the target sample.

[0023] The method for acquiring the underwater posture data of the target sample comprises: after the target sample block image in the image information is acquired according to the YOLO model recognition, the target sample block image needs to be preprocessed, wherein the data preprocessing method comprises: using the Roberts operator to perform edge detection on the target sample block image and the standard sample image, obtaining the detected edge pixel features of the target sample in the target sample block image and the standard edge pixel features of the standard sample image in different postures; wherein the detected edge pixel features and the standard edge pixel features of the standard sample image in different postures are detected by the method The invention comprises: constructing a horizontal convolution kernel and a vertical convolution kernel according to the Roberts operator, wherein the horizontal convolution kernel and the vertical convolution kernel can be preset as a 2*2 convolution kernel, performing grayscale conversion on the target sample block image and the standard target sample image, and then using the filter2D function to calculate the horizontal and vertical gradient values ​​of the target sample block image and the standard target sample image of different postures, and merging the horizontal and vertical gradient values, and outputting the corresponding detected edge pixel features and the standard edge pixel features of the standard sample image. In the present invention, the gradient amplitude is obtained according to the merged horizontal and vertical gradient values, and the gradient amplitude is binarized and a threshold is set, and the pixel points whose gradient amplitude is greater than the preset threshold are used as the pixel points of the corresponding posture edge features.

[0024] Furthermore, after obtaining the detection edge pixel features and the standard edge pixel features of the standard sample image based on the Roberts operator, the present invention compares the similarity between the detection edge pixel features actually detected and the standard edge pixel features of the standard sample image in different postures, and selects the highest standard edge pixel feature posture as the detection edge pixel feature posture actually detected. And configure the entry point on the edge pixel of the detection edge pixel feature of the identified posture, wherein the setting of the entry point is set according to different sample types, for example, the entry point for the fan-shaped shell sample can be set at the edge pixel point of the shell near the shell ear part. Because the shell ear part is suitable for the clamping device to clamp the shell sample after inserting from the edge, such an automated clamping method has higher stability. It should be noted that the configuration of the above entry point will be set according to the sample type, and the position of the entry point will be determined in combination with the posture information of the detection sample, and the driving strategy of the sampling mechanism will be automatically generated in combination with the depth information of the target sample relative to the binocular camera. The above entry point is only for example, and the present invention is not limited to this.

[0025] The method for obtaining the depth information of the target sample relative to the binocular camera comprises the following steps: obtaining the baseline distance B of the binocular camera after the underwater robot is installed, wherein the baselines between the binocular cameras are installed in parallel, and respectively identifying and obtaining the target sample block images of the two cameras according to the YOLO model, and recording the first target sample block image P 1 and the second target sample block image P 2 , and respectively calculate the first target sample block image P 1 and the second target sample block image P 2 The coordinate values ​​of the pixels in the corresponding cameras are calculated to calculate the first target sample block image P 1 and the second target sample block image P 2 The average coordinate value n of all pixels in the corresponding camera 1 and n 2 , according to the first target sample block image P 1 and the second target sample block image P 2 The average coordinate value n of all pixels in the corresponding camera 1 and n 2 Calculate the disparity d=(n 2 -n 1 ) / B, where B represents the baseline distance between the binocular cameras, and the depth information Z of the target sample relative to the binocular cameras is further calculated according to the disparity d.

[0026] In one of the preferred embodiments of the present invention, in order to reduce the influence of the background image in the rectangular cut image on the depth information in the target sample image recognized by the YOLO model, the present invention makes the following improvements: the gray value information of the target sample block image is obtained, and the gray features of the target sample are screened out according to the gray value information, wherein the screening method can include but is not limited to threshold screening and deep learning model feature analysis screening, etc., and feature matching is performed according to the gray features of the target sample to obtain the average coordinate value n of all pixels of the target sample in the two cameras. 1 and n 2 , and calculate the disparity d, obtain the focal length f of the binocular camera, and calculate the depth information Z=f×B / d of the target sample block image relative to the binocular camera according to the triangulation method based on the calculated disparity d. In another preferred embodiment of the present invention, in order to better obtain the different local depth information of the target sample, the present invention can calculate the depth information of the local area of ​​the target sample according to the triangulation method for a single pixel point or a pixel block composed of multiple pixels in different areas of the target sample, thereby using a sampling strategy for local depth information.

[0027] The automated sampling strategy of the target sample in the present invention includes: after obtaining the depth information Z of the target sample block image and the posture information of the corresponding target sample, the robot body and / or the sampling mechanism are driven to approach the target sample according to the depth information Z of the block image, wherein the telescopic mechanism can be driven to approach the target sample at a preset speed, and when the depth information reaches a suitable range, an efficient and accurate sampling strategy of the corresponding sampling mechanism is further generated according to the edge entry point in the posture information of the target sample. The sampling strategy also includes the search and sampling strategy of the nearest target sample. When the sampling is successful, the corresponding sampling sensor can generate relevant signals, such as but not limited to cameras, pressure sensors, gravity sensors, etc. And the depth information of all samples identified in the image information is obtained, and the sample with the smallest depth information is selected as the new target sample for sampling operation.

[0028] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0029] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0030] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A method for identifying and grabbing samples by an underwater robot, characterized in that: The method comprises: The underwater robot uses a binocular camera to acquire collected image information in real time, and uses a target detection model to detect the number and type of target samples in the image information in real time; Determine the underwater posture information of the target sample type identified according to the image information, and automatically generate action instructions for a sampling mechanism corresponding to the target sample type according to the target sample type and the posture information; Automatically detecting the depth information of the target sample of the corresponding type relative to the binocular camera underwater according to the binocular camera, and automatically adjusting the action posture of the corresponding sampling mechanism according to the depth information of the target sample and the posture information of the target sample; Determine whether sampling is successful according to the corresponding type sensor of the sampling mechanism; if sampling fails, re-use the target detection model to track and identify the depth information and posture information of the target sample, and generate a new action posture of the corresponding sampling mechanism according to the re-identified depth information and posture information of the target sample; If the sampling is successful, the depth information of all samples identified in the image information is obtained, and the sample with the smallest depth information is selected as a new target sample for sampling operation.

2. The underwater robot sample identification and grasping method according to claim 1, characterized in that: Obtain image information collected by the binocular camera, and input the collected image information into a trained YOLO model for target detection, and use the YOLO model to obtain a sample image area present in the image information, wherein the identified sample image area is segmented into rectangles according to the YOLO model to obtain a target sample block image; The KCF algorithm is used to extract the corresponding gradient histogram of each segmented target sample block image, and a circulant matrix describing the sample image area is constructed according to the corresponding gradient histogram. A sliding detection window of the circulant matrix is ​​constructed, and the similarity between the segmented target sample block image and the target sample is detected according to the sliding window and the trained classifier. If the similarity is greater than a preset threshold, it is tracked in real time as a target sample.

3. The underwater robot sample identification and grasping method according to claim 1, characterized in that: A driving instruction binding relationship corresponding to underwater sample types and sampling mechanism types is pre-constructed, wherein the sampling types include aquatic plants, aquatic animals, underwater rocks, and underwater mud, wherein target samples of different sampling types correspond to different sampling mechanisms, and after the sample type is identified by the YOLO model and used as the target sample, a driving instruction of the corresponding type of sampling mechanism is generated according to the driving instruction binding relationship to perform the sampling operation of the target sample.

4. The underwater robot sample identification and grasping method according to claim 1, characterized in that: The method for acquiring the posture information of the target sample includes: after obtaining the target sample block image in the image information according to the YOLO model recognition, edge detection is performed on the target sample block image using the Roberts operator to obtain the detected edge pixel features of the target sample in the target sample block image; the Roberts operator is used to pre-detect the standard edge pixel features of different postures of the standard samples of the corresponding sample type, the similarity of the detected edge pixel features and the standard edge pixel features is calculated, and the standard edge pixel features of the standard sample posture with the highest similarity are selected as the posture of the target sample.

5. The underwater robot sample identification and grasping method according to claim 4, characterized in that: The method for generating action instructions for the sampling mechanism includes: configuring at least one edge entry point in the standard edge pixel features of the standard sample posture of the corresponding sample type, and generating action instructions including rotation, movement, and clamping for different control joints of the sampling mechanism according to the edge entry points of the matched standard sample posture of the corresponding sample type, so that the action instructions will execute the sampling strategy of the corresponding target sample according to the edge entry points.

6. The underwater robot sample identification and grasping method according to claim 1, characterized in that: The method for acquiring depth information of the target sample includes: acquisition includes: using a binocular camera to respectively identify and acquire the target sample block image according to the YOLO model, and calculating the coordinate values ​​of the pixels of the target sample block image in the corresponding cameras, respectively calculating the average coordinate values ​​n1 and n2 of all pixels of the target sample block image in the two cameras, and calculating the disparity d=(n2-n1) / B according to the average coordinate values ​​n1 and n2 in the two cameras, wherein B represents the baseline distance between the binocular cameras, and further calculating the depth information Z of the target sample relative to the binocular camera according to the disparity d.

7. The underwater robot sample identification and grasping method according to claim 6, characterized in that: The method for calculating the depth information Z of the target sample includes: obtaining the grayscale value information of the target sample block image, and filtering out the grayscale features of the target sample according to the grayscale value information, performing feature matching according to the target sample grayscale features to obtain the average coordinate values ​​n1 and n2 of all pixels of the target sample in the two cameras, and calculating the disparity d, obtaining the focal length f of the binocular camera, and calculating the depth information Z=f×B / d of the target sample block image relative to the binocular camera according to the calculated disparity d using the triangulation method.

8. The underwater robot sample identification and grasping method according to claim 6, characterized in that: After acquiring the depth information Z of the target sample block image and the posture information of the corresponding target sample, the robot body and / or the sampling mechanism are driven to approach the target sample according to the depth information Z of the block image. When the depth information reaches a suitable range, a sampling strategy of the corresponding sampling mechanism is further generated according to the edge entry point in the posture information of the target sample.

9. An underwater robot sample grabbing and identification system, characterized in that: The system executes an underwater robot sample identification and grasping method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the underwater robot sample identification and grasping method according to any one of claims 1 to 8.

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