Inverse Kinematics Modelling and Simulation for Upper Case Writing Robot Control Using ANFIS

2016 ◽  
Vol 836 ◽  
pp. 37-41 ◽  
Author(s):  
Adlina Taufik Syamlan ◽  
Bambang Pramujati ◽  
Hendro Nurhadi
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Inverse Kinematics ◽  
Robot Control ◽  
Industrial Revolution ◽  
Training Data ◽  
Robotic Arm ◽  
End Effector ◽  
Precision And Accuracy ◽  
Case Writing

Robotics has lots of use in the industrial world and has lots of development since the industrial revolution, due to its qualities of high precision and accuracy. This paper is designed to display the qualities in a form of a writing robot. The aim of this study is to construct the system based on data gathered and to develop the control system based on the model. There are four aspects studied for this project, namely image processing, character recognition, image properties extraction and inverse kinematics. This paper served as discussion in modelling the robotic arm used for writing robot and generating theta for end effector position. Training data are generated through meshgrid, which is the fed through anfis.

Vision-Based Trajectory Generation for a Two-Link Robotic Arm Using Quadtree Decomposition and Curve Smoothing

2010 ◽  
Vol 108-111 ◽  
pp. 1439-1445
Author(s):  
Shahed Shojaeipour ◽  
Sallehuddin Mohamed Haris ◽  
Ehsan Eftekhari ◽  
Ali Shojaeipour ◽  
Ronak Daghigh
Keyword(s):  
Image Processing ◽  
A Priori ◽  
Trajectory Generation ◽  
Robotic Arm ◽  
Generation System ◽  
End Effector ◽  
Quadtree Decomposition ◽  
Practical Applications ◽  
Robot Trajectory ◽  
Curve Smoothing

In this article, the development of an autonomous robot trajectory generation system based on a single eye-in-hand webcam, where the workspace map is not known a priori, is described. The system makes use of image processing methods to identify locations of obstacles within the workspace and the Quadtree Decomposition algorithm to generate collision free paths. The shortest path is then automatically chosen as the path to be traversed by the robot end-effector. The method was implemented using MATLAB running on a PC and tested on a two-link SCARA robotic arm. The tests were successful and indicate that the method could be feasibly implemented on many practical applications.

scholarly journals Design Of 4DOF 3D Robotic Arm to Separate the Objects Using a Camera

2021 ◽  
Vol 3 (1) ◽  
pp. 27-35
Author(s):  
Akhmad Fahruzi ◽  
Bimo Satyo Agomo ◽  
Yulianto Agung Prabowo
Keyword(s):  
Inverse Kinematics ◽  
Robotic Arm ◽  
Robot Arm ◽  
Fixed Position ◽  
End Effector ◽  
Robotic Arms ◽  
Work Area ◽  
Intended Object ◽  
Place Of Origin ◽  
Prototype Design

Nowadays robotic arm is widely used in various industries, especially those engaged in manufacturing. Robotic arms are usually used to perform jobs such as picking up and moving goods from their place of origin to the location desired by the operator. In this study, a 3d 4 DOF (Degree of Freedom) robotic arm. The prototype was made to move goods with random coordinates to places or boxes whose coordinates were determined in advance. The robot can know the coordinates of the object to be taken or moved. The arm robot prototype design is completed with a camera connected to a computer, where the camera is installed statically (fixed position) above the robot's work area. The camera functions like image processing to detect the object's position by taking the coordinates of the object. Then the object coordinates will be input into inverse kinematics that will produce an angle in every point of the servo arm so that the position of the end effector on the robot arm can be founded and reach the intended object. From the results of testing and analysis, it was found that the error in the webcam test to detect object coordinates was 2.58%, the error in the servo motion test was 12.68%, and the error in the inverse kinematics test was 7.85% on the x-axis, the error was 6.31% on the y-axis and an error of 12.77% on the z-axis. The reliability of the whole system is 66.66%.

scholarly journals Computational Analysis of Kinematics of 3 – Links Articulated Robotic Manipulator

2019 ◽  
Vol 9 (2) ◽  
pp. 3640-3643
Keyword(s):  
Inverse Kinematics ◽  
Computational Analysis ◽  
Robot Manipulators ◽  
Robotic Manipulator ◽  
Robotic Arm ◽  
Forward Kinematics ◽  
End Effector ◽  
Computational Estimation ◽  
Arm Motion ◽  
Free Body

The Computational Analysis of Kinematics of 3 – Links Articulated Robotic Manipulator has been presented in this. The design of robot manipulators requires accurate computational analysis, involving the geometric position of the linking arms. The method of Forward Kinematics and Inverse Kinematics were employed in estimating the robotic arm’s position with respect to link lengths and angle, in which the angle required to move the end effector to a desired position is estimated and determined. A three link robotic arm with a rigid rotational base was also illustrated using free body diagrams, and computational estimation of the required parameters. The outcomes of the forward kinematics reveals that the robot end effector position can be estimated using the values of x, y, and z coordinates thereby providing a better means of controlling or adapting robot’s arm/motion to its environment.

scholarly journals Kinematics Analysis of 5 DOF Robotic Arm

2020 ◽  
Vol 38 (3A) ◽  
pp. 412-422
Author(s):  
Tahseen F. Abaas ◽  
Ali A. Khleif ◽  
Mohanad Q. Abbood
Keyword(s):  
Inverse Kinematics ◽  
Jacobian Matrix ◽  
Maximum Error ◽  
Robotic Arm ◽  
Forward Kinematics ◽  
Algebraic Solution ◽  
Kinematics Analysis ◽  
End Effector ◽  
Position And Orientation

This paper presents the forward, inverse, and velocity kinematics analysis of a 5 DOF robotic arm. The Denavit-Hartenberg (DH) parameters are used to determination of the forward kinematics while an algebraic solution is used in the inverse kinematics solution to determine the position and orientation of the end effector. Jacobian matrix is used to calculate the velocity kinematics of the robotic arm. The movement of the robotic arm is accomplished using the microcontroller (Arduino Mega2560), which controlling on five servomotors of the robotic arm joints and one servo of the gripper. The position and orientation of the end effector are calculated using MATLAB software depending on the DH parameters. The results indicated the shoulder joint is more effect on the velocity of the robotic arm from the other joints, and the maximum error in the position of the end-effector occurred with the z-axis and minimum error with the y-axis.

Teaching Manipulator Kinematics by Painting With Light

2011 ◽  
Author(s):  
Michael Shomin ◽  
Jonathan Fiene
Keyword(s):  
Inverse Kinematics ◽  
Three Dimensional ◽  
Robotic Manipulators ◽  
Video Camera ◽  
Robotic Arm ◽  
End Effector ◽  
Teaching Platform ◽  
Manipulator Arm ◽  
Servo Controller ◽  
Six Degree Of Freedom

In this paper, we examine the creation and benefits of a new teaching platform to introduce and reinforce the key concepts of robotic manipulators in an introductory-level robotics course. This system combines a vintage PUMA 260 six-degree-of-freedom robotic arm with modern control circuitry and a Matlab API. The API operates as a servo controller for the robot, thereby allowing students to apply their knowledge of inverse kinematics to a real manipulator arm. To further motivate the exploration of manipulators, we have developed an open-ended project where students engage in the art of three-dimensional light painting. To facilitate this activity, a tricolor LED has been affixed to the end-effector of the robot. With a digital SLR camera, we take a long-exposure photograph as the robot is driven through a trajectory, effectively painting a picture with the end effector. We have also developed a method to quickly assemble pseudo-long-exposure photographs and videos using an inexpensive video camera. We believe this novel setup and project are an effective way to engage and motivate students to learn the underlying math and dynamics of robotic manipulators.

Pose Estimation of Objects Using Digital Image Processing for Pick-and-Place Applications of Robotic Arms

2020 ◽  
Vol 38 (5A) ◽  
pp. 707-718 ◽  
Author(s):  
Firas S. Hameed ◽  
Hasan M. Alwan ◽  
Qasim A. Ateia
Keyword(s):  
Image Processing ◽  
Robot Vision ◽  
Object Identification ◽  
Robotic Arm ◽  
Edge Image ◽  
End Effector ◽  
Robotic Arms ◽  
Pick And Place ◽  
Visual Interaction ◽  
2D Images

Robot Vision is one of the most important applications in Image processing. Visual interaction with the environment is a much better way for the robot to gather information and react more intelligently to the variations of the parameters in that environment. A common example of an application that depends on robot vision is that of Pick-And-Place objects by a robotic arm. This work presents a method for identifying an object in a scene and determines its orientation. The method presented enables the robot to choose the best-suited pair of points on the object at which the two-finger gripper can successfully pick the object. The scene is taken by a camera attached to the arm’s end effector which gives 2D images for analysis. The edge detection operation was used to extract a 2D edge image for all the objects in the scene to reduce the time needed for processing. The methods proposed showed accurate object identification which enabled the robotic to successfully identify and pick an object of interest in the scene.

scholarly journals Design and Control of an Automated SCARA Robotic Arm with a Pneumatic Soft Gripper

2021 ◽  
Vol 2070 (1) ◽  
pp. 012207
Author(s):  
V K Pranav ◽  
Nitheesh Kumar G ◽  
V Yadukrishnan ◽  
Krishnanand Anil ◽  
S Ghanashyam ◽  
...  
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Acrylonitrile Butadiene Styrene ◽  
Nitrile Rubber ◽  
Robotic Arm ◽  
End Effector ◽  
Pick And Place ◽  
Predicted Values ◽  
And Control ◽  
Butadiene Styrene

Abstract The paper focuses on the design, analysis and control of an automated 3 DOF SCARA robotic manipulator with an end effector made of asymmetric flexible pneumatic bellow actuator (AFPBA). The manipulator is made for use in the poultry industry and therefore tested in its ability to detect, pick and place eggs. The links of the manipulator are made using acrylonitrile butadiene styrene (ABS) and the end effector is made using nitrile rubber. A neural network derived from the VGG-16 and YOLO architecture is then implemented to detect and localize eggs. The predicted values were then used to calculate the inverse kinematics of the manipulator.

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Sign in / Sign up

Export Citation Format

Share Document