A method and system for self-adaptive scanning of carotid artery by mechanical arm based on ultrasonic image

Abstract

The application discloses a kind of based on ultrasonic image's mechanical arm adaptive scanning carotid artery method and system, belong to medical robot technical field.The method includes: obtaining ultrasonic image;Based on the obtained ultrasonic image, the outline center point of carotid artery is detected, and the pixel coordinates of the outline center point of carotid artery are calculated;Utilize the preliminary path planned and step by point and step scanning, according to the imaging effect feedback of ultrasonic image, the position and direction of next path point are adjusted in real time, to update the remaining path container point value, simultaneously based on fuzzy adaptive PID algorithm, the force and position when mechanical arm moves are compensated control.The application has the beneficial effects as follows: carotid artery autonomous diagnosis is realized, the technical requirement of detection personnel is very low, and the position of carotid artery is found by mechanical arm according to signal autonomously and intelligently, is adjusted in real time, is suitable for different crowd experience, and generalization is stronger.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of medical robots, and particularly relates to a mechanical arm adaptive carotid artery scanning method and system based on an ultrasonic image. BACKGROUND

[0002] Carotid artery scanning is a relatively important examination method. Carotid artery scanning can check the degree of systemic arteriosclerosis and the brain blood supply condition to determine the automatic treatment plan. By observing the plaque in the carotid artery and the blood flow rate, not only can atherosclerotic diseases be diagnosed, but also cardiovascular and cerebrovascular diseases can be evaluated. In addition, doctors also need to diagnose and treat the treatment plan according to the results of carotid artery examination to achieve the purpose of relieving the disease, so the examination is very important in the field of medical detection.

[0003] The mechanical arm in the existing medical field is mainly applied to nursing, physiotherapy and palletizing functions, and mainly realizes a simple open-loop path guiding function. The movement needs to be performed in advance according to a depth camera or a laser radar or a fixed position direction. This method is not suitable for complex and variable target path planning. The carotid artery scanning path is such a multi-directional and person-specific path planning task.

[0004] Therefore, the application provides a mechanical arm adaptive carotid artery scanning method and system based on an ultrasonic image to solve the above problems. SUMMARY

[0005] The purpose of the embodiment of the application is to provide a mechanical arm adaptive carotid artery scanning method and system based on an ultrasonic image. The carotid artery ultrasonic examination is performed based on the existing portable palm ultrasonic device. The whole examination process is replaced by the operation of a professional doctor by a mechanical arm. The carotid artery position is automatically found. The feedback information of the end pressure sensor is used to adaptively adjust to a suitable and comfortable force range. The imaging path is adaptively adjusted according to the ultrasonic image feedback to maximize the effective area of ultrasonic imaging, thereby greatly reducing the use threshold of the examination device, and at least one technical problem in the background art can be solved.

[0006] To solve the above technical problems, the application is implemented as follows:

[0007] The embodiment of the application provides a mechanical arm adaptive carotid artery scanning method based on an ultrasonic image, which comprises the following steps.

[0008] Step S1: acquiring an ultrasonic image;

[0009] Step S2: detecting a profile center point of a carotid artery based on the acquired ultrasonic image, and calculating a pixel coordinate value of the profile center point of the carotid artery;

[0010] Step S3: Plan the initial path of the robotic arm based on the obtained pixel coordinate values;

[0011] Step S4: Using the planned preliminary path, scan point by point, and adjust the position and direction of the next path point in real time according to the ultrasound image imaging effect feedback, thereby updating the value of the remaining path container points. At the same time, the force and position of the robotic arm during movement are compensated and controlled based on the fuzzy adaptive PID algorithm.

[0012] Optionally, in step S1, acquiring the ultrasound image includes:

[0013] The robotic arm scans along the transverse path;

[0014] The ultrasonic probe at the end of the robotic arm is used to image the human neck, and ultrasonic images of the current probe position in the left and right fields of view are obtained.

[0015] After obtaining ultrasound images from the left and right visual fields, the following is also included:

[0016] Based on the principle of three-dimensional measurement, the three-dimensional information of each pixel in the left visual field image is calculated to obtain a three-dimensional information matrix;

[0017] Construct a DeepLabV3+ semantic segmentation network;

[0018] Ultrasound images are input into the DeepLabV3+ semantic segmentation network to achieve accurate segmentation of the human body and output the segmentation results;

[0019] Based on the segmentation results, obtain the pixel coordinates of the human body parts;

[0020] A mask is constructed based on the obtained pixel coordinates. The mask is then used to index the three-dimensional information matrix to obtain the spatial coordinates and normal vectors of each point on the human body.

[0021] A point cloud is constructed based on the obtained spatial coordinates and normal vectors to achieve three-dimensional reconstruction of the human body.

[0022] Optionally, in step S2, the step of detecting the center point of the carotid artery contour based on the acquired ultrasound image and calculating the pixel coordinates of the center point of the carotid artery contour includes:

[0023] Based on the acquired ultrasound images, the spatial moments, central moments, and normalized central matrix of the ultrasound image contours are calculated to detect neck ultrasound images.

[0024] Based on the zeroth and first moments, the center point of the carotid artery contour (c) is obtained. x c y The pixel coordinates of ).

[0025] Optionally, in step S3, the preliminary path of the mechanical arm is planned according to the acquired pixel coordinate values, including:

[0026] Step S31: judging the horizontal coordinate c of the center point of the contour x is located in the interval of 124-132, if yes, step S32 is executed;

[0027] Step S32: recording the traversal serial number i of the current path point, and passing to the path planner to plan the longitudinal cutting path of the mechanical arm, according to the current recorded serial number i, the initial coordinate value of the longitudinal cutting path is obtained by interpolation method under pixel coordinates, and the path is planned upward according to the initial coordinate value:

[0028]

[0029] y start2 = y start1

[0030] wherein (x star , y start ) is the initial position of the transverse cutting path, (x start2 , y start2 ) is the initial position of the longitudinal cutting path, and step is the total step length.

[0031] Optionally, in step S4, the planned preliminary path is used for point-by-point step scanning, and the position and direction of the next path point are adjusted in real time according to the feedback of the ultrasonic image imaging effect, so as to update the remaining path container point value, including:

[0032] The obtained longitudinal cutting initial path point (x star , y star ) is used for preliminary path planning, a certain angle θ is used to deflect upward to obtain the mechanical arm path point container, and the container value is used for movement, and the initial step point coordinate is as follows:

[0033]

[0034] wherein (x i , y i ) is the pixel coordinate of the i-th point of the longitudinal cutting path, and θ is the deflection angle relative to the x-axis.

[0035] Optionally, in step S4, the fuzzy self-adaptive PID algorithm is represented by the following formula:

[0036]

[0037] Δu(k) = k p (e(k)-e(k-1))+k i e(k)+k d(e(k)-2e(k-1)+e(k-2))

[0038] wherein e(k) is an error; Δu(k) is an error rate of change;

[0039] The compensation control is represented by the following formula:

[0040] q r = q d -τ / K p1

[0041] wherein K p1 is a direct force feedback parameter, taking 0.001m; τ is a control rate coefficient.

[0042] The application also provides a system for performing the method, the system comprising:

[0043] an ultrasonic image acquisition module, configured to acquire an ultrasonic image;

[0044] a pixel coordinate acquisition module, configured to detect a profile center point of the carotid artery based on the acquired ultrasonic image, and calculate a pixel coordinate value of the profile center point of the carotid artery;

[0045] a preliminary path planning module, configured to plan a preliminary path of the mechanical arm according to the acquired pixel coordinate value;

[0046] a movement control module, configured to perform point-by-point step scanning by using the planned preliminary path, adjust the position and direction of a next path point in real time according to the ultrasonic image imaging effect feedback, update the remaining path container point value, and perform compensation control on the force and position of the mechanical arm during movement based on a fuzzy adaptive PID algorithm.

[0047] The application has the following beneficial effects:

[0048] 1. The carotid artery is autonomously diagnosed, the technical requirements for the detection personnel are low, the mechanical arm is used to autonomously and intelligently find the carotid artery position according to the signal, real-time adjustment is performed, it is suitable for different people to experience, and the generalization is strong.

[0049] 2. The threshold of traditional professional physician examination is broken, and it is undoubtedly a great blessing for small hospitals and daily physical lesion screening. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is a flowchart of the method for adaptively scanning a carotid artery by a mechanical arm based on an ultrasonic image provided by the application;

[0051] Figure 2 is a schematic diagram of a scanning overall path provided by the application;

[0052] Figure 3 is a schematic diagram of a fuzzy adaptive PID control for realizing contact force compliance control provided by an embodiment of the present application.

[0053] Figure 4 is a schematic diagram of initial path planning of a slitting path provided by an embodiment of the present application.

[0054] Figure 5 is a system structure block diagram of a mechanical arm adaptive scanning carotid artery based on an ultrasound image provided by an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0056] The terms “first”, “second”, and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by “first”, “second”, etc. are generally a category and do not limit the number of objects, for example, the first object can be one or more. In addition, “and / or” in the specification and claims indicates at least one of the connected objects, and the character “ / ” generally indicates that the front and rear associated objects are in an “or” relationship.

[0057] The method for adaptive scanning of carotid artery by a mechanical arm based on an ultrasound image provided by an embodiment of the present application will be described in detail below with reference to the drawings, through specific embodiments and application scenarios.

[0058] Please refer to Figure 1 is a method for adaptive scanning of carotid artery by a mechanical arm based on an ultrasound image provided by an embodiment of the present application, comprising:

[0059] Step S1: acquiring an ultrasound image;

[0060] Step S2: detecting a profile center point of the carotid artery based on the acquired ultrasound image, and calculating a pixel coordinate value of the profile center point of the carotid artery;

[0061] Step S3: planning a preliminary path of the mechanical arm according to the acquired pixel coordinate value;

[0062] Step S4: using the planned preliminary path to step by step scan, adjusting the position and direction of the next path point in real time according to the ultrasonic image imaging effect feedback, updating the remaining path container point value, and compensating the force and position of the mechanical arm movement based on the fuzzy adaptive PID algorithm, so as to meet the comfortable experience of different groups of people in the detection process.

[0063] In combination Figure 2 As shown in step S1, the ultrasonic image is acquired, including:

[0064] The mechanical arm scans according to the cross-section path;

[0065] The ultrasonic probe at the end of the mechanical arm is used to image the neck of the human body to obtain the ultrasonic image of the current probe position in the left and right fields of view;

[0066] After obtaining the ultrasonic image in the left and right fields of view, further comprising:

[0067] Based on the three-dimensional measurement principle, the three-dimensional information of each pixel point in the left field of view image is calculated to obtain a three-dimensional information matrix;

[0068] The DeepLabV3+ semantic segmentation network is constructed, and it should be noted that the network mainly consists of two parts of encoding and decoding, and can realize pixel-level segmentation of the target object. The input of the network is an RGB color image, and the output is the segmentation result of the target object in the image;

[0069] The ultrasonic image is input into the DeepLabV3+ semantic segmentation network to realize accurate segmentation of the human body and output the segmentation result;

[0070] Based on the segmentation result, the pixel point coordinates belonging to the human body part are obtained;

[0071] Based on the obtained pixel point coordinates, a mask is constructed, and the mask is used to index in the three-dimensional information matrix to obtain the spatial coordinates and normal vectors of each point of the human body;

[0072] Based on the obtained spatial coordinates and normal vectors, a point cloud is constructed to realize three-dimensional reconstruction of the human body.

[0073] In combination Figure 3 As shown in step S2, based on the obtained ultrasonic image, the profile center point of the carotid artery is detected, and the pixel coordinate value of the profile center point of the carotid artery is calculated, including:

[0074] Based on the obtained ultrasonic image, the spatial moment, center moment and normalized center moment matrix of the ultrasonic image profile are calculated, and the neck ultrasonic image is detected;

[0075] It should be noted that due to the different widths of the neck and blood vessels of different groups of people, the mechanical arm scans according to the initial cross-section path (such asFigure 2 The ultrasound image is acquired at a rate of 10 frames per second, and the center point of the carotid artery is detected in the threshold region of the image center point, i.e., the current probe center is just opposite to the carotid artery position, and the mechanical arm is given a pause button. The path is planned again at the current position and moved upward, so that the scanning is along the direction of the mechanical arm to achieve the purpose of tracking. Specifically, the center point of the carotid artery is detected using the OpenCV-based contour detection, and the spatial moment, center moment and normalized center moment matrix of the image contour are calculated using cv2.moment().

[0076] According to the zeroth moment and the first moment, the pixel coordinates of the contour center points (c x , c y ) of the carotid artery are obtained.

[0077] It should be noted that the center distance has translational invariance, which can ignore the positional relationship of the two objects and help us compare the consistency of the two objects at different positions. For example, in many cases, we want to compare the consistency of the two objects at different positions. The method to solve this problem is to introduce the center moment. The center moment obtains translational invariance by subtracting the mean value, so it can compare whether the two objects at different positions are consistent.

[0078] The normalized center moment has translational and scaling invariance, which considers the consistency of objects of different sizes after scaling. That is, we want the image to have the same feature value before and after scaling. Obviously, the center moment does not have this property. For example, two objects with the same shape and different sizes have different center moments. The normalized center moment obtains scaling invariance by dividing the total size of the object. Specifically, it includes the following moment features, as shown in Table 1.

[0079] Table 1

[0080]

[0081] Based on Table 1, the pixel coordinates of the contour center points c x and c y of the carotid artery are obtained according to the zeroth moment and the first moment, respectively:

[0082]

[0083]

[0084] where c x is the horizontal coordinate and c y is the vertical coordinate.

[0085] In step S3, the preliminary path of the mechanical arm is planned according to the obtained pixel coordinate values, including:

[0086] Step S31: judging the horizontal coordinate c of the center point of the contour x is located in the interval of 124-132, if yes, then step S32 is executed;

[0087] Step S32: recording the traversal serial number i of the current path point, and passing it to the path planner to plan the vertical cutting path of the mechanical arm, according to the current recorded serial number i, the starting coordinate value of the vertical cutting path is obtained according to the interpolation method under the pixel coordinate, and the path is planned upward according to the starting coordinate value:

[0088]

[0089] y start2 = y sta

[0090] wherein (x sta , y star ) is the starting position of the horizontal cutting path, (x start2 , y start ) is the starting position of the vertical cutting path, and step is the total step length.

[0091] In step S4, the planned preliminary path is used to scan point by point, and the position and direction of the next path point are adjusted in real time according to the feedback of the ultrasonic image imaging effect, so as to update the remaining path container point value, including:

[0092] The obtained vertical cutting initial path point (x start , y start2 ) is used for preliminary path planning, and the path point container of the mechanical arm is obtained by stepping upward at a certain angle θ, and the mechanical arm moves according to the container value, combined with Figure 3 , the starting step point coordinates are as follows:

[0093]

[0094] wherein (x i , y i ) is the pixel coordinate of the i-th point of the vertical cutting path, and θ is the deflection angle relative to the x-axis.

[0095] Figure 2 Second segment path initial path planning

[0096] Specifically, due to the different neck artery directions, neck thickness and other degrees of different people, the initial straight path cannot meet the needs of the picture, and the motion path of the robot arm needs to be adjusted in real time during the tracking process. The next path point information is inseparable from the current path point and the last path point information, so a dynamic programming algorithm is used to adjust the position and posture information of the next point on the original planning path according to the information of the last motion path point. Dynamic programming (DP) is a branch of operations research, which is a process of solving the optimization of decision-making process. Dynamic programming is widely used in engineering technology, economy, industry, military and automation control fields, and has achieved remarkable results in knapsack problem, production and operation problem, fund management problem, resource allocation problem, shortest path problem and complex system reliability problem. In this application, the following conditions are considered: ① the left and right position offset position relative to the center position; ② the angle offset posture relative to the center position; ③ the end force touch feedback adjustment, which is too large to make the patient feel uncomfortable, and too small to cause the phenomenon of blocking the artery by the vein. The recursive formula is as follows:

[0097] dp[i][j][k]=dp[i-1][j][k]*s position +dp[i][j-1][k]*s posture +dp[i][j][k-1]*s force

[0098] Where s position , s posture , s force are the deviation information returned by the last path point of the ultrasound image, respectively position deviation, angle deviation and force compensation.

[0099] Due to the small and soft neck position and the dense surrounding blood vessels, and the different needs of different people for carotid artery scanning, the force and path are different for fat and thin people and people of different ages. In terms of contact force during scanning, too large force will cause discomfort to the patient. Since the venous blood vessels are relatively soft and usually located above the carotid artery on the skin surface, too small contact force can easily scan the vein, and cannot get ideal diagnostic results or even misdiagnosis, so the force control should be increased within a suitable range, and the force size should be controlled within a suitable range. According to the clinical guidance of professional physicians and our repeated tests, the scanning effect is best when the force and position compensation have the following regular changes:

[0100] q r =q d -τ / K p1

[0101] wherein, K p1 is a direct force feedback parameter, taking 0.001 m; τ is a control rate coefficient.

[0102] In this way, the size of the contact compensation distance is increased by 0.001 m each time as the path changes, and the force gradually increases by about 0.6 N, which is the process of fuzzy reasoning.

[0103] Specifically, as shown in Figure 3 , the force size in the range is realized. The method used in the present application is a fuzzy adaptive PID algorithm. Fuzzy adaptive PID control is based on the PID algorithm, takes error e and error change rate ec as input, uses fuzzy rules for fuzzy reasoning, queries the fuzzy matrix table for parameter adjustment, and meets the requirements of e and ec at different times for PID parameter self-adjustment. The deviation calculation formula method in the discrete case, i.e., the fuzzy adaptive PID algorithm, is as follows:

[0104]

[0105] Δu(k)=k p (e(k)-e(k-1))+k i e(k)+k d (e(k)-2e(k-1)+e(k-2))

[0106] Fuzzy adaptive PID control takes error e(k) and error change rate Δu(k) as input, which can meet the force touch size control of the mechanical arm end at different times to meet the requirements of PID parameter self-adjustment. Modifying the PID parameters using fuzzy rules is the structure of fuzzy adaptive PID control.

[0107] The present application also provides an ultrasonic image-based mechanical arm adaptive scanning carotid artery system for executing the method, which comprises an ultrasonic image acquisition module 1, a pixel coordinate acquisition module 2, a preliminary path planning module 3, and a movement control module 4.

[0108] The ultrasonic image acquisition module 1 is used to acquire an ultrasonic image.

[0109] The pixel coordinate acquisition module 2 is used to detect the outline center point of the carotid artery based on the acquired ultrasonic image, and calculate the pixel coordinate value of the outline center point of the carotid artery.

[0110] The preliminary path planning module 3 is used to plan a preliminary path of the mechanical arm according to the acquired pixel coordinate value.

[0111] The movement control module 4 is used for point-by-point step scanning with a planned preliminary path, real-time adjustment of the position and direction of the next path point according to the feedback of ultrasonic image imaging effect, updating of the remaining path container point value, and compensation control of the force and position of the mechanical arm during movement based on a fuzzy adaptive PID algorithm.

[0112] The beneficial effects of the present application are as follows:

[0113] 1. The carotid artery is autonomously diagnosed, the technical requirements for the detection personnel are very low, the mechanical arm is used to autonomously and intelligently find the carotid artery position according to the signal, real-time adjustment is adopted, it is suitable for different people to experience, and the generalization is relatively strong.

[0114] 2. The threshold of traditional professional physician examination is broken, and it is undoubtedly a great blessing for small hospitals and daily physical lesion screening.

[0115] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above specific embodiments, the above specific embodiments are only illustrative but not restrictive, and those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope protected by the claims.

Claims

1. A method for a robotic arm to adaptively scan a carotid artery based on ultrasound images, the method comprising: Comprise: Step S1: obtaining an ultrasound image; Step S2: based on the obtained ultrasound image, detecting the profile center point of the carotid artery, calculating the pixel coordinate value of the profile center point of the carotid artery; Step S3: according to the obtained pixel coordinate value, planning the preliminary path of the mechanical arm; Step S4: using the planned preliminary path to step by point scanning, at each step point, according to the imaging effect feedback of the current ultrasound image, the position and direction of the next path point are adjusted in real time by dynamic programming algorithm, and the remaining path container point value is updated, at the same time, the force and position of the mechanical arm during movement are compensated and controlled based on fuzzy adaptive PID algorithm; Wherein, the imaging effect feedback includes position deviation, angle deviation and force compensation situation, the recursive formula of the dynamic programming algorithm is: wherein, respectively the position deviation, the angle deviation and the force compensation information returned by the ultrasound image under the previous path point.

2. The method of claim 1, wherein, In step S1, the ultrasound image is obtained, including: The mechanical arm scans according to the transverse path; The ultrasound probe at the end of the mechanical arm is used to image the neck of the human body to obtain the ultrasound image of the current probe position in the left and right fields of view; After obtaining the ultrasound image of the left and right fields of view, it also includes: Based on the principle of three-dimensional measurement, the three-dimensional information of each pixel point in the left field of view image is calculated to obtain a three-dimensional information matrix; DeepLabV3+ semantic segmentation network is constructed; The ultrasound image is input into the DeepLabV3+ semantic segmentation network to realize accurate segmentation of the human body and output the segmentation result; Based on the segmentation result, the pixel point coordinates belonging to the human body part are obtained; Based on the obtained pixel point coordinates, a mask is constructed, and the mask is used to index in the three-dimensional information matrix to obtain the spatial coordinates and normal vector of each point of the human body; Based on the obtained spatial coordinates and normal vector, a point cloud is constructed to realize three-dimensional reconstruction of the human body.

3. The method of claim 2, wherein, In step S2, the profile center point of the carotid artery is detected based on the obtained ultrasound image, and the pixel coordinate value of the profile center point of the carotid artery is calculated, including: Based on the obtained ultrasound image, the spatial moment, central moment and normalized central moment matrix of the ultrasound image profile are calculated, and the carotid ultrasound image is detected; According to the zeroth moment and the first moment calculation, the pixel coordinates of the center point of the carotid artery profile (X0, Y0) are obtained. , ​ 4. The method of claim 3, wherein, In step S3, the preliminary path of the mechanical arm is planned according to the obtained pixel coordinate value, including: Step S31: judging the abscissa of the profile center point is located in the interval of 124-132, if so, then step S32 is executed; Step S32: record the traversal serial number i of the current path point and pass it to the path planner to plan the longitudinal path of the mechanical arm, and according to the current record serial number i, the starting coordinate value of the longitudinal path is obtained by interpolation method under pixel coordinate, and the path is planned upwards: wherein is the start position of the cross-cut path, is the start position of the longitudinal-cut path, and step is the total step length.

5. The method of claim 4, wherein, In step S4, the planned preliminary path is used to scan step by point, and the position and direction of the next path point are adjusted in real time according to the ultrasound image imaging effect feedback, and the remaining path container point value is updated, including: Utilizing the resulting slit initial path points A preliminary path plan is performed, and the arm path point container is stepped up by deflecting at an angle θ.

6. The method of claim 5, wherein, In step S4, the fuzzy adaptive PID algorithm is represented by the following formula: wherein is the error; and Δu(k) is the error rate of change; The compensation control is represented by the following formula: wherein, is the direct force feedback parameter, taken as 0.001 m; τ is the control rate coefficient.

7. A system for performing the method of any one of claims 1-6, the system comprising an ultrasound image-based robotic arm adaptive carotid scan, wherein, The system comprises: An ultrasound image acquisition module for acquiring an ultrasound image; A pixel coordinate acquisition module for detecting the profile center point of the carotid artery based on the obtained ultrasound image, and calculating the pixel coordinate value of the profile center point of the carotid artery; A preliminary path planning module for path planning according to the obtained pixel coordinate value, three-dimensional point cloud retrieval, and obtaining the moving path container of the mechanical arm; The mobile control module is used for step-by-step scanning with a planned preliminary path, at each step point, the position and direction of the next path point are adjusted in real time according to the imaging effect feedback of the current ultrasonic image through a dynamic programming algorithm, and the remaining path container point value is updated, and the force and position of the mechanical arm during movement are compensated and controlled based on a fuzzy adaptive PID algorithm.

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