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DOFBOT Pro📖
1. Get started quickly
  • 0.Unboxing introduction video
  • 1. First Trial
  • 2. Analysis of common problems
  • 3. Calibration offset
  • 4. Jetpack old system upgraded to SUPER system
  • 5. Precautions for DIY Robotic Arm
  • 6. Jetson Nano Version – Must Read
  • 7.Robotic arm gripping anomaly solutions
2. Assembly course
  • 1. Installation video
  • 2. Calibrate the Robotic Arm
  • 3. Install the map
3. Remote control course
  • 1. Mobile APP control
  • 2. USB handle control
  • 3. PC web page programming
4. Preparation
  • 1. Manage APP control services
  • 2. Manage JupyterLab services
  • 3. Manage OLED services
  • 4. Modifying ROS_DOMAIN_ID for Multi-Machine
5. 2D visual tracking course
  • 1. Color block positioning
  • 2. Color block tracking
  • 3. Face positioning
  • 4. Face tracking
  • 5. Label code positioning
  • 6. Label code tracking
6. 2D visual grabbing course
  • 1. Identify grab color block
  • 2. Catch game
  • 3. Color sorting
  • 4. Color stacking
  • 5. Label code sorting
  • 6. Label code stacking
  • 7. Identify gesture grab color block
  • 8. Gesture stacking
  • 9.1 Single garbage sorting(Jetson-Nano)
  • 9.2 Single garbage sorting(Orin)
  • 10.1 Garbage sorting(Jetson-Nano)
  • 10.2 Garbage sorting(Orin)
  • 11. Tracking game
7. 2D Mediapipe course
  • 1. Hand detection
  • 2. Posture detection
  • 3. Overall detection
  • 4. Facial Detection
  • 5. Face Detection
  • 6. Face effects
  • 7. 3D Object Recognition
  • 8. Paintbrush
  • 9. Finger control
  • 10. Gesture Recognition
  • 11. Gesture control robotic arm action group
  • 12. Arm posture control robotic arm
  • 13. Palm Targeting
  • 14.1 Robotic arm tracks the palm(Jetson-Nano)
  • 14.2 Robotic arm tracks the palm(Orin)
  • 15. Fingertip trajectory recognition
  • 16. Fingertip trajectory control robotic arm
  • 17. Gesture Grasp and Release Objects
  • 18. Finger Control Robot Arm
8. AI Large Model Voice Module (Must Read)new
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
9. 2D Voice Control Course
  • 1.Simple Voice Control
  • 2.Smart Voice Control
  • 3.Voice Action Control
  • 4.Memory Learning Mode
  • 5.Color Recognition with Voice Broadcast
  • 6.Color Recognition and Grasping with Voice Broadcast
  • 7.Color Recognition and Sorting with Voice Broadcast
  • 8.Voice Recognition Target Tracking
  • 9.Voice Control Stacking
  • 10.Voice Control Color Sorting
  • 11.1 Garbage Recognition with Voice Broadcast(Jetson Nano)
  • 11.2 Garbage Recognition with Voice Broadcast(Orin)
  • 12.1 Garbage Sorting with Voice Broadcast(Jetson Nano)
  • 12.2 Garbage Sorting with Voice Broadcast(Orin)
10. 3D Depth Camera Course
  • 1. Dabai_DCW2 camera
  • 2. Deep Pseudo-Color Image
  • 3. Depth camera ranging
  • 4. Color block volume measurement
11. 3D Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
12. 3D Sorting and Grasping Course
  • 1. AprilTag ID sorting
  • 2. AprilTag abnormal height sorting
  • 3. MediaPipe Gesture-AprilTag ID sorting
  • 4. MediaPipe Gesture-AprilTag height sorting
  • 5. MediaPipe Gesture-AprilTag distance sorting
  • 6. Block color sorting
  • 7. Block abnormal height sorting
  • 8. Block different shape sorting
  • 9. Block shape abnormal height sorting
  • 10. Yolov11 garbage sorting(Orin)
13. 3D Tracking and Grasping Course
  • 1. AprilTag Tracking and Grasping
  • 2. Color Block Tracking and Grasping
  • 3. KCF Object Tracking and Grasping
14. 3D Voice Control for Grasping Course (Sorting & Tracking)
  • 1. Voice Control AprilTag ID Sorting
  • 2. Voice Control Remove Height Abnormal AprilTag
  • 3. Voice Control Tracking and Grasping AprilTag
  • 4. Voice Control Color Block Sorting
  • 5. Voice Control Remove Height Abnormal Color Block
  • 6. Voice Control Tracking and Grasping Color Block
  • 7. Voice Control Yolov11 Garbage Recognition Sorting
  • 8. Voice Control Object Tracking and Grasping
15. AI Model Basicsnew
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
16. AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
17. AI Model – Text Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
18. AI Model – Voice Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
19. AI Model – Fun Applicationsnew
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
20. MoveIt Course
  • 1. MoveIt Configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
21. YOLOv11 Development Course (ORIN)
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Dataset Annotation
  • 10. Model Training_Conversion
  • 11. Model Prediction
22. YOLOv11 Development Course (Jetson Nano)
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Model Prediction
23. 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
24. Basic Control Course
  • 1. Control RGB Light
  • 2. Control Buzzer
  • 3. Control Single Servo
  • 4. Read Servo Angle
  • 5. Control All Servos
  • 6. DOFBOT Up Down Left Right Movement
  • 7.DOFBOT dancing
  • 8. DOFBOT Memory Actions
  • 9. DOFBOT Grab Blocks
  • 10. Porter
  • 11. Stack Blocks
  • 12. Two DOFBOT Synchronized Actions
25. Jetson Board Basic Course
  • 1. Jetson board Introduction
  • 2. Write system image
  • 3. Re-write system 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
26. GPIO Hardware Control Course
Jetson Nano
  • 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
Jetson Orin Nano_NX
  • 1.GPIO description
  • 2.GPIO library installation
  • 3.GPIO reading
  • 4.GPIO output
  • 5.Serial communication
  • 6.I2C communication
27. OpenCV 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
28. Visual Basics Course
  • 1. Camera display
  • 2. Color Calibration
  • 3. Color recognition
  • 4. Face Detection
  • 5.1 Garbage Identification(Jetson-Nano)
  • 5.2 Garbage Identification(Orin)
  • 6.1 Object Recognition(Jetson-Nano)
  • 6.2 Object Recognition(Orin)
  • 7. Label code recognition
  • 8.PID algorithm basics
29. ROS2 Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 Topic Communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
30. Visual Advanced Course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.ROS+Opencv basics
  • 6.AR QR code
  • 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
31. Jupyter Lab Usage Course
  • 1. Read the document before operation
  • 2. Jupyter lab environment construction tutorial
  • 3. Basic tutorial of Jupyter lab
  • 4. Jupyter widgets configuration tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
Download
  • APP(Android/iOS)
  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_NANO4GB_SUB
  • VM_File
  • Code
  • Instruction_Manual
  • Hardware_Info
  • 3D_Model_File
  • ROS2_Basic_VM
  • Tools
Download ZIP

Welcome to DOFBOT Pro repository

2. Inverse solution

Inverse Solution

Before starting this function, you need to close the large program and APP processes. If you need to restart the large program and APP later, start them from the terminal:

bash ~/dofbot_pro/APP_DOFBOT_PRO/start_app.sh

1. Function Description

After the node program starts, it will provide a /dofbot_kinemarics service. This service provides both FK and IK services. You can choose to call FK or IK by specifying kin.name. The fk service content is to input the current angle values of 6 servos, that is, joint values, and will output the current pose of the robotic arm end, where the pose includes xyz coordinates and Euler angles (roll, pitch, yaw); the ik service content is to input the pose of the robotic arm end, that is, xyz coordinates and Euler angle values, and will output the angle values of 6 servos, that is, joints. In subsequent operations when we grasp objects in 3D space, we need to first read the current pose of the robotic arm end, then identify the object's center to get a new pose coordinate through coordinate system conversion, and then call IK to calculate the value of each servo.

2. Startup and Operation

2.1. Startup

Enter in the terminal:

xxxxxxxxxx
#Start inverse kinematics program
ros2 run dofbot_pro_info kinemarics_dofbot
#Start ROS control robotic arm program
ros2 run dofbot_pro_driver arm_driver

After the inverse kinematics node program starts, it will provide a service. Use the following command to check. Enter in the terminal:

xxxxxxxxxx
ros2 node info /kinemarics_dofbot

In the listed service list, there is a /dofbot_kinemarics, which is the inverse kinematics service. Use the following command to view the relevant information of this service:

xxxxxxxxxx
ros2 service type /dofbot_kinemarics

image-20250415145640939

As shown in the figure above, it describes that the service type data of /dofbot_kinemarics is the custom dofbot_pro_interface/srv/Kinemarics. The specific parameters are the Args content below. We can use the following command to see what content is included in the dofbot_pro_interface/srv/Kinemarics data type:

xxxxxxxxxx
ros2 interface show dofbot_pro_interface/srv/Kinemarics

image-20250415145924996

Here the --- divides it into two parts. The upper part is the request (can be understood as input parameters), and the lower part is the response (can be understood as output return values). The request part has an additional kin_name, which is used to distinguish whether we are calling IK or FK. If we want to call the IK service, assign IK to kin_name; conversely, assign the value of FK to kin_name to call the FK service. Request part parameters:

  • tar_x: x value of target position, unit is m
  • tar_y: y value of target position, unit is m
  • tar_z: z value of target position, unit is m
  • Roll: Roll value of target pose, unit is radians
  • Pitch: Pitch value of target pose, unit is radians
  • Yaw: Yaw value of target pose, unit is radians

The above 6 are parameters that must be assigned when calling IK.

  • cur_joint1: Current angle value of servo 1, unit is degrees
  • cur_joint2: Current angle value of servo 2, unit is degrees
  • cur_joint3: Current angle value of servo 3, unit is degrees
  • cur_joint4: Current angle value of servo 4, unit is degrees
  • cur_joint5: Current angle value of servo 5, unit is degrees
  • cur_joint6: Current angle value of servo 6, unit is degrees

The above 6 are parameters that must be assigned when calling FK.

  • kin_name: Name of the service to call, ik or fk

Response part parameters:

  • joint1: Angle value of servo 1, unit is degrees
  • joint2: Angle value of servo 2, unit is degrees
  • joint3: Angle value of servo 3, unit is degrees
  • joint4: Angle value of servo 4, unit is degrees
  • joint5: Angle value of servo 5, unit is degrees
  • joint6: Angle value of servo 6, unit is degrees

The above 6 values are the angle values of 6 servos that will be returned after calling the IK service.

  • x: Calculated x value of end position, unit is m
  • y: Calculated y value of end position, unit is m
  • z: Calculated z value of end position, unit is m
  • Roll: Calculated Roll value of end position, unit is radians
  • Pitch: Calculated Pitch value of end position, unit is radians
  • Yaw: Calculated Yaw value of end position, unit is radians

The above six parameters are the end position and pose that will be returned after calling the FK service.

2.2. Operation

2.2.1. FK

First, let's make the robotic arm move to a certain state, and then read out the end position and pose based on this state, and compare it with the actual measured values to see if it's accurate. From the previous lesson "ROS Control Robotic Arm" content, we can use topics to first publish a robotic arm pose, such as publishing a pose like [90,120,30,30,90,90]. Enter in the terminal:

xxxxxxxxxx
ros2 topic pub /TargetAngle dofbot_pro_interface/ArmJoint "id: 0
run_time: 1000
angle: 0.0
joints: [90,120,30,30,90,90]"

Press Enter to publish the message, and the robotic arm moves to the pose we specified. Then, we call the FK service, write the previously set [90,120,30,30,90,90] into the current values of the six servos mentioned above, that is, cur_joint1 to cur_joint6, and assign fk to kin_name. Enter in the terminal:

xxxxxxxxxx
ros2 service call /dofbot_kinemarics dofbot_pro_interface/srv/Kinemarics "{tar_x: 0.0, tar_y: 0.0, tar_z: 0.0, roll: 0.0, pitch: 0.0, yaw: 0.0, cur_joint1: 90.0,
  cur_joint2: 120.0, cur_joint3: 30.0, cur_joint4: 30.0, cur_joint5: 90.0, cur_joint6: 90.0,
  kin_name: 'fk'}"

Press Enter, and the response will be printed in the terminal as shown below:

image-20250415150748895

From the printed response, we can see that the xyz representing the robotic arm end position and the Euler angles Roll, Pitch, Yaw representing the robotic arm end pose are respectively: x=-0.0006, y=0.1684148999974, z=0.268150411789, Roll=1.21669278515e-08, Pitch=-0.0, Yaw=0.0. From these data, we can know that the robotic arm end is 0.0006 meters to the right of the base coordinate system, 0.168414899974 meters forward, and 0.26810411789 meters high. The pose can be considered unchanged because the Euler angles are all equal to zero or infinitely close to 0. We can use a ruler to roughly measure the distance from the robotic arm end to the base coordinate system to see if it matches the calculated xyz values.

2.2.2. IK

First, we send the following message to make the robotic arm extend upward. Enter in the terminal:

xxxxxxxxxx
ros2 topic pub /TargetAngle dofbot_pro_interface/ArmJoint "id: 0
run_time: 1000
angle: 0.0
joints: [90,90,90,90,90,90]"

Then, we call the IK service, given the position and pose parameters of the robotic arm end, to calculate what the value of each servo of the robotic arm should be to reach this position and pose. We take the xyz and Euler angles calculated by the previous FK as the position and pose to reach. Enter in the terminal:

xxxxxxxxxx
ros2 service call /dofbot_kinemarics dofbot_pro_interface/srv/Kinemarics "{tar_x: -0.0006, tar_y: 0.1684148999974, tar_z: 0.268150411789, roll: 1.21669278515e-08, pitch: -0.0, yaw: 0.0, cur_joint1: 00.0,
  cur_joint2: 0.0, cur_joint3: 0.0, cur_joint4: 0.0, cur_joint5: 0.0, cur_joint6: 0.0,
  kin_name: 'ik'}"

Press Enter, and the response will be printed in the terminal as shown below:

image-20250415151006088

From the printed response, we can see that the calculated returned joint1-joint6 correspond to the values of six servos. After rounding each joint, it is [90,120,30,30,90,0]. We try to publish the calculated joint1-joint6 through topics to the robotic arm control node to make it move to the pose we specified. Enter in the terminal:

xxxxxxxxxx
ros2 topic pub /TargetAngle dofbot_pro_interface/ArmJoint "id: 0
run_time: 1000
angle: 0.0
joints: [90,120,30,30,90,0]"

Press Enter to make the robotic arm move to the pose we specified. Then, we use a ruler to measure whether the distance from the robotic arm end to the base coordinate system corresponds to the xyz we input. An error within 1cm is acceptable. This is because each servo of the robotic arm cannot guarantee complete consistency when leaving the factory, and there are small differences.

3. Program Flowchart

image-20240924110534308

4. Core Code Analysis

Code path:

xxxxxxxxxx
/home/jetson/dofbot_pro_ws/src/dofbot_pro_info/src/kinemarics_dofbotpro.cpp

Import relevant header files

​x
#include <iostream>
#include <vector>
#include "rclcpp/rclcpp.hpp"
#include <kdl/chain.hpp>
#include <kdl/tree.hpp>
#include <kdl/segment.hpp>
#include <kdl/frames_io.hpp>
#include <kdl/chainiksolverpos_lma.hpp>
#include <kdl/chainfksolverpos_recursive.hpp>
#include <kdl_parser/kdl_parser.hpp>
#include "dofbot_pro_interface/srv/kinemarics.hpp"
#include "dofbot_pro_info/kinemarics_dofbotpro.h"
​

Define some constants

xxxxxxxxxx
// Radians to degrees
const float RA2DE = 180.0f / M_PI;
// Degrees to radians
const float DE2RA = M_PI / 180.0f;
//URDF model, used as parameter input for inverse kinematics calculation
const char *urdf_file = "/home/jetson/dofbot_pro_ws/src/dofbot_pro_info/urdf/DOFBOT_Pro-V24.urdf";

Main function

xxxxxxxxxx
auto server_node = rclcpp::Node::make_shared("kinemarics_dofbot"); // Create service
auto service = server_node->create_service<dofbot_pro_interface::srv::Kinemarics>(
        "dofbot_kinemarics", srvicecallback);

Service callback function srvicecallback

FK service part

xxxxxxxxxx
if (request->kin_name == "fk") {
        double joints[]{request->cur_joint1, request->cur_joint2, request->cur_joint3, request->cur_joint4,
                        request->cur_joint5};
        // Define target joint angle container
        vector<double> initjoints;
        // Define pose container
        vector<double> initpos;
        // Target joint angle unit conversion, from degrees to radians
        for (int i = 0; i < 5; ++i) initjoints.push_back((joints[i] - 90) * DE2RA);
        // Call forward kinematics function to get current pose
        dofbot_pro.dofbot_getFK(urdf_file, initjoints, initpos);
        RCLCPP_INFO(rclcpp::get_logger("rclcpp"), "fk sent***");
        response->x = initpos.at(0);
        response->y = initpos.at(1);
        response->z = initpos.at(2);
        response->roll = initpos.at(3);
        response->pitch = initpos.at(4);
        response->yaw = initpos.at(5);
    }

IK service part

xxxxxxxxxx
//Call ik service according to kin_name
if (request->kin_name == "ik") {
​
       // Gripper length
        //double tool_param = 0.12;
        // Grasping pose
        double Roll = request->roll ;
        double Pitch = request->pitch;
        double Yaw = request->yaw ;
        double x=request->tar_x;
        double y=request->tar_y;
        double z=request->tar_z;
        // End position (unit: m)
        double xyz[]{x, y, z};
        cout << x << y << z << endl;
        // End pose (unit: radians)
        //double rpy[]{Roll * DE2RA, Pitch * DE2RA, Yaw * DE2RA};
        double rpy[]{Roll , Pitch, Yaw };
        // Create output angle container
        vector<double> outjoints;
        // Create end position container
        vector<double> targetXYZ;
        // Create end pose container
        vector<double> targetRPY;
        for (int k = 0; k < 3; ++k) targetXYZ.push_back(xyz[k]);
        for (int l = 0; l < 3; ++l) targetRPY.push_back(rpy[l]);
        // Inverse kinematics to calculate joint angles to reach target point
        dofbot_pro.dofbot_getIK(urdf_file, targetXYZ, targetRPY, outjoints);
        // Print inverse kinematics results
        for (int i = 0; i < 5; i++) cout << (outjoints.at(i) * RA2DE) + 90 << ",";
        cout << endl;
        a++;
        response->joint1 = (outjoints.at(0) * RA2DE) + 90;
        response->joint2 = (outjoints.at(1) * RA2DE) + 90;
        response->joint3 = (outjoints.at(2) * RA2DE) + 90;
        response->joint4 = (outjoints.at(3) * RA2DE) + 90;
        response->joint5 = (outjoints.at(4) * RA2DE) + 90;
​
        // redis.set("joint1", std::to_string(response->joint1));
        cout<<"-----------------"<<endl;
       
       
       
    }

 

Robotic arm ROS control
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