Control System for U-Arm Robot Arm Movement with Linear Gripper Based on Inverse Kinematic Method

With the rapid advancement of technology, 6-DOF robotic arms, capable of simulating complex human arm movements and achieving precise positioning and manipulation in 3D space, have become crucial in modern industrial automation. This paper aims to validate the design and implementation of a six-degree-of-freedom (6-DOF) robotic arm controlled by a Raspberry Pi 4B. The robotic arm, composed of six servo systems, is capable of object grasping and manipulation in three-dimensional space. This paper presents forward and inverse kinematic models for the robotic arm's motion control and discusses their application in object grasping tasks. With technological advancements, robotic arms have become increasingly important in various fields such as manufacturing, medical, and construction. This study delves into the forward and inverse kinematics of the 6-DOF robotic arm through the development of new formulas, solutions, and control programs, aiming to achieve precise control over the arm's movements during object manipulation. By creating and testing custom formulas, solutions, and programs, this research seeks to implement and validate the theoretical aspects of the robotic arm's design and operation.

The object of research: Robot arm performance analysis 6 degrees freedom for automatic welding applications. Robot arm, built from 1010 aluminum, has six joints driven by motor stepper nema 23 and Nema 17 with various transmission systems to ensure proper movements. Controlled by the Arduino Mega 2560, this system integrates Stepper Motor Drivers, 24V Power Supplies, and rotary encoder for monitoring of real time connections. Investigated problem: Analyze the inverse kinematics, trajectory planning, and capability process of a robotic arm for automated welding applications. Inaccuracy and trajectory errors in the movement of robot arm when doing automatic welding tasks. The transition from manual welding to the automatic system faces the main challenges in achieving precise movement control, especially in maintaining the stability and accuracy of the welding path. Although various studies have developed advanced and reverse kinematics approaches to optimize the movement of robot arm, there is still a difference between the simulation and experimental results in terms of position and end-effector orientation. Errors in the joint angle estimation and trajectory execution can cause deviations in the welding path, which affect the quality of welding joints and production efficiency. The aim of this study was to analyze the inverse kinematics, trajectory planning, and capability process of a robotic arm for automated welding applications. The main scientific results: It showed a difference between reversed kinematics solutions and simulations, with joint angular errors 2, 3, and 5 ranging from 0.98º -2.7º. The movement of the robot arm when welding is quite smooth, but there is still an average trajectory error of 2.25 mm on the X axis and 10.57 mm on the Y axis. The area of practical use of the research results: The robotic welding systems by integrating inverse kinematics and trajectory planning algorithms with an Arduino 2560 microcontroller. Innovative technological product: The developed are robot arm control systems with 6 freedom degrees of Arduino 2560, equipped with a rotary encoder sensor to increase the accuracy of movement. This system allows more precise path control through more optimal planning, so as to produce a more consistent and high-quality welding connection. Scope of the innovative technological product: The automotive manufacturing industry, shipbuilding, and metal production that requires welding with a high precision level.

In recent years, the master-slave control system has been widely used in complex precision operations and high-risk locations such as nuclear environment, marine, medical, aerospace, etc. The control system designed in this research is based on the risk control principle, which isolates the hazard source from the operator so that the operator can operate accurately through remote control. The purpose is to ensure the accuracy of the operation and prevent the operator from being injured by the working environment. In this paper, the master robot system based on the Robot Operating System (ROS) is established under the Linux system. A humanoid robot arm driven by a 7 degree of freedom (7-DoF) tendon driven robot arm is used as a slave robot system. On this platform, an interactive interface with an electromagnetic position tracking sensor as an input device has been developed. Through the kinematics model of the robot arm, the mapping relationship between the human arm and the robot arm is established, and inverse kinematics is used to control the movement of the subordinate robot arm through the posture information of the human arm. The experimental results show that the end position of the robot arm controlled by the master-slave is roughly the same as the end position of the human arm, which proves the effectiveness of the control strategy.

Robotic arms have been used in various processes such as for moving goods, welding, assembling, and painting. In the case of welding and painting, it is necessary to move the end-effector robot accurately and smoothly to follow the specified trajectory. In robotic arm control, 2 things are important to be analyzed and implemented in controlling the motion of the robotic arm, namely inverse kinematic and trajectory planning. In this study, the inverse kinematic and trajectory planning algorithms are implemented to the robotic arm controller in the form of an Arduino Mega 2560 microcontroller. The inverse kinematic solution uses geometric and algebraic analytical methods. while the trajectory planning method is using LSPB (Linear Segment Parabolic Blend) Trajectory in Cartesian Space. Data retrieval is done by giving 2 input coordinates of the desired position and orientation, then the data in the form of the joint angle value will be measured using a rotary encoder as an angle sensor. Furthermore, the joint angle measurement value is converted in cartesian coordinates to get the end-effector position. Data analysis is done by comparing the data value of each joint angle with the calculated value so that the error value appears. The results showed that the inverse kinematic and trajectory planning algorithms were successfully applied to the 6-DOF robotic arm to perform straight-flat welding movements. Inverse kinematic testing on both input coordinates, the average error value for joints 2, 3, and 5 is 1.82º, 1.26º, and 2.08º. Meanwhile, the average error of the end-effector position at the x and z coordinates is 2.08 mm and 12.9 mm, respectively. Then for the trajectory planning test, the error value for the end-effector position in the x and z coordinates is 2.25 mm and 10.7 mm.

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

Reducing weight and inertias of conventional robot arms with an elastic structure allows safer interactive cooperation between humans and robots. While the end effector pose of a rigid robot is determined by the forward kinematic chain, the pose of elastic arms results from a superposition of the rigid kinematics and the pose dependent deflection caused by gravity. This property complicates the computation of forward and inverse kinematics in particular in case of dynamic loads. This paper presents a machine learning approach to extract various nonlinear regression models of the forward and inverse kinematics of a three degrees of freedom (DOF) flexible-link robot arm with dynamic loads from experimental data. The forward model predicts the target pose, given the joint angles and the strain signals while the inverse kinematic model predicts the joint angles required to assume a target pose. The transformation of the original features onto suitable nonlinear features substantially improves the generalisation ability of the both forward and inverse kinematic model. The closed loop inverse kinematic controller archieves a pose accuracy of 3 mm and the results show that the learned model can solve the inverse kinematics problem of flexible robot arms with sufficient accuracy even with unknown payloads.

This research delves into the automated functionality of robotic arm manipulators, a hallmark of Industry 4.0, within the manufacturing sector. The study focuses on precise movement adhering to predetermined trajectories, addressing the vital aspects of inverse kinematics and trajectory planning in robotic arm control. Utilizing the Matlab robotic toolbox, the research conducts simulations of inverse kinematic and trajectory planning. An experimental setup involving a robotic arm controlled by an Arduino Mega 2560 microcontroller, embedded with the inverse kinematic algorithm and trajectory planning, is executed. Data acquisition involves inputting coordinates and orientation for automatic welding along a straight path. Joint angles are measured using rotary encoders and converted into Cartesian coordinates to determine the end-effector's position. Discrepancy analysis compares measured joint angles with simulation values, revealing error margins. Movement quality of the robotic arm is assessed through Capability Processes (CP) evaluation. Results indicate disparities between experimental and simulated values. At input coordinates (400mm, 0mm, 300mm), joint angle errors of 2.51º, 0.98º, and 1.48º are observed for joints 2, 3, and 5, respectively. Similarly, at input coordinates (300mm, 0mm, 300mm), joint angle errors of 1.17º, 1.5º, and 2.7º are registered for the same joints. Trajectory error analysis during straight welding reveals average errors of 2.25 mm and 10.57 mm along the x and y axes. Mean absolute errors for joints 2, 3, and 5 are 1.9º, 0.48º, and 1.91º.
 Keywords: Robotic Arm Manipulators, Inverse Kinematics, Trajectory Error Analysis

The presence of robots that can assist humans with heavy or dangerous work makes the need for robots more pressing at the moment. One type of robot needed is a robot arm, which is widely used in the manufacturing industry, such as in the assembly process and pick and place. The types of robotic arms used vary both in terms of configuration and the number of degrees of freedom. However, with different types of robotic arms, different models of movement are used. Therefore, research related to the modeling of the robotic arm continues to be carried out to obtain the appropriate movement of the robotic arm. One of the methods used as a first step in designing a robotic arm movement model is kinematics analysis. Kinematics analysis aims to analyze the movement of the robot arm without knowing what force causes the movement. This paper aims to produce an ideal movement model for the AX-12A 3-DoF robotic arm using forward kinematic and inverse kinematic analysis using two methods, the Denavit-Haterberg method and the geometric approach method. The difference from other papers is that this paper makes the kinematics model using Robotic, Vision, and Control (RVC) tools based on the Peter I. Corke model on MATLAB software first before implementing it on hardware. The results show that the error percentage for the forward kinematic model is 1.04% and the inverse kinematic is 0.76%, which means the two models achieved the target that the model’s error maximum must be less than 2%.

Robot arm is often used in industry with various tasks, one of which is a pick and place. Rapid prototyping of robot arms is needed to facilitate the development of many industrial tasks, especially on a laboratory scale. This study aims to design a small-scale three degree of freedom (3-DoF) robot arm for pick and place mission using the inverse kinematics method. The mechanical robotic arm system is designed using Solidworks with four servo motors as the actuator. Arduino Mega 2560 is used as a microcontroller in which the inverse kinematic method is embedded. This method is used to move the robot based on the coordinates of the destination pick and place. The test results show that the robot arm can carry out the pick and place mission according to the target coordinates given with the largest average error of about 5 cm. While the error generated between the calculation and computation results is around 3%.

Robot arm is often used in industry with various tasks, one of which is a pick and place. Rapid prototyping of robot arms is needed to facilitate the development of many industrial tasks, especially on a laboratory scale. This study aims to design a small-scale three degree of freedom (3-DoF) robot arm for pick and place mission using the inverse kinematics method. The mechanical robotic arm system is designed using Solidworks with four servo motors as the actuator. Arduino Mega 2560 is used as a microcontroller in which the inverse kinematic method is embedded. This method is used to move the robot based on the coordinates of the destination pick and place. The test results show that the robot arm can carry out the pick and place mission according to the target coordinates given with the largest average error of about 5 cm. While the error generated between the calculation and computation results is around 3%.

In order to improve efficiency and automation of spraying, a automatic spray lance, which was regard as an robotic arm, was studied. It could reach the desired position automatically. A study was carried out using Denavit and Hartenberg (D-H) approach to automate the four Degrees of Freedom (DOF) robotic arm. The kinematics of robotic arm was constructed us D-H coordinate frame model. And the inverse kinematics has been solved with inverse transform technique. Some motion parameters could be acquired. Simulation of the robotic arm was studied using the Robotics Toolbox which is MATLAB software. Simulations’ content included homogeneous, construction of robot object, kinematics and trajectory. The results of simulation indicated that the movement of robotic arm could satisfy the demands of four DOF. The robotic was founded able to move to the desired position by simulating. The links’ motion parameters were reasonable and it provided a new spray lance for automatic sprayer.

The Unmanned Ground Vehicle (UGV) Robot Arm is needed to take samples of volcanic material, namely material samples in the form of rocks from the Volcano which will later be studied more accurately. The focus of this research is on developing a robotic arm to take samples of volcanic material mounted on the UGV. The Robot Arm consists of 3-DOF (Degree Of Freedom) equipped with a Gripper to make it easier to take samples of the material from the Top of the Volcano. The Robot Arm is placed on the upper front of UGV Robot body. The movement of the Robot Arm utilizes Rotational Motion in each DOF. The Kinematic Model of this 3-DOF robotic arm was built using the Denavit Hartenberg method. This study discusses the movement of the robot arm with 3 degrees of freedom where the robot used has one kinematic strand consisting of 3 joints with the revolute type. The angle at each joint can be determined using a method used in this research, namely forward kinematics and inverse kinematics. Experimental activity shows that the error occurs because the mechanical system is less precise rather than the method used

Robotic systems are known for their precision and accuracy. To achieve a desired set of operating parameters the system must have a good control system. Robotic arms can vary in size shape and purpose of use. With increasing DOF (Degree of Freedom) the complexity of the system increases and hence a DAQ system is required to check various parameters of the process. This paper discusses the implementation of kinematic and inverse kinematic modelling of a 6 Degree of Freedom (DOF) hydraulic arm in MATLAB environment. The forward kinematic model is based on Denavit-Hartenberg parameters. To obtain a given set of position and orientation of end effector, inverse kinematics gives the required set of joint angles. Potentiometers mounted on arm joint are used as the prime feedback elements for extracting the joint angles. All six joints of the arm are revolute joints. The values of potentiometer obtained are compared to the desired input values. Proportional direction control valve is the main controlling element of the hydraulic circuit. The forward and inverse kinematics is validated and implemented in MATLAB robotics toolbox (developed by Peter Croke). A GUI is also used to ease the process of retrieving and feeding the information in the system. The results of forward and inverse kinematics from MATLAB are verified by the physical model discussed in this paper.

The robot arm is a device consisting of a moving chain of links connected by joints. Electrical motors are frequently used to move each robot arm joint. An end-effector that can move freely in space is usually attached to one end of the robot platform, which is fixed. Robot arms can do repetitive operations at rates and precision far exceeding human operators. Nowadays, robot arm systems are widely used worldwide to increase the quality and efficiency of the manufacturing process in the industry. Typical applications of the robot arm system are assembly, painting, welding, pick and place operation, and others. Besides, many industries employ robot arms for various jobs such as selecting and putting, painting, and material handling. However, one of the most challenging issues in completing these jobs is determining the target location of the robot arm's end-effector. There are two different methods for analyzing the robot arm's movement: forward and inverse kinematic analysis. Based on the visual servo algorithm, this study uses inverse kinematics to execute the pick and place operation. First, an object recognition algorithm is implemented to identify the object to be grasped. Then, an algorithm to avoid any obstacles is done. The study's findings show that good system performance has been obtained in all three algorithms: first, object recognition algorithm, second, obstacle avoidance algorithm, and lastly, visual servobased pick and place operation. Thus, it can be concluded that the robot arm's visual servo algorithm is suitable for pick-and-place applications.

This paper combines visual recognition and robotic arm, optimizes the design of robotic arm structure, and researches the logistics robotic arm with six degrees of freedom and visual perception ability to enrich the logistics robotic use scene. Firstly, the visual recognition system is installed on the existing six-degree-of-freedom robotic arm, and the coordinate system is established by the light bar recognition method combined with the D-H parameter method. Secondly, a kinematic simulation model was established by using MATLAB, and the state of the joints was analyzed by using forward and inverse kinematic simulation and fifth degree polynomial interpolation. Finally, the actual logistics fixed-point transportation task is designed for trajectory planning. The results show that the robotic arm is able to be able to perform different grasping tasks in non-structural environments, around the visual analysis of the robotic arm as well as the optimization of the motion track, to enhance the efficiency of the comprehensive use of visual and tactile information, to improve the adaptability of the robotic hand grasping, and to be able to meet the precise control of the pick-up process and the use of the realization of the payload grasping and dexterous movement. The research in this paper provides a certain reference value for the propulsion robot arm to perform intelligent grasping tasks.