Wavelet network solution for the inverse kinematics problem in robotic manipulator

2006 ◽  
Vol 7 (4) ◽  
pp. 525-529 ◽  
Author(s):  
Hua Chen ◽  
Wei-shan Chen ◽  
Tao Xie
Keyword(s):  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Wavelet Network ◽  
Network Solution ◽  
Inverse Kinematics Problem

scholarly journals Cerebellum-inspired neural network solution of the inverse kinematics problem

2015 ◽  
Vol 109 (6) ◽  
pp. 561-574 ◽  
Author(s):  
Mitra Asadi-Eydivand ◽  
Mohammad Mehdi Ebadzadeh ◽  
Mehran Solati-Hashjin ◽  
Christian Darlot ◽  
Noor Azuan Abu Osman
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Network Solution ◽  
Inverse Kinematics Problem

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2020 ◽  
pp. 814-832
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Input Data ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

scholarly journals Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Aryslan Malik ◽  
Troy Henderson ◽  
Richard Prazenica
Keyword(s):  
Swarm Intelligence ◽  
Inverse Kinematics ◽  
A Priori ◽  
Trajectory Generation ◽  
Robotic Manipulator ◽  
Degree Of Freedom ◽  
Control Effort ◽  
End Effector ◽  
Multi Objective ◽  
Inverse Kinematics Problem

This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared.

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2017 ◽  
Vol 7 (3) ◽  
pp. 20-40 ◽  
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training ◽  
Computing Models

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

scholarly journals Study on the Inverse Kinematics Problem of Robot (2nd Report)

1997 ◽  
Vol 63 (5) ◽  
pp. 699-703
Author(s):  
Xiaohai JIN ◽  
Sachio SHIMIZU ◽  
Nobuyuki FURUYA
Keyword(s):  
Inverse Kinematics ◽  
Inverse Kinematics Problem

Kinematics of a Fully-Decoupled Remote Center-of-Motion Parallel Manipulator for Minimally Invasive Surgery

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
Chin-Hsing Kuo ◽  
Jian S. Dai
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Invasive Surgery ◽  
Inverse Kinematics ◽  
Parallel Manipulator ◽  
Degrees Of Freedom ◽  
Surgical Instruments ◽  
End Effector ◽  
Minimally Invasive Surgical ◽  
Inverse Kinematics Problem

A crucial design challenge in minimally invasive surgical (MIS) robots is the provision of a fully decoupled four degrees-of-freedom (4-DOF) remote center-of-motion (RCM) for surgical instruments. In this paper, we present a new parallel manipulator that can generate a 4-DOF RCM over its end-effector and these four DOFs are fully decoupled, i.e., each of them can be independently controlled by one corresponding actuated joint. First, we revisit the remote center-of-motion for MIS robots and introduce a projective displacement representation for coping with this special kinematics. Next, we present the proposed new parallel manipulator structure and study its geometry and motion decouplebility. Accordingly, we solve the inverse kinematics problem by taking the advantage of motion decouplebility. Then, via the screw system approach, we carry out the Jacobian analysis for the manipulator, by which the singular configurations are identified. Finally, we analyze the reachable and collision-free workspaces of the proposed manipulator and conclude the feasibility of this manipulator for the application in minimally invasive surgery.

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