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DOFBOT-Ultra📖
1. Get Started Quickly
  • 0.Unboxing Video
  • 1. Get Started Quickly
  • 2. Access the Terminal
  • 3. Calibration offset(Depth Camera)
  • 4. Calibration offset(USB Camera)
2. Assembly Course
  • 1. Installation Steps
  • 2. Calibrate the Robotic Arm
  • 3. Install the Map
3. Remote Control Course
  • 1. Mobile Phone Control
  • 2. USB Handle Control
  • 3. PC Web Page Programming
4. Preparation
  • 1. Manage Web control services
  • 2. Manage JupyterLab services
  • 3. Manage OLED services
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 Grasping Course
  • 1. Identify grab color block
  • 2. Catch game
  • 3. Color sorting
  • 4. Color stacking
  • 5. Label code sorting
  • 6. Label code stacking
  • 7. Gesture grasping color blocks
  • 8. Gesture stacking color blocks
  • 9. Single garbage sorting
  • 10. Garbage sorting
  • 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. Robotic arm tracks the palm
  • 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)
  • 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 Sorting
  • 11. Garbage Recognition with Voice Broadcast
  • 12. Garbage Sorting with Voice Broadcast
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. Yolov26 garbage sorting
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 Yolov26 Garbage Recognition Sorting
  • 8. Voice Control Object Tracking and Grasping
15. AI Model Basics
  • 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 Development
  • 0. DofbotUltra Large Model Development Quick Start Guide
  • 1. Register a Model Service Provider Account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. AI Large Model Development - chatbot
  • 6. Deploy the RAG knowledge base
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
17. AI Model – Text Version
  • 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 + YOLOv26 + Intelligent Garbage Classification
18. AI Model – Voice Version
  • 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 + YOLOv26 + Intelligent Garbage Classification
19. AI Model – Fun Applications
  • 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
PI 5
  • 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 and Conversion
  • 11. Model Prediction
RDK X5
  • 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 Prediction
  • 9. Model Conversion
22. YOLOv26 Development Course
PI 5
  • 6. Image Classification
  • 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 and Conversion
  • 11. Model Prediction
RDK X5
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Dataset Annotation
  • 10. Model Training and Conversion
  • 11. 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.Capacity expansion and resource allocation
  • 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.RDKx5 Board Basic Course
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Use 7-inch display
  • 6.SD card expansion
  • 7.Network configuration
  • 8.Remote login
  • 9.Update system
  • 10.srpi-config user guide
  • 11.config. txt configuration file
  • 12.Self-startup setting
  • 13.RDK exclusive command usage
  • 14.Linux command usage
  • 15.GPIO readwrite operation
  • 16.Image classification algorithm
  • 17.Use USB camera
  • 18.Use MIPI camera
  • 19.Binocular camera depth algorithm
26.PI5 Board Basic Course
  • 1.Introduction to Raspberry PI 5
  • 2.Raspberry PI system installation and backup
  • 3.Powering the Raspberry PI 5
  • 4.Startup of Raspberry PI 5
  • 5.Installation and introduction of shell and active radiator
  • 6.GPIO and 40-pin pins
  • 7.Tips for using Raspberry PI 5
  • 8.Raspberry PI PCIe connector
  • 9.MIPI CSI DSI connector
  • 10.UART connector
  • 11.Real-time clock
  • 12.Power over Ethernet (PoE) connector
  • 13.Update and upgrade operating system
  • 14.Introduction to raspi-config tool
  • 15.config.txt file description
  • 16.Network Configuration
  • 17.remote access
  • 18.Transfer files remotely
  • 19.Set display resolution and rotation
  • 20.Set screen to sleep
  • 21.Get real-time temperature of Raspberry Pi
  • 22.Play audio and video
  • 23.Using USB camera
  • 24.Using MIPI camera
27. Visual Basics 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 Detection
  • 16. Line Segment Drawing
  • 17. Rectangular Circle Drawing
  • 18. Text and Picture Drawing
28. 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
29. 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
  • System_File
  • VM_File
  • Instruction Manual
  • Code
  • Software Tools
  • Communication_Protocols
  • Hardware_Info
Download ZIP

Welcome to DOFBOT-Ultra repository

2. Inverse solution

Inverse Kinematics

Please ensure that the web control program backend process is not running in the background. To close it, refer to the [Get Started Quickly - 2. Access the Terminal] section in [Disable the Auto-Start Web Control Backend Process]. After closing, please restart DofbotUltra!!!

Before starting this function, you need to close the web control program backend process.

1. Function Description

After the node program starts, it provides a /dofbot_kinematics service that offers both fk (forward kinematics) and ik (inverse kinematics) services. You can choose which service to call by specifying kin_name. The fk service takes the current angle values of the 6 servos (joint values) as input and outputs the current end-effector pose of the robotic arm. The pose includes xyz coordinates and Euler angles (roll, pitch, yaw). The ik service takes the end-effector pose (xyz coordinates and Euler angles) as input and outputs the 6 servo angle values (joint values). In subsequent 3D space object grasping operations, we need to first read the current end-effector pose, then identify the object's center and calculate a new pose coordinate through coordinate transformation, and finally call ik to compute each servo value.

2. Startup and Operation

2.1. Startup

Enter in the terminal:

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# Start the inverse kinematics program
ros2 run dofbot_ultra_info kinematics_dofbotultra
# Start the ROS control program for the robotic arm
ros2 run dofbot_ultra_driver arm_driver

After the inverse kinematics node program starts, it provides a service. Use the following command to check:

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ros2 node info /kinematics_dofbotultra

In the service list, there is /dofbot_kinematics, which is the inverse kinematics service. Use the following command to check its related information:

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ros2 service type /dofbot_kinematics
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#### Output Information #######
root@raspberrypi:/home/yahboom/dofbot_ultra# ros2 node info /kinematics_dofbotultra
/kinematics_dofbotultra
  Subscribers:
    /parameter_events: rcl_interfaces/msg/ParameterEvent
  Publishers:
    /parameter_events: rcl_interfaces/msg/ParameterEvent
    /rosout: rcl_interfaces/msg/Log
  Service Servers:
    /dofbot_kinematics: dofbot_ultra_interface/srv/Kinematics
    /kinematics_dofbotultra/describe_parameters: rcl_interfaces/srv/DescribeParameters
    /kinematics_dofbotultra/get_parameter_types: rcl_interfaces/srv/GetParameterTypes
    /kinematics_dofbotultra/get_parameters: rcl_interfaces/srv/GetParameters
    /kinematics_dofbotultra/list_parameters: rcl_interfaces/srv/ListParameters
    /kinematics_dofbotultra/set_parameters: rcl_interfaces/srv/SetParameters
    /kinematics_dofbotultra/set_parameters_atomically: rcl_interfaces/srv/SetParametersAtomically
  Service Clients:

  Action Servers:

  Action Clients:

root@raspberrypi:/home/yahboom/dofbot_ultra# ros2 service type /dofbot_kinematics
dofbot_ultra_interface/srv/Kinematics
root@raspberrypi:/home/yahboom/dofbot_ultra#

The /dofbot_kinematics service type is the custom dofbot_ultra_interface/srv/Kinematics. The specific parameters are in the Args section below. We can use the following command to view what content is included in the dofbot_ultra_interface/srv/Kinematics data type:

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ros2 interface show dofbot_ultra_interface/srv/Kinematics

image-20250415145924996

The --- divides the content into two parts: the upper part is the request (input parameters), and the lower part is the response (output return values). The request part has an additional kin_name field, which distinguishes whether we are calling IK or FK. If you want to call the IK service, assign IK to kin_name. Conversely, assign FK to kin_name to call the FK service.

Request parameters:

  • tar_x: The x value of the target position, unit is m

  • tar_y: The y value of the target position, unit is m

  • tar_z: The z value of the target position, unit is m

  • Roll: The Roll value of the target pose, unit is radians

  • Pitch: The Pitch value of the target pose, unit is radians

  • Yaw: The Yaw value of the target pose, unit is radians

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

  • kin_name: The name of the service to call, either ik or fk

Response 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 servo angle values returned after calling the IK service.

  • x: The calculated x value of the end-effector position, unit is m

  • y: The calculated y value of the end-effector position, unit is m

  • z: The calculated z value of the end-effector position, unit is m

  • Roll: The calculated Roll value of the end-effector position, unit is radians

  • Pitch: The calculated Pitch value of the end-effector position, unit is radians

  • Yaw: The calculated Yaw value of the end-effector position, unit is radians

The above 6 parameters are the end-effector position and pose returned after calling the FK service.

2.2. Operation

2.2.1. FK (Forward Kinematics)

First, let's move the robotic arm to a certain state. Then, based on this state, we read the end-effector position and pose and compare them with actual measurements to check accuracy. From the previous lesson "ROS Control Robotic Arm," we can use a topic to publish a robotic arm pose. For example, publish a pose of [90, 120, 30, 30, 90, 90]. Enter in the terminal:

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ros2 topic pub --once /TargetAngle dofbot_ultra_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 specified pose. Then, we call the FK service and input the just-set [90, 120, 30, 30, 90, 90] as the six servo current values, i.e., cur_joint1 through cur_joint6. Also, set kin_name to fk. Enter in the terminal:

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ros2 service call /dofbot_kinematics dofbot_ultra_interface/srv/Kinematics "
{
  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 end-effector position xyz and the Euler angles (Roll, Pitch, Yaw) representing the end-effector pose are: x=-0.0006, y=0.1684148999974, z=0.268150411789, Roll=1.21669278515e-08, Pitch=-0.0, Yaw=0.0. From these data, we can see that the end-effector is 0.0006 meters to the right of the base coordinate system, 0.168414899974m in front, and 0.26810411789m high. The pose can be considered unchanged, as all Euler angles are equal to zero or very close to zero. We can use a ruler to roughly measure the end-effector position relative to the base coordinate system and check if it matches the calculated xyz values.

2.2.2. IK (Inverse Kinematics)

First, let's send a message to make the robotic arm extend upward. Enter in the terminal:

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ros2 topic pub /TargetAngle dofbot_ultra_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 end-effector position and pose parameters, we calculate what each servo value of the robotic arm should be to achieve that position and pose. Taking the xyz and Euler angles from the previous FK calculation as the target position and pose, enter in the terminal:

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ros2 service call /dofbot_kinematics dofbot_ultra_interface/srv/Kinematics "
{
  tar_x: -0.0006,
  tar_y: 0.1684148999974,
  tar_z: 0.268150411789,
  roll: 1.21669278515e-08,
  pitch: 0.0,
  yaw: 0.0,
  cur_joint1: 0.0,
  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, the calculated joint1-joint6 correspond to the 6 servo values. After rounding each joint, they are [90, 120, 30, 30, 90, 0]. We try to publish the calculated joint1-joint6 to the robotic arm control node via topic to move it to the specified pose. Enter in the terminal:

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

Press Enter to move the robotic arm to the specified pose. Then, we use a ruler to measure whether the distance from the end-effector to the base coordinate system matches the input xyz values. An error within 1cm is acceptable. This is because each servo of the robotic arm cannot be guaranteed to be completely identical at the factory, and there are slight differences.

3. Program Flowchart

image-20240924110534308

4. Core Code Analysis

Code paths:

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Raspberry Pi: /home/yahboom/dofbot_ultra_ws/src/dofbot_ultra_info/src/kinematics_dofbotultra.cpp
RDK: /home/sunrise/dofbot_ultra_ws/src/core/dofbot_ultra_info/src/kinematics_dofbotultra.cpp

Import related header files:

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#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_ultra_interface/srv/Kinematics.hpp"
#include "dofbot_ultra_info/Kinematics_dofbotultra.h"

Define some constants:

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// Radian to degree
const float RA2DE = 180.0f / M_PI;
// Degree to radian
const float DE2RA = M_PI / 180.0f;
// URDF model. During inverse kinematics calculation, the URDF model is used as an input parameter.
// The 3D camera version description model filename in the current repository is:
const char *urdf_file = "/home/yahboom/dofbot_ultra_ws/src/core/dofbot_ultra_description/urdf/DOFBOT-Pro-3DCamera-URDF-V2.urdf";
//RDK:const char *urdf_file = "/home/sunrise/dofbot_ultra_ws/src/core/dofbot_ultra_description/urdf/DOFBOT-Pro-3DCamera-URDF-V2.urdf";

Main function:

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auto server_node = rclcpp::Node::make_shared("Kinematics_dofbot"); // Create service
auto service = server_node->create_service<dofbot_ultra_interface::srv::Kinematics>(
        "dofbot_kinematics", 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_ultra.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:

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// According to kin_name, the IK service is called
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-effector position (unit: m)
        double xyz[]{x, y, z};
        cout << x << y << z << endl;
        // End-effector orientation (unit: radians)
        //double rpy[]{Roll * DE2RA, Pitch * DE2RA, Yaw * DE2RA};
        double rpy[]{Roll , Pitch, Yaw };
        // Create output angle container
        vector<double> outjoints;
        // Create end-effector position container
        vector<double> targetXYZ;
        // Create end-effector orientation 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 for reaching the target point
        dofbot_ultra.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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