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JetCobot📖
1.Introduction to the Jetcobot
  • 0.Unboxing
  • 1 Version Configuration Instructions
  • 2. Power adapter usage specifications
  • 3. Common Problems and Solutions
  • 4. First-time use instructions
  • 5. Get started
  • 6. Open and close the virtual desktop
2.Assembly and assembly tutorial
  • Installation Video
3.Linux operating system
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
4.Jupyter Lab Basic Course
  • 1. Read the document before operation
  • 2. JupyterLab environment construction tutorial
  • 3. JupyterLab Basic Usage Tutorial
  • 4. JupyterWidgets Configuration Tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
5.Basic courses of main control board
  • 1. Introduction to the main control board
  • 2. Burn the system image
  • 3. Re-read the burned image
  • 4. System startup
  • 5. System and desktop introduction
  • 6. System expansion
  • 7. Network Configuration
  • 8. SSH remote login and file transfer
  • 9. VNC Remote Login
  • 10. System backup
  • 11. Increased swap space
  • 12. Use of Jtop
6.GPIO hardware control course
  • 1.GPIO library API usage
  • 2. Hardware library configuration
  • 3. Pin reading function
  • 4. Pin level output control
  • 5. Control LED
  • 6. External device serial communication
  • 7.I2C communication
7.Jetson-inference vision course
  • 1. Install Jetcam (optional)
  • 2. Install TensorFlow (optional)
  • 3. Jetson-inference environment construction (optional)
  • 4. Hello AI World
  • 5. Image Classification Reasoning
  • 6. Object Detection Reasoning
  • 7. Semantic Segmentation
8.Opencv basic course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Picture cut
  • 8. Picture pan
  • 9. Picture mirroring
  • 10. Affine transformation
  • 11. Picture rotation
  • 12. Perspective transformation
  • 13. Grayscale processing
  • 14. Binary image
  • 15. Edge green detection
  • 16. Line segment drawing
  • 17. Rectangular circle drawing
  • 18. Text and picture drawing
9.ROS Basic Course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
10.ROS+OpenCV course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.AR QR code
  • 6.ROS+Opencv basics
  • 7.Face recognition
  • 8.harris corner detection
  • 9.Target tracking algorithm
  • 10.Contour moment
  • 11.Polygon outline
  • 12.Discrete fourier transform algorithm
  • 13.Edge detection algorithm
  • 14.Face detection algorithm
  • 15.Optical flow detection algorithm
  • 16.Contour detection
  • 17.General contour detection
  • 18.Feature point tracking
  • 19.HLS color filtering
  • 20.Hough circle detection
  • 21.Hough linear detection
  • 22.HSV color filtering
  • 23.LK optical flow algorithm
  • 24.Detection algorithm
  • 25.Phase dependent displacement detection
  • 26.Image pyramid sampling algorithm
  • 27.RGB color filtering
  • 28.Clear background detection algorithm
  • 29.Simplified optical flow algorithm
  • 30.Simple filter
  • 31.Threshold image processing
  • 32.Watershed segmentation algorithm
11.Robotic arm control course
  • 1.Send data
  • 2.Read data
  • 3.Control robotic arm joint angle
  • 4.Control coordinates of robotic arm end
  • 5.Control robotic arm posture
  • 6.Handle control robotic arm
  • 7.Calibrate robotic arm joint
  • 8.Calibrate robotic arm gripper
12.AI visual basic ourse
  • 1.Camera driver
  • 2.Block color calibration
  • 3.Color recognition
  • 4.Face detection
  • 5.Gesture recognition
  • 6.Model training
  • 7.Garbage recognition
13.Mediapipe course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Face detection
  • 5.Face recognition
  • 6.Face effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Gesture recognition
14.AI visual tracking course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Fingertip trajectory recognition
  • 7.Mediapipe gesture control robotic arm action group
  • 8.Mediapipe arm posture control robotic arm
  • 9.Mediapipe palm arm target positioning
  • 10.Mediapipe palm recognition and tracking control robotic arm
  • 11.Apriltag tag code recognition
  • 12.Apriltag tag code tracking
  • 13.KCF object tracking
15.AI visual grabbing course
  • 1.Gesture recognition stacking blocks
  • 2.Color recognition grab blocks
  • 3.Apriltag recognition grabbing blocks
  • 4.Catch game
  • 5.Color sorting
  • 6.Color stacking
  • 7.apriltag sorting
  • 8.apriltag stacking
  • 9.Garbage sorting
  • 10.Garbage stacking
  • 11.Color interaction
16.URDF Model course
  • 1.URDF model of the robotic arm
  • 2.URDF model controls the real robotic arm
17.Kinesiology advanced course
  • 1.Movelt configuration
  • 2.MoveIt control the real machine
  • 3.Moves randomly
  • 4.Movelt kinematic design
  • 5.Movelt cartesian path
  • 6.Movelt collision detection
  • 7.Movelt scene design
18.Jetson Orin SUPER -Introduction to the Jetcobotnew
  • 1. Version Configuration Instructionsnew
  • 2. Power adapter usage specificationsnew
  • 3. Common Problems and Solutionsnew
  • 4. First-time use instructionsnew
  • 5. Get startednew
  • 6. Open and close the virtual desktopnew
19.Jetson Orin SUPER -Assembly and assembly tutorial
  • Installation Videonew
20.Jetson Orin SUPER -Linux operating systemnew
  • 1.Introduction to Linux systemnew
  • 2.Ubuntu file systemnew
  • 3.Ubuntu common commandsnew
  • 4.Ubuntu common editorsnew
  • 5.Ubuntu software operation commandsnew
  • 6.Virtual machine installationnew
  • 7.SSH remote controlnew
  • 8.VNC remote controlnew
  • 9.Transfer files remotelynew
  • 10.Driver library and communicationnew
  • 11.Static IP and hotspot modenew
  • 12.Bind device IDnew
  • 13.Capacity expansion and resource allocationnew
  • 14.Update system software sourcesnew
  • 15.Set root user passwordnew
  • 16.sudo free passwordnew
  • 17.Connect to WiFi networknew
  • 18.View system versionnew
  • 19.Customized service managementnew
  • 20.Back up system imagenew
21.Jetson Orin SUPER -Jupyter Lab Basic Course
  • 1. Read the document before operationnew
  • 2. JupyterLab environment construction tutorialnew
  • 3. JupyterLab Basic Usage Tutorialnew
  • 4. JupyterWidgets Configuration Tutorialnew
  • 5. Getting Started Tutorial for JupyterWidgets Controlsnew
22.Jetson Orin SUPER -Basic motherboard usage course
  • 1. Motherboard Introductionnew
  • 2. Restore the factory image systemnew
  • 3. System startupnew
  • 4. System and desktop introductionnew
  • 5.VNC Remote Controlnew
  • 6. System expansionnew
  • 7. Network Configurationnew
  • 8.SSH remote loginnew
  • 9. Remote file transfernew
  • 10. Chinese input methodnew
  • 11. Use of Jtop toolnew
  • 12. Swap space expansionnew
  • 13.Backup SSD systemnew
  • 14.Jetpack old system upgraded to SUPER system
23.Jetson Orin SUPER -GPIO hardware control course
  • 1.GPIO library API usagenew
  • 2.Hardware library configurationnew
  • 3.Pin reading functionnew
  • 4.Pin level output controlnew
  • 5.Control LEDnew
  • 6.External device serial communicationnew
  • 7.I2C communicationnew
24.Jetson Orin SUPER -YOLO11 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detection
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
25.Jetson Orin SUPER -OpenCV course
  • 1. Introduction to Open Source CVnew
  • 2. Image reading and displaynew
  • 3. Image writingnew
  • 4. Image qualitynew
  • 5. Pixel Operationnew
  • 6. Image zoomnew
  • 7. Picture cutnew
  • 8. Picture pannew
  • 9. Picture mirroringnew
  • 10. Affine transformationnew
  • 11. Picture rotationnew
  • 12. Perspective transformationnew
  • 13. Grayscale processingnew
  • 14. Binary imagenew
  • 15. Edge green detectionnew
  • 16. Line segment drawingnew
  • 17. Rectangular circle drawingnew
  • 18. Text and picture drawingnew
26.Jetson Orin SUPER -ROS2 basic course
  • 1. Introduction to ROS2new
  • 2.ROS2 install Humblenew
  • 3.ROS2 development environmentnew
  • 4.ROS2 workspacenew
  • 5.ROS2 function packagenew
  • 6.ROS2 nodenew
  • 7.ROS2 topic communicationnew
  • 8.ROS2 service communicationnew
  • 9.ROS2 action communicationnew
  • 10.ROS2 custom interface messagenew
  • 11.ROS2 parameter service casenew
  • 12.ROS2 meta-function packagenew
  • 13.ROS2 distributed communicationnew
  • 14.ROS2 DDSnew
  • 15.ROS2 time related APInew
  • 16.ROS2 common command toolsnew
  • 17.ROS2 rviz2 usenew
  • 18.ROS2 rqt toolboxnew
  • 19.ROS2 Launch startup file configurationnew
  • 20.ROS2 recording and playback toolnew
  • 21.ROS2 URDF modelnew
  • 22.ROS2 Gazebo simulation platformnew
  • 23.ROS2 TF2 coordinate transformationnew
27.Jetson Orin SUPER -Advanced vision course
  • 1.QR codenew
  • 2.Human pose estimationnew
  • 3.Target Detectionnew
  • 4.AR visionnew
  • 5.ROS+Opencv Basicsnew
28.Jetson Orin SUPER -Robotic Arm Control Course
  • 1. Sending Datanew
  • 2. Read datanew
  • 3. Control the joint angle of the robotic armnew
  • 4. Control the coordinates of the end of the robot armnew
  • 5. Control the posture of the robotic armnew
  • 6.Handle remote control robotic armnew
  • 7. Calibrate the robot arm jointsnew
  • 8. Calibrate the robot grippernew
29.Jetson Orin SUPER -AI Vision Basic Course
  • 1. Camera drivernew
  • 2. Color threshold adjustment and color block calibrationnew
  • 3. Color recognitionnew
  • 4. Face Detectionnew
  • 5. Gesture Recognitionnew
  • 6. Garbage Identificationnew
30.Jetson Orin SUPER -Mediapipe Development Course
  • 1. Hand detectionnew
  • 2. Posture detectionnew
  • 3. Overall detectionnew
  • 4.FaceMeshnew
  • 5. Face Detectionnew
  • 6. Face effectsnew
  • 7. 3D Object Recognitionnew
  • 8. Brushnew
  • 9. Finger controlnew
  • 10. Gesture Recognitionnew
31.Jetson Orin SUPER -AI visual tracking course
  • 1.PID algorithm basicsnew
  • 2. Color block positioning experimentnew
  • 3. Color block tracking experimentnew
  • 4. Face location experimentnew
  • 5. Face tracking experimentnew
  • 6. Fingertip trajectory recognitionnew
  • 7.Mediapipe gesture control robotic arm action groupnew
  • 8.Mediapipe arm posture control robotic armnew
  • 9. Mediapipe palm arm target positioningnew
  • 10. Mediapipe identifies and tracks your palm to control the robotic armnew
  • 11. Apriltag label code recognitionnew
  • 12. Apriltag label code trackingnew
  • 13.KCF Object Trackingnew
32.Jetson Orin SUPER -AI Visual Capture Course
  • 1. Gesture recognition stacking blocksnew
  • 2. Color recognition grab blocksnew
  • 3. Apriltag recognition and grabbing building blocksnew
  • 4.Catch gamenew
  • 5. Color sortingnew
  • 6. Color stackingnew
  • 7. Apriltag sortingnew
  • 8. Apriltag stackingnew
  • 9. Garbage sortingnew
  • 10. Garbage dumpnew
  • 11.Color interactionnew
33.Jetson Orin SUPER -Robotic Arm URDF Model Course
  • 1. Robotic arm URDF modelnew
  • 2. URDF model controls the real robot armnew
34.Jetson Orin SUPER -Moveit advanced kinematics course
  • 1.Movelt Configurationnew
  • 2. Use of MoveItnew
  • 3. MoveIt drives the real machinenew
  • 4. MoveIt Random Movenew
  • 5.Movelt positive kinematics designnew
  • 6.Movelt inverse kinematics designnew
  • 7.Movelt Cartesian pathnew
  • 8.Movelt scene designnew
  • 9.Movelt Collision Detectionnew
35.FAQnew
  • 1.Using your own Jetson board, JetCobot cannot start normally
Download
  • Instruction_Manual
  • SystemFile_for_NANO4GB
  • SystemFile_For_OrinNANO
  • SystemFile_For_OrinNX
  • VM_File
  • Code
  • 3D Model File
  • Jetson_Boot_USB.tar.gz
  • ROS2_Basic_VM
Download ZIP

Welcome to JetCobot repository

1.PID algorithm basics

1.PID algorithm basics

1.1 Introduction

PID is to perform proportional integral differential operation on the input deviation, and the superposition result of the operation is used to control the actuator. The formula is as follows:

It consists of three parts:

  • P is the proportion, which is the input deviation multiplied by a coefficient;
  • I is the integral, which is the integral operation of the input deviation;
  • D is differentiation, which performs differential operations on the input deviation.

As shown below is a basic PID controller:

(1) Proportional part

The mathematical expression of the proportional part is: Kp*e(t)

In an analog PID controller, the role of the proportional link is to react instantaneously to deviations. Once the deviation occurs, the controller immediately takes control to change the control quantity in the direction of reducing the deviation. The strength of the control effect depends on the proportional coefficient,The larger the proportional coefficient, the stronger the control effect, the faster the transition process, and the smaller the static deviation of the control process;But the larger it is, the easier it is to produce oscillations and destroy the stability of the system. Therefore, the sample coefficient must be selected appropriately to achieve a short transition time, small static difference and stable effect.

Advantages: Adjust the open-loop proportional coefficient of the system, improve the steady-state accuracy of the system, reduce the inertia of the system, and speed up the response speed.

Disadvantages: Using only P controller, an excessively large open-loop proportional coefficient will not only increase the overshoot of the system, but also make the system stability margin smaller and even unstable.

(2) Points part

The mathematical expression of the integral part is:

The larger the integral constant, the weaker the accumulation effect of the integral, and the system will not oscillate during the transition; However, increasing the integral constant will slow down the static error elimination process, and the time required to eliminate the deviation is also longer, but it can reduce overshoot and improve the stability of the system. When Ti is small, the integral effect is strong. At this time, oscillation may occur in the system transition time, but the time required to eliminate the deviation is shorter. Therefore, Ti must be determined according to the specific requirements of actual control.

Advantages: Eliminate steady-state errors.

Disadvantages: The addition of the integral controller will affect the stability of the system and reduce the stability margin of the system.

(3) Differential part

The mathematical expression of the differential part is:

The function of the differential link is to prevent the change of deviation. It is controlled based on the change trend (change speed) of the deviation. The faster the deviation changes, the larger the output of the differential controller is and the correction can be made before the deviation becomes larger. The introduction of differential action will help reduce overshoot, overcome oscillation, and stabilize the system. It is especially beneficial to high-order systems because it speeds up the tracking speed of the system. However, the effect of differential is very sensitive to the noise of the input signal. For systems with larger noise, differential is generally not used, or the input signal is filtered before differential takes effect. The role of the differential part is determined by the differential time constant Td. The larger the Td, the stronger its effect in suppressing deviation changes; the smaller the Td, the weaker its effect in resisting deviation changes. The differential part obviously plays a big role in the stability of the system. Appropriate selection of the differential constant Td can optimize the differential effect.

Advantages: It makes the response speed of the system faster, reduces overshoot, alleviates oscillation, and has a "predictive" effect on the dynamic process.

1.2 Selection of PID algorithm

Digital PID control algorithms can be divided into positional PID and incremental PID control algorithms. So before we decide which PID algorithm to use, we should first understand its principles:

  • Positional PID algorithm:

e(k): User-set value (target value) - the current status value of the control object

Proportion P : e(k)

Integral I : ∑e(i) Accumulation of errors

Differential D : e(k) - e(k-1) this time error - last time error

That is, positional PID is the actual position of the current system, and the deviation from the expected position you want to achieve is used for PID control.

Because there is an error integral ∑e(i), which is always accumulated, that is, the current output u(k) is related to all past states, and the accumulated value of the error is used;(The error e will be accumulated), the output u(k) corresponds to the actual position of the actuator,Once the control output goes wrong (there is a problem with the current state value of the control object), a large change in u(k) will cause a large change in the system. When the positional PID reaches saturation in the integral term, the error will still continue to accumulate under the action of the integral.Once the error starts to change in the opposite direction, the system needs a certain amount of time to exit the saturation zone, so when u(k) reaches the maximum and minimum, the integral function must be stopped,And there must be integral limiting and output limiting, so when using positional PID, generally we use PD control directly and positional PID is suitable for objects whose actuators do not have integral components. Such as steering gear and balance car upright and temperature control system control

Advantages: Positional PID is a non-recursive algorithm that can directly control the actuator (such as a balancing car).The value of u(k) has a one-to-one correspondence with the actual position of the actuator (such as the current angle of the car), so it can be used well in objects where the actuator does not have an integral component.

Disadvantages: Each output is related to the past state. When calculating, e(k) needs to be accumulated, which requires a large amount of calculation work.

  • Incremental PID:

Proportion P: e(k)-e(k-1) error this time - error last time

Integral I: e(k) error

Differential D: e(k)-2e(k-1)+e(k-2)this time error-2*last error+last time error

Incremental PID can be well seen from the formula. Once KP, TI and TD are determined, as long as the deviation of the three measured values before and after is used,That is, the control amount $Delta u(k)$ obtained by calculating the control increment from the formula corresponds to the increment of the position error in recent times,Instead of corresponding to the deviation from the actual position, there is no error accumulation. That is to say, there is no need for accumulation in incremental PID.The determination of the control increment $Delta u(k)$ is only related to the latest three sampling values. It is easy to obtain better control effects through weighted processing, and when a problem occurs in the system, the incremental method will not seriously affect the work of the system.

Summary: Incremental PID is an increment to positional PID.At this time, the controller outputs the difference between the position values calculated at two adjacent sampling moments. The result is an increment, that is, the control amount needs to be increased (negative value means decrease) based on the last control amount.

Advantages: ① The impact of malfunction is small. If necessary, the erroneous data can be removed by logical judgment.

②The impact is small when switching between manual and automatic, making it easy to achieve disturbance-free switching. When the computer fails, the original value can still be maintained.

③ No accumulation is required in the calculation formula. The determination of the control increment Δu(k) is only related to the latest three sampling values.

Disadvantages: ① The integral truncation effect is large and there is a steady-state error;

②The impact of overflow is large. It is not good to use incremental method for some controlled objects;

  • Difference between incremental and positional

(1) The incremental algorithm does not require accumulation. The determination of the increment of the control volume is only related to the recent deviation sampling values. The calculation error has a small impact on the calculation of the control volume. The positional algorithm uses the accumulated value of past deviations, which is prone to large accumulated errors.

(2) The incremental algorithm obtains the increment of the control amount. For example, in valve control, only the change in valve opening is output, and the impact of malfunction is small. If necessary, logical judgment can be used to limit or prohibit this time. The output will not seriously affect the work of the system. The positional output directly corresponds to the output of the object, so it has a greater impact on the system.

(3) The incremental PID control output is an increment of the control amount and has no integral effect. Therefore, this method is suitable for objects with integral components in the actuator, such as stepper motors, etc., while the positional PID is suitable for objects with no integral components in the actuator. Objects with integral components, such as electro-hydraulic servo valves.

(4) When performing PID control, positional PID requires integral limiting and output limiting, while incremental PID only needs to output limiting. Positional PID and incremental PID are just two implementations of digital PID control algorithms. Just the form, the essence is exactly the same. The main difference is that the integral items are stored in different ways. The positional PID integral items are stored separately, while the incremental PID integral items are stored as part of the output. Various players on the Internet also have their own unique insights and opinions on the use of positional and incremental PIDs. The specific opinion depends on which algorithm is suitable for our specific application scenarios.

1.3 Debugging PID parameters

There are many methods for selecting parameters of PID controller, such as trial and error method, critical proportional method, extended critical proportional method, etc. However, for PID control, parameter selection is always a very complicated task, and continuous adjustments are required to obtain a more satisfactory control effect. Based on experience, the general steps for determining PID parameters are as follows:

  • Determine proportional coefficient Kp

When determining the proportional coefficient Kp, first remove the integral term and differential term of the PID, you can set Ti=0, Td=0, making it

Pure proportional adjustment. The input is set to 60% to 70% of the maximum allowable output of the system. The proportional coefficient Kp gradually increases from 0 until the system oscillates; conversely, the proportional coefficient Kp gradually decreases from this time until the system oscillation disappears. Record the proportional coefficient Kp at this time, and set the PID proportional coefficient Kp to 60% to 70% of the current value.

  • Determine the integration time constant Ti

    After the proportional coefficient Kp is determined, set a larger integration time constant Ti, then gradually reduce Ti until the system oscillates, and then in turn, gradually increase Ti until the system oscillation disappears. Record the Ti at this time, and set the PID integration time constant Ti to 150% to 180% of the current value.

  • Determine the differential time constant Td

    The differential time constant Td generally does not need to be set, it can be 0. At this time, PID adjustment is converted into PI adjustment. If it needs to be set, it is the same as the method for determining Kp, taking 30% of its value when there is no oscillation.

Of course, this is just my personal debugging method, and may not be suitable for everyone and every environment. It is only provided for your reference; however, there are also classic trial tips for debugging PID circulating on the Internet, and I also posted them for your reference:

Find the best parameter setting, and check in order from small to large.

First the proportion, then the integral, and finally the differential.

The curve oscillates frequently, and the proportional dial needs to be enlarged.

The curve floats around the big bend, and the proportion dial is turned smaller.

The curve deviates and recovers slowly, and the integration time decreases.

The curve fluctuation period is long, and the integration time will be longer.

The oscillation frequency of the curve is fast, so first reduce the differential.

If the dynamic difference is large, the fluctuation will be slow, and the differential time should be lengthened.

The ideal curve has two waves, the front is high and the back is low four to one.

One look, two adjustments and multiple analyses, the adjustment quality will not be low.

PID is a proportional (P), integral (I), and differential (D) control algorithm. It is not necessary to have these three algorithms at the same time. It can also be PD, PI, or even only P algorithm control. One of the simplest ideas I had for closed-loop control was P control, which feeds back the current result and then subtracts it from the target. If it is positive, it will slow down, and if it is negative, it will speed up. Of course, this is just the simplest closed-loop control algorithm. , that is, let’s go back to the summary of positional and incremental in the previous section. For details, we must refer to our current control environment. Because of the differences in each control system, the parameters that can allow our system to achieve the most stable effect are certainly OK. .

Gesture recognition
Color block positioning

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