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End-Effector Position Analysis Using Forward Kinematics For 5 Dof Pravak Robot Arm Jolly Atit Shah; S.S. Rattan; B.C. Nakra
IAES International Journal of Robotics and Automation (IJRA) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (515.984 KB) | DOI: 10.11591/ijra.v2i3.pp112-116

Abstract

Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major issue. This paper involves the kinematic analysis of a Pravak Robot arm which is used for doing successful robotic manipulation task in its workspace. The Pravak Robot Arm is a 5-DOF robot having all the joints revolute. The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. In this study the Denavit- Hartenberg (D-H) model is used to model robot links and joints. Pravak Robot Arm is a simple and safe robotic system designed for laboratory training and research applications. This robot allows to gain theoretical and practical experience in robotics, automation and control systems. The MATLAB R2007 is used to analyse end effectors position for a set of joint parameter.
Kinematic Analysis of 3-DOF Planer Robot Using Artificial Neural Network Jolly Atit Shah; S.S. Rattan; Bahadur Chand Nakra
IAES International Journal of Robotics and Automation (IJRA) Vol 1, No 3: September 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.983 KB)

Abstract

Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major issue. This paper involves the forward and inverse kinematics of 3-DOF robotic manipulator with revolute joints. In this study the Denavit- Hartenberg (D-H) model is used to model robot links and joints. Also forward and inverse kinematics solution has been achieved using Artificial Neural Networks for 3-DOF robotic manipulator. It shows that by using artificial neural network the solution we get is faster, acceptable and has zero error.DOI: http://dx.doi.org/10.11591/ijra.v1i3.511