Kinematics Analysis of a Novel Five-Degree-of-Freedom Spatial Parallel Micromanipulator

Prusak, Daniel; Kobus, Konrad; Karpiel, Grzegorz

doi:10.1155/2014/806294

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The direct kinematic model seeks examine the position and orientation of the end effector of the robot (Figure 2), based on the values of the variables of each joint and the constructive parameters of the elements that compose it [1,21,17,16]. To solve the DKM, the following methods are found: • Geometric Method: Geometric relationships that define the position and location of the final effector are sought.…”

Section: Direct Kinematic Model -Dkmmentioning

confidence: 99%

Kinematic model of the scorbot 4PC manipulator implemented in Matlab's Guide

Trujillo¹,

Serrezuela²,

Azhmyakov³

et al. 2018

ces

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This paper will discuss the development and implementation of the direct and inverse kinematics models with five degrees of freedom manipulator robot (5 D.O.F) Scorbot ER 4PC , under an interface designed in Matlab. For the analysis of the Direct Kinematic Model (DKM), parameters and homogeneous transformation of Denvit Hartenverg are used, and for the Inverse Kinematic Model (IKM) an algebraic solution is used, which must be manipulate directly the equations found in DKM. These models are processed in Matlab, where they will have a graphical

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Section: Direct Kinematic Model -Dkmmentioning

confidence: 99%

Kinematic model of the scorbot 4PC manipulator implemented in Matlab's Guide

Trujillo¹,

Serrezuela²,

Azhmyakov³

et al. 2018

ces

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show abstract

“…An analytical solution of inverse kinematics for a five DOF spatial parallel micromanipulator was presented. A geometrical mode and structural of system were introduced for the microrobot's task [17]. Forward kinematics and inverse kinematics were calculated and simulation was done for joints and link parameters of six-axis robotic arm.…”

Section: Introductionmentioning

confidence: 99%

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Almusawi

lger

Kapucu

2016

Computational Intelligence and Neuroscience

129

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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.

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Neural Network Based Inverse Kinematic Solution of a 5 DOF Manipulator for Industrial Application

Aravinddhakshan

Apte

Saxena

2021

J. Phys.: Conf. Ser.

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This paper showcases the application of Neural Networks for manipulation. Algorithm based approaches work well although are limited in their ability to find solutions sometimes. The tedious task of programming a manipulator can be replaced with Neural Networks which can learn how to solve Inverse Kinematics. We present a pick and place scenario using Neural Network to solve Inverse Kinematics. Their ability to learn from examples make them a good candidate to solve the inverse kinematics problem. For this purpose, a unique configuration of 5 DOF arm is designed to suit industrial needs. To train the network, a dataset of random joint positions is created and forward kinematics is derived for the corresponding joint angles. The joint variables are then fed to a path planner in Simulink and then the process is simulated.

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Kinematics Analysis of a Novel Five-Degree-of-Freedom Spatial Parallel Micromanipulator

Cited by 4 publications

References 16 publications

Kinematic model of the scorbot 4PC manipulator implemented in Matlab's Guide

Kinematic model of the scorbot 4PC manipulator implemented in Matlab's Guide

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Neural Network Based Inverse Kinematic Solution of a 5 DOF Manipulator for Industrial Application

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