Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator

1994 ◽  
Vol 2 (2) ◽  
pp. 123-134 ◽  
Author(s):  
S. Chiaverini ◽  
B. Siciliano ◽  
O. Egeland
Keyword(s):  
Least Squares ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Damped Least Squares

Robust task-space control of robot manipulators using differential equations for uncertainty estimation

Robotica ◽  
2016 ◽  
Vol 35 (9) ◽  
pp. 1923-1938 ◽  
Author(s):  
Alireza Izadbakhsh ◽  
Saeed Khorashadizadeh
Keyword(s):  
Differential Equations ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Design Procedure ◽  
Weierstrass Theorem ◽  
Actuator Dynamics ◽  
Model Free ◽  
Electrically Driven Robots ◽  
Linear Differential

SUMMARYMost control algorithms for rigid-link electrically driven robots are given in joint coordinates. However, since the task to be accomplished is expressed in Cartesian coordinates, inverse kinematics has to be computed in order to implement the control law. Alternatively, one can develop the necessary theory directly in workspace coordinates. This has the disadvantage of a more complex robot model. In this paper, a robust control scheme is given to achieve exact Cartesian tracking without the knowledge of the manipulator kinematics and dynamics, actuator dynamics and nor computing inverse kinematics. The control design procedure is based on a new form of universal approximation theory and using Stone–Weierstrass theorem, to mitigate structured and unstructured uncertainties associated with external disturbances and actuated manipulator dynamics. It has been assumed that the lumped uncertainty can be modeled by linear differential equations. As the method is Model-Free, a broad range of manipulators can be controlled. Numerical case studies are developed for an industrial robot manipulator.

scholarly journals Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators

2008 ◽  
Vol 3 (3) ◽  
pp. 224 ◽  
Author(s):  
Srinivasan Alavandar ◽  
M. J. Nigam
Keyword(s):  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Industrial Robot ◽  
Training Data ◽  
Robot Kinematics ◽  
Mathematical Representation ◽  
Inference System ◽  
Neuro Fuzzy

Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control. As the complexity of robot increases, obtaining the inverse kinematics solution requires the solution of non linear equations having transcendental functions are difficult and computationally expensive. In this paper, using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS, an implementation of a representative fuzzy inference system using a BP neural network-like structure, with limited mathematical representation of the system. Computer simulations conducted on 2 DOF and 3DOF robot manipulator shows the effectiveness of the approach.

Spatially Hyper-Redundant Robotic Inverse Kinematics by Discrete Link Characterization 

2014 ◽  
Vol 592-594 ◽  
pp. 2204-2209
Author(s):  
Anand Nagarajan ◽  
S. Joseph Winston ◽  
S. Venugopal
Keyword(s):  
Least Squares ◽  
Inverse Kinematics ◽  
Inverse Method ◽  
Jacobian Matrix ◽  
Least Squares Method ◽  
Kinematic Model ◽  
The Matrix ◽  
Damped Least Squares ◽  
Pseudo Inverse ◽  
Novel Design

In this paper, a novel design of a multi-sectioned, remotely-actuated, continuum type manipulator is presented. Spatially Hyper-Redundant Robot (SHRR) is based on a continuous backbone model which is divided into four sections. In the area of hyper redundant robotics, kinematic redundant systems result in non square Jacobian matrix which requires a pseudo inverse method to inverse the matrix. A methodology has been devised to solve the Inverse Kinematics (IK) problem of SHRR by predicting the curvature values of each of the section. Redundant IK techniques like Pseudo-Inverse Method (PIM), Jacobian Transpose Method (JTM), Damped Least Squares Method (DLS) and Selectively Damped Least Squares Method (SDLS) are tested on the formulated kinematic model of SHRR using MATLAB and a comparative study has been made.

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