Robot Manipulator Control with Inverse Kinematics PD-Pseudoinverse Jacobian and Forward Kinematics Denavit Hartenberg

TL;DR

This paper develops a vision-based control system for a robotic arm using PD-PIJ inverse kinematics and DH forward kinematics to sort objects by color, achieving a success rate of over 94% in real experiments.

Contribution

It introduces a combined PD-PIJ inverse kinematics and DH forward kinematics approach for vision-based robotic manipulation with real-world validation.

Findings

Success rate of 94.46% in object sorting task

Effective error propagation analysis using PDF and Weibull distribution

Robust performance in real manipulator implementation

Abstract

This paper presents the development of vision-based robotic arm manipulator control by applying Proportional Derivative-Pseudoinverse Jacobian (PD-PIJ) kinematics and Denavit Hartenberg forward kinematics. The task of sorting objects based on color is carried out to observe error propagation in the implementation of manipulator on real system. The objects image captured by the digital camera were processed based on HSV-color model and the centroid coordinate of each object detected were calculated. These coordinates are end effector position target to pick each object and were placed to the right position based on its color. Based on the end effector position target, PD-PIJ inverse kinematics method was used to determine the right angle of each joint of manipulator links. The angles found by PD-PIJ is the input of DH forward kinematics. The process was repeated until the square end effector reached the target. The experiment of model and implementation to actual manipulator were analyzed using Probability Density Function (PDF) and Weibull Probability Distribution. The result shows that the manipulator navigation system had a good performance. The real implementation of color sorting task on manipulator shows the probability of success rate cm is 94.46% for euclidian distance error less than 1.2 cm.