Mechanical arm control system and method

Abstract

The invention provides a mechanical arm control system and method, and relates to the technical field of intelligent assembly. The system comprises a master control end, an execution control end and a target recognition device, and the method comprises the steps that a tail end node of the mechanical arm is positioned, the geometrical relationship between the tail end node and a corner node is established, and corner node information of the mechanical arm moving from a starting point to an end point is further determined through an inverse kinematics algorithm and the geometrical relationship; the rotating speed and the rotating angle of a stepping motor of the mechanical arm are controlled, accurate positioning control over movement of the mechanical arm is achieved, a target recognition device is installed at a tail end node of the mechanical arm, world coordinates are converted into pixel coordinates, the distance between the mechanical arm and a target object is determined, more accurate target detection and positioning are achieved, and the accuracy of target detection is improved. And the autonomous processing capacity and the assembling efficiency are improved. Through the combination of the master control end, the execution control end and the target recognition device, the mechanical arm is remotely controlled, and high-automation target recognition grabbing and placing are achieved under the condition of low cost.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of intelligent assembly, and in particular to a robot arm control system and method. Background Art

[0002] With the development of artificial intelligence and sensor technology, the application of robotic arms has become more and more extensive, which has put forward higher requirements for the precise positioning and target recognition of robotic arms. Traditional robotic arms are expensive and complex to control. The longer arm has deficiencies in workspace and operational flexibility, increases inertia and friction, and affects accuracy and sensitivity. Especially in the field of assembly manufacturing, the position accuracy of the robotic arm directly affects the assembly accuracy. Traditional robotic arms usually require accurate programming and settings, and have weak autonomous processing capabilities for non-preset tasks or environmental changes. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a robot arm control system and method in view of the deficiencies in the prior art.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: a mechanical arm control system, comprising a general control end, an execution control end and a target recognition device, wherein the execution control end is installed on the mechanical arm, the general control end is connected to the execution control end, and the target recognition device is connected to the execution control end and the general control end respectively;

[0005] The general control terminal is used to receive a control request sent by an operator, generate a first positioning instruction through the control request and send it to the target recognition device;

[0006] The target recognition device is used to image the target object according to the first positioning instruction to form a target object image, obtain target positioning information based on the target object image, and send it to the general control terminal to generate a second positioning instruction;

[0007] The execution control end is used to perform real-time positioning of the end node of the robot arm according to the second positioning instruction, obtain the end positioning information, and send it to the general control end;

[0008] The general control end is used to determine whether the target positioning information and the terminal positioning information are consistent, and if so, generate a grasping instruction; if not, calculate the corner node information of the robot arm based on the inverse kinematics algorithm, the target positioning information and the terminal positioning information, and generate a moving instruction according to the corner node information;

[0009] The execution control end is used to perform a grabbing operation on the target object according to the grabbing instruction; and is also used to control the robot arm to move to the target object according to the moving instruction, and when reaching the target object, perform a grabbing operation on the target object.

[0010] Another technical solution of the present invention to solve the above technical problem is as follows: a robot arm control method, applied to the robot arm control system, comprising a general control end, an execution control end and a target recognition device, wherein the execution control end is installed on the robot arm, the general control end is connected to the execution control end, and the target recognition device is connected to the execution control end and the general control end respectively;

[0011] The general control end receives a control request sent by an operator, generates a first positioning instruction through the control request and sends it to the target recognition device;

[0012] The target recognition device images the target object according to the first positioning instruction to form a target object image, obtains target positioning information based on the target object image, and sends the target positioning information to the general control terminal to generate a second positioning instruction;

[0013] The execution control end performs real-time positioning of the end node of the robot arm according to the second positioning instruction, obtains the end positioning information, and sends it to the general control end;

[0014] The general control end determines whether the target positioning information and the terminal positioning information are consistent. If so, a grasping instruction is generated. If not, the turning angle node information of the manipulator is calculated based on the inverse kinematics algorithm, the target positioning information and the terminal positioning information, and a moving instruction is generated according to the turning angle node information.

[0015] The execution control end performs a grabbing operation on the target object according to the grabbing instruction; and is also used to control the robot arm to move to the target object according to the moving instruction, and when reaching the target object, performs a grabbing operation on the target object.

[0016] The beneficial effects of the present invention are as follows: through the combination of the main control end, the execution control end and the target recognition device, remote control of the robotic arm is realized, and highly automated target recognition, grasping and placement are achieved under low cost conditions, so that the invention can efficiently and accurately complete the target grasping and placement tasks, and can replace manual labor in different work scenarios, thereby improving production efficiency and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the connection of various functional components in a robotic arm control system provided by an embodiment of the present invention;

[0018] Figure 2A diagram showing the relationship between the rotation angles of the mechanical arm provided by an embodiment of the present invention;

[0019] Figure 3 A trajectory diagram of the end node of the robotic arm provided by an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of the structure of a mechanical arm provided in an embodiment of the present invention;

[0021] Figure 5 A schematic diagram of the structure of a slide module provided in an embodiment of the present invention;

[0022] Figure 6 A transmission principle diagram of a slide module provided in an embodiment of the present invention;

[0023] Figure 7 A schematic diagram of camera coordinate system conversion provided by an embodiment of the present invention;

[0024] Figure 8 A schematic flow chart of a robot arm control method provided by an embodiment of the present invention;

[0025] Fig. 9 A control flow chart provided for an embodiment of the present invention.

[0026] In the accompanying drawings, the names of the components represented by the various marks are as follows:

[0027] 1. Robotic arm; 2. Camera; 3. Mechanical gripper; 4. Slide module; 4-1. Stepper motor; 4-2. Displacement sensor; 4-3. Threaded rod; 4-4. Double linear rail. DETAILED DESCRIPTION

[0028] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0029] like Figure 1-Figure 4 As shown, an embodiment of the present invention provides a robotic arm control system, comprising a general control end, an execution control end and a target recognition device, wherein the general control end is connected to the execution control end, and the target recognition device is respectively connected to the execution control end and the general control end;

[0030] The general control terminal is used to receive a control request sent by an operator, generate a first positioning instruction through the control request and send it to the target recognition device;

[0031] The target recognition device is used to image the target object according to the first positioning instruction to form a target object image, obtain target positioning information based on the target object image, and send it to the general control terminal to generate a second positioning instruction;

[0032] The execution control end is used to perform real-time positioning of the end node of the robot arm according to the second positioning instruction, obtain the end positioning information, and send it to the general control end;

[0033] The general control end is used to determine whether the target positioning information and the terminal positioning information are consistent, and if so, generate a grasping instruction; if not, calculate the corner node information of the robot arm based on the inverse kinematics algorithm, the target positioning information and the terminal positioning information, and generate a moving instruction according to the corner node information;

[0034] The execution control end is used to perform a grabbing operation on the target object according to the grabbing instruction; and is also used to control the robot arm to move to the target object according to the moving instruction, and when reaching the target object, perform a grabbing operation on the target object.

[0035] In the above embodiment, remote control of the robotic arm 1 is achieved through the combination of the main control end, the execution control end and the target recognition device, and highly automated target recognition, grasping and placement are achieved at a relatively low cost, so that the invention can efficiently and accurately complete the target grasping and placement tasks, and can replace manual labor in different work scenarios, thereby improving production efficiency and reducing costs.

[0036] The main control terminal can be a PC terminal, and the execution control terminal is equipped with an STM32 main control chip. The execution control terminal is installed on the robot arm 1 and is electrically connected to the main control terminal. The target recognition device is installed on the robot arm 1 and is electrically connected to the execution control terminal and the main control terminal respectively. The electrical connection can be through a data cable connection, or through a wireless connection such as a 4G / 5G signal, Bluetooth, or WiFi.

[0037] Based on the above embodiments, preferably, the mechanical gripper is a replaceable mechanical gripper used for gripping and placing by a robotic arm.

[0038] like Figure 7 As shown, preferably, in the target recognition device, the target object is imaged to form a target object image, and the target positioning information is obtained based on the target object image, specifically:

[0039] Projecting the grating onto the surface of the target object, and imaging the target object using an imaging element based on the grating to form an image of the target object;

[0040] The target object is converted from world coordinates to pixel coordinates based on the target object image and the coordinate conversion formula to obtain the target positioning information. The coordinate conversion formula is:

[0041]

[0042] Among them, X w,Y w ,Z w is the point of the target object in the world coordinate system, and u, v are the points of the target object in the image coordinate system.

[0043] Specifically, first, the internal and external parameters of the imaging element (camera) are determined through camera calibration, wherein the internal parameters include focal length, principal point coordinates, etc., while the external parameters involve the position and posture of the camera.

[0044] The internal parameter matrix is ​​usually expressed as:

[0045]

[0046] The external parameters describe the position and attitude of the camera relative to the world coordinate system, usually represented by the rotation matrix R and the translation vector t.

[0047] like Figure 2 As shown, preferably, in the general control end, the corner node information of the mechanical arm is calculated based on the inverse kinematics algorithm, the target positioning information and the end positioning information, specifically:

[0048] A geometric relationship between the end node of the robotic arm and the corner node of the robotic arm is established according to the end positioning information and the working range of the robotic arm, specifically:

[0049] Next, let the end node of the robot be S 3 , the corner node of the robot arm is S 1 and S 2 , S 1 Located at the front end of the robotic arm, S 2 Located in S 1 and S 3 Between; Assume that the working range of the robot arm is [l 1 -l 2 ,l 1 +l 2 ], where l 1 For S 1 With S 2 The distance between 2 For S 2 With S 3 The distance between 1 Establish a coordinate system for the origin, S 1 With S 2 The angle between the robot arm and the vertical axis is θ 1 , S 1 With S 2 The robot arm and S 2 With S 3 The angle between the two arms is θ 2, given the end positioning information of the end node of the robot arm is (L,H), where L is the horizontal coordinate and H is the vertical coordinate,

[0050] Establish the geometric relationship between the end node of the robotic arm and the corner node of the robotic arm:

[0051]

[0052] Among them, l 1 >l 2 , the working range of the robot arm is [l 1 -l 2 ,l 1 +l 2 ].

[0053] Assuming the target positioning information as the end point, the corner node information of the end node moving from the starting point to the middle point and the middle point moving to the end point is determined based on the inverse kinematics algorithm and the geometric relationship, specifically:

[0054] Based on the inverse kinematics algorithm and the geometric relationship, the rotation angle of each corner node of the end node moving from the starting point to the middle point is calculated,

[0055] The rotation angle of each corner node of the terminal node moving from the middle point to the end point is calculated based on the inverse kinematics algorithm and the geometric relationship. The calculation formula of the inverse kinematics algorithm is:

[0056]

[0057] Where L>0, S 2 With S 3 The angle between the robot arm and the horizontal axis is θ 3 ,θ 3 The angle of the actuator at the end node, and ensure that the actuator at the end node is in a horizontal state, θ 1 and θ 2 is the corner node S 1 With corner node S 2 The rotation angle of .

[0058] In the above embodiment, for the end node S 3 The starting and ending coordinates of the corner node S are obtained by performing two inverse kinematic calculations 1 With corner node S 2 The rotation angle of .

[0059] The horizontal movement of the end of the robot arm will cause the bottom of the grasped object to bump against the problem, so the middle point is set to solve it. The movement of extending and retracting the robot arm requires determining the coordinates of the starting and ending points of the end of the robot arm and the middle point, as well as determining the trajectory of each node, the angular velocity of the node, and the rotation angle.

[0060] like Figure 5-Figure 6 As shown, preferably, the slide module 4 includes a stepper motor 4-1, a displacement sensor 4-2, and a double-line rail threaded rod, and the double-line rail threaded rod is mainly composed of a threaded rod 4-3 and a double-line rail 4-4.

[0061] The slide is slidably connected to the double-line rail threaded rod, the robotic arm is installed on the slide, the stepper motor controls the double-line rail threaded rod to rotate, and the rotation of the double-line rail threaded rod drives the slide and the robotic arm to reciprocate on the double-line rail threaded rod.

[0062] The stepper motor 4-1 is used to control the planar positioning of the robot arm 1 platform.

[0063] The displacement sensor 4-2 is used to convert the wire length into a resistance signal output. The resistance signal of the displacement sensor is collected through the ADC channel of the STM32 main control chip and converted into position information.

[0064] The threaded rod 4 - 3 has a good self-locking phenomenon when rotating downward at a constant speed, and the robot arm 1 is more stable during the movement.

[0065] Double linear rail 4-4 is used for double linear rail to make up for the shortcoming of low load-bearing capacity of threaded rod, which is beneficial to the stable operation of the slide.

[0066] The robot arm platform 4-5 is used to install the robot arm 1. The robot arm 1 is combined with the slide module 4 to be used for moving and positioning the robot arm 1.

[0067] On the basis of the above embodiment, the execution control end is further used to send a control signal to the stepping motor to control the overall left and right movement of the entire robotic arm, specifically:

[0068] The angular velocity of the stepper motor is converted using a speed conversion formula. On the basis of the above embodiment, the execution control end is also used to send a control signal to the stepper motor to control the overall left and right movement of the entire robotic arm. Specifically, the speed of the slide is the moving speed of the slide, and the control signal is generated according to the moving speed of the slide. The speed conversion formula is:

[0069]

[0070] Among them, v is the moving speed of the slide, Ph is the lead of the double-line rail threaded rod, and ω is the relative angular velocity of the stepper motor.

[0071] Specifically, the transmission principle of the slide module 4 is as follows: Figure 6 As shown, the slide module 4 is mainly used for plane positioning of the robot platform, and the stepper motor drives the screw transmission to realize the movement of the slide, so that the robot 1 can operate stably.

[0072] In the above embodiment, the movement of the slide is achieved by driving the screw through a stepping motor;

[0073] The slide table uses a displacement sensor to achieve precise positioning and prevent the slide table from exceeding the stroke. The displacement sensor converts the wire length into a resistance signal output;

[0074] The execution control end includes an STM32 control chip, which collects the resistance signal of the displacement sensor through the ADC channel of the STM32 control chip and converts it into position information.

[0075] Preferably, in the execution control end, the grabbing operation on the target object is performed as follows:

[0076] A monocular distance measurement model is established according to the coordinate transformation, and the distance between the manipulator mounted on the manipulator arm and the target object is calculated by the monocular distance measurement model. The monocular distance measurement model is:

[0077]

[0078] Where d is the distance between the robot and the target object, F is the focal length of the camera, and H is c Indicates the camera installation height, y b Represents the ordinate of the midpoint of the bottom of the target object obtained from the image coordinate system, y h represents the horizon ordinate obtained from the image coordinate system, α represents the angle between the camera optical axis and the ground,

[0079] The manipulator is controlled to grasp the target object according to the distance between the manipulator and the target object.

[0080] Preferably, the execution control end is further configured to send a scanning instruction to the target recognition device when the target object is captured;

[0081] The target recognition device is used to scan the positioning placement area according to the scanning instruction, determine whether it is the target placement position according to the scanning result, and if so, send a placement instruction to the execution control end; if not, send a move to next point instruction to the execution control end;

[0082] The execution control end is also used to control the manipulator on the robotic arm to place the target object at the target placement position according to the placement instruction; and is also used to control the manipulator of the robotic arm to move to the next point according to the move to next point instruction.

[0083] Specifically, the target recognition device includes a camera, a servo cloud platform and a code scanning module. The servo cloud platform is installed at the end node of the robotic arm 1, and the camera and the code scanning module are installed on the servo cloud platform. The servo cloud platform is used to adjust the angle and position of the camera and the code scanning module. The camera and the code scanning module are mainly used to obtain image data and / or barcode data. The camera recognizes the barcode and obtains the position information of the anchor frame center from the camera center coordinate system. The position is adjusted to ensure that the code scanning module obtains the barcode information to determine the target placement position.

[0084] The device of the present invention realizes remote control of the robotic arm 1 through the combination of the main control end, the execution control end and the target recognition device, and realizes highly automated target recognition, grasping and placement at a relatively low cost, so that the invention can efficiently and accurately complete the target grasping and placement tasks, and can replace manual labor in different work scenarios, thereby improving production efficiency and reducing costs.

[0085] like Figure 8 As shown, an embodiment of the present invention further provides a robot arm control method, which is applied to the robot arm control system, and includes a general control end, an execution control end and a target recognition device, wherein the execution control end is installed on the robot arm, the general control end is connected to the execution control end, and the target recognition device is connected to the execution control end and the general control end respectively;

[0086] The general control end receives a control request sent by an operator, generates a first positioning instruction through the control request and sends it to the target recognition device;

[0087] The target recognition device images the target object according to the first positioning instruction to form a target object image, obtains target positioning information based on the target object image, and sends the target positioning information to the general control terminal to generate a second positioning instruction;

[0088] The execution control end performs real-time positioning of the end node of the robot arm according to the second positioning instruction, obtains the end positioning information, and sends it to the general control end;

[0089] The general control end determines whether the target positioning information and the terminal positioning information are consistent. If so, a grasping instruction is generated. If not, the turning angle node information of the manipulator is calculated based on the inverse kinematics algorithm, the target positioning information and the terminal positioning information, and a moving instruction is generated according to the turning angle node information.

[0090] The execution control end performs a grabbing operation on the target object according to the grabbing instruction; and is also used to control the robot arm to move to the target object according to the moving instruction, and when reaching the target object, performs a grabbing operation on the target object.

[0091] Preferably, imaging the target object to form a target object image, and obtaining target positioning information based on the target object image, specifically, as follows:

[0092] Projecting the grating onto the surface of the target object, and imaging the target object using an imaging element based on the grating to form an image of the target object;

[0093] The target object is converted from world coordinates to pixel coordinates based on the target object image and the coordinate conversion formula to obtain the target positioning information. The coordinate conversion formula is:

[0094]

[0095] Among them, X w ,Y w ,Z w is the point of the target object in the world coordinate system, and u, v are the points of the target object in the image coordinate system.

[0096] Preferably, the corner node information of the robotic arm is calculated based on the inverse kinematics algorithm, the target positioning information and the end positioning information, specifically:

[0097] A geometric relationship between the end node of the robotic arm and the corner node of the robotic arm is established according to the end positioning information and the working range of the robotic arm, specifically:

[0098] Assume the end node of the robot arm is S 3 , the corner node of the robot arm is S 1 and S 2 , S 1 Located at the front end of the robotic arm, S 2 Located in S 1 and S 3 Between; Assume that the working range of the robot arm is [l 1 -l 2 ,l 1 +l 2 ], where l 1 For S 1 With S 2 The distance between 2 For S 2 With S 3 The distance between 1 Establish a coordinate system for the origin, S 1 With S 2 The angle between the robot arm and the vertical axis is θ 1 , S 1 With S 2 The robot arm and S 2 With S 3The angle between the two arms is θ 2 , given the end positioning information of the end node of the robot arm is (L,H), where L is the horizontal coordinate and H is the vertical coordinate,

[0099] Establish the geometric relationship between the end node of the robotic arm and the corner node of the robotic arm:

[0100]

[0101] Among them, l 1 >l 2 , the working range of the robot arm is [l 1 -l 2 ,l 1 +l 2 ];

[0102] Assuming the target positioning information as the end point, the corner node information of the end node moving from the starting point to the middle point and the middle point moving to the end point is determined based on the inverse kinematics algorithm and the geometric relationship, specifically:

[0103] Based on the inverse kinematics algorithm and the geometric relationship, the rotation angle of each corner node of the end node moving from the starting point to the middle point is calculated,

[0104] The rotation angle of each corner node of the terminal node moving from the middle point to the end point is calculated based on the inverse kinematics algorithm and the geometric relationship. The calculation formula of the inverse kinematics algorithm is:

[0105]

[0106] Where L>0, S 2 With S 3 The angle between the robot arm and the horizontal axis is θ 3 ,θ 3 The angle of the actuator at the end node, and ensure that the actuator at the end node is in a horizontal state, θ 1 and θ 2 is the corner node S 1 With corner node S 2 The rotation angle of .

[0107] Below through Fig. 9 Describe the remote control process of the robot control system during the assembly process:

[0108] The remote control process can be divided into manual control mode and automatic control mode.

[0109] In manual control mode, the control signal is sent to the STM32 main control chip at the execution control end through the control panel. The STM32 main control chip controls the robotic arm and positions the slide to grab the target object. The placement area is located and detected by the target recognition device. The target placement position is determined by scanning and comparison, and the target object is placed at this position.

[0110] In the automatic control mode, the general control end sends a positioning command for locating the target position to the STM32 main control chip of the execution control end, and detects and locates the target through the camera. The general control end generates a movement command through the calculation process of the robot arm's corner node and sends it to the STM32 main control chip. The STM32 main control chip controls the robot arm to grasp. The STM32 main control chip controls the robot arm and positions the slide according to the corner node information to grasp the target object, locates the placement area through the target recognition device, detects the placement area position, determines the target placement position through scanning and comparison, and places the target object at this position.

[0111] Compared with the prior art, the present invention realizes remote control of the robotic arm through the combination of the main control end, the execution control end and the target recognition device, and achieves highly automated target recognition, grasping and placement at a relatively low cost, so that the invention can efficiently and accurately complete the target grasping and placement tasks, and can replace manual labor in different work scenarios, thereby improving production efficiency and reducing costs.

[0112] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robotic arm control system, characterized in that: It includes a general control terminal, an execution control terminal and a target identification device, wherein the general control terminal is connected to the execution control terminal, and the target identification device is connected to the execution control terminal and the general control terminal respectively; The general control terminal is used to receive a control request sent by an operator, generate a first positioning instruction through the control request and send it to the target recognition device; The target recognition device is used to image the target object according to the first positioning instruction to form a target object image, obtain target positioning information based on the target object image, and send it to the general control terminal to generate a second positioning instruction; The execution control end is used to perform real-time positioning of the end node of the robot arm according to the second positioning instruction, obtain the end positioning information, and send it to the general control end; The general control end is used to determine whether the target positioning information and the terminal positioning information are consistent, and if so, generate a grasping instruction; if not, calculate the corner node information of the robot arm based on the inverse kinematics algorithm, the target positioning information and the terminal positioning information, and generate a moving instruction according to the corner node information; The execution control end is used to perform a grabbing operation on the target object according to the grabbing instruction; and is also used to control the robot arm to move to the target object according to the moving instruction, and when reaching the target object, perform a grabbing operation on the target object.

2. The robot arm control system according to claim 1, characterized in that: In the target recognition device, the target object is imaged to form a target object image, and target positioning information is obtained based on the target object image, specifically: Projecting the grating onto the surface of the target object, and imaging the target object using an imaging element based on the grating to form an image of the target object; The target object is converted from world coordinates to pixel coordinates based on the target object image and the coordinate conversion formula to obtain the target positioning information. The coordinate conversion formula is: Among them, X w ,Y w ,Z w is the point of the target object in the world coordinate system, and u, v are the points of the target object in the image coordinate system.

3. The robot arm control system according to claim 1, characterized in that: In the general control end, the corner node information of the robotic arm is calculated based on the inverse kinematics algorithm, the target positioning information and the end positioning information, specifically: Establishing a geometric relationship between the end node of the robotic arm and the corner node of the robotic arm according to the end positioning information and the working range of the robotic arm, including: Suppose the end node of the robot arm is S3, the corner nodes of the robot arm are S1 and S2, S1 is located at the front end of the robot arm, and S2 is located between S1 and S3; suppose the working range of the robot arm is [l1-l2, l1+l2], where l1 is the distance between S1 and S2, l2 is the distance between S2 and S3, S1 is the origin of the coordinate system, the angle between the robot arm between S1 and S2 and the ordinate axis is θ1, the angle between the robot arm between S1 and S2 and the angle between the robot arm between S2 and S3 is θ2, and the end positioning information of the end node of the given robot arm is (L, H), where L is the horizontal coordinate and H is the vertical coordinate. Establish the geometric relationship between the end node of the robotic arm and the corner node of the robotic arm: Among them, l1>l2; Assuming the target positioning information as the end point, determining the corner node information of the end node moving from the starting point to the middle point and the middle point moving to the end point based on the inverse kinematics algorithm and the geometric relationship includes: Based on the inverse kinematics algorithm and the geometric relationship, the rotation angle of each corner node of the end node moving from the starting point to the middle point is calculated, The rotation angle of each corner node of the terminal node moving from the middle point to the end point is calculated based on the inverse kinematics algorithm and the geometric relationship. The calculation formula of the inverse kinematics algorithm is: Among them, L>0, the angle between the robot arm between S2 and S3 and the horizontal axis is θ3, θ3 is the angle of the actuator of the end node, and ensure that the actuator of the end node is in a horizontal state, θ1 and θ2 are the rotation angles of the corner node S1 and the corner node S2.

4. The robot arm control system according to claim 1, characterized in that: In the execution control end, the target object is grasped by: A monocular distance measurement model is established according to the coordinate transformation, and the distance between the manipulator mounted on the manipulator arm and the target object is calculated by the monocular distance measurement model. The monocular distance measurement model is: Where d is the distance between the robot and the target object, F is the focal length of the camera, and H is c Indicates the camera installation height, y b Represents the ordinate of the midpoint of the bottom of the target object obtained from the image coordinate system, y h represents the ordinate of the horizon obtained from the image coordinate system, α represents the angle between the camera optical axis and the ground, The manipulator is controlled to grasp the target object according to the distance between the manipulator and the target object.

5. The robot arm control system according to claim 1, characterized in that: It also includes a slide module, which includes a double-linear rail threaded rod, a slide and a stepper motor. The slide is slidably connected to the double-linear rail threaded rod, and the robotic arm is installed on the slide. The stepper motor controls the rotation of the double-linear rail threaded rod, and the rotation of the double-linear rail threaded rod drives the slide and the robotic arm to reciprocate on the double-linear rail threaded rod.

6. The robot arm control system according to claim 5, characterized in that: The execution control end is also used to send a control signal to the stepper motor, specifically: The angular velocity of the stepper motor is converted into the moving speed of the slide by using the speed conversion formula, and a control signal is generated according to the moving speed of the slide. The speed conversion formula is: Among them, v is the moving speed of the slide, Ph is the lead of the double-line rail threaded rod, and ω is the relative angular velocity of the stepper motor.

7. The robot arm control system according to claim 1, characterized in that: The execution control end is also used to send a scanning instruction to the target recognition device when the target object is captured; The target recognition device is used to scan the positioning placement area according to the scanning instruction, determine whether it is the target placement position according to the scanning result, and if so, send a placement instruction to the execution control end; if not, send a move to next point instruction to the execution control end; The execution control end is also used to control the manipulator on the robotic arm to place the target object at the target placement position according to the placement instruction; and is also used to control the manipulator of the robotic arm to move to the next point according to the move to next point instruction.

8. A robot arm control method, applied to the robot arm control system according to any one of claims 1 to 7, comprising a general control end, an execution control end and a target recognition device, wherein the general control end is connected to the execution control end, and the target recognition device is connected to the execution control end and the general control end respectively; characterized in that: The general control end receives a control request sent by an operator, generates a first positioning instruction through the control request and sends it to the target recognition device; The target recognition device images the target object according to the first positioning instruction to form a target object image, obtains target positioning information based on the target object image, and sends the target positioning information to the general control terminal to generate a second positioning instruction; The execution control end performs real-time positioning of the end node of the robot arm according to the second positioning instruction, obtains the end positioning information, and sends it to the general control end; The general control end determines whether the target positioning information and the terminal positioning information are consistent. If so, a grasping instruction is generated. If not, the turning angle node information of the manipulator is calculated based on the inverse kinematics algorithm, the target positioning information and the terminal positioning information, and a moving instruction is generated according to the turning angle node information. The execution control end performs a grabbing operation on the target object according to the grabbing instruction; and is also used to control the robot arm to move to the target object according to the moving instruction, and when reaching the target object, performs a grabbing operation on the target object.

9. The robot arm control method according to claim 8, characterized in that: Imaging the target object to form a target object image, and obtaining target positioning information based on the target object image, specifically: Projecting the grating onto the surface of the target object, and imaging the target object using an imaging element based on the grating to form an image of the target object; The target object is converted from world coordinates to pixel coordinates based on the target object image and the coordinate conversion formula to obtain the target positioning information. The coordinate conversion formula is: Among them, X w ,Y w ,Z w is the point of the target object in the world coordinate system, and u, v are the points of the target object in the image coordinate system.

10. The robot arm control method according to claim 8, characterized in that: The corner node information of the robotic arm is calculated based on the inverse kinematics algorithm, the target positioning information and the end positioning information, specifically: Establishing a geometric relationship between the end node of the robotic arm and the corner node of the robotic arm according to the end positioning information and the working range of the robotic arm, including: Suppose the end node of the robot arm is S3, the corner nodes of the robot arm are S1 and S2, S1 is located at the front end of the robot arm, and S2 is located between S1 and S3; suppose the working range of the robot arm is [l1-l2, l1+l2], where l1 is the distance between S1 and S2, l2 is the distance between S2 and S3, S1 is the origin of the coordinate system, the angle between the robot arm between S1 and S2 and the ordinate axis is θ1, the angle between the robot arm between S1 and S2 and the angle between the robot arm between S2 and S3 is θ2, and the end positioning information of the end node of the given robot arm is (L, H), where L is the horizontal coordinate and H is the vertical coordinate. Establish the geometric relationship between the end node of the robotic arm and the corner node of the robotic arm: Among them, l1>l2; Assuming the target positioning information as the end point, determining the corner node information of the end node moving from the starting point to the middle point and the middle point moving to the end point based on the inverse kinematics algorithm and the geometric relationship includes: Based on the inverse kinematics algorithm and the geometric relationship, the rotation angle of each corner node of the end node moving from the starting point to the middle point is calculated, The rotation angle of each corner node of the terminal node moving from the middle point to the end point is calculated based on the inverse kinematics algorithm and the geometric relationship. The calculation formula of the inverse kinematics algorithm is: Among them, L>0, the angle between the robot arm between S2 and S3 and the horizontal axis is θ3, θ3 is the angle of the actuator of the end node, and ensure that the actuator of the end node is in a horizontal state, θ1 and θ2 are the rotation angles of the corner node S1 and the corner node S2.

Images