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

scholarly journals Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Takehiko Ogawa ◽  
Hajime Kanada
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Inverse Kinematics ◽  
Robot Arm ◽  
Joint Angles ◽  
End Effector ◽  
Inverse Kinematics Problem ◽  
Ill Posed ◽  
The Given

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

Investigation of Neural-Network-Based Inverse Kinematics for a 6-DOF Serial Manipulator With Non-Spherical Wrist

2018 ◽  
Author(s):  
Benjamin E. Hargis ◽  
Wesley A. Demirjian ◽  
Matthew W. Powelson ◽  
Stephen L. Canfield
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Computational Time ◽  
Robot Arm ◽  
Training Set ◽  
Test Set ◽  
Network Training ◽  
Inverse Kinematics Problem ◽  
Spherical Wrist ◽  
Artificial Neural Network Ann

This study proposes using an Artificial Neural Network (ANN) to train a 6-DOF serial manipulator with a non-spherical wrist to solve the inverse kinematics problem. In this approach, an ANN has been trained to determine the configuration parameters of a serial manipulator that correspond to the position and pose of its end effector. The network was modeled after the AUBO-i5 robot arm, and the experimental results have shown the ability to achieve millimeter accuracy in tool space position with significantly reduced computational time relative to an iterative kinematic solution when applied to a subset of the workspace. Furthermore, a separate investigation was conducted to quantify the relationship between training example density, training set error, and test set error. Testing indicates that, for a given network, sufficient example point density may be approximated by comparing the training set error with test set error. The neural network training was performed using the MATLAB Neural Network Toolbox.

Controlling of the 3D Space Trajectory of the Multi Robots Applications by Using the Proper Iterative Pseudo Inverse Jacobian Neural Network Matrix Method

2016 ◽  
Vol 841 ◽  
pp. 227-233
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai ◽  
Doru Bardac
Keyword(s):  
Neural Network ◽  
Matrix Method ◽  
Inverse Kinematics ◽  
Parallel Robot ◽  
Coupled Method ◽  
Space Curves ◽  
3D Space ◽  
The Neural Network ◽  
Inverse Kinematics Problem ◽  
Pseudo Inverse

In many applications we used the multi robots with the central coordination of the 3D space trajectory. In the controlling of the space movement of the end effecter of the all robots from this type of applications and the robot’s joints one of the most important problem is to solve the forward and inverse kinematics, that is different from the single robot application. It is important to know with the extreme precision the joints relative displacements of all robots. One of the most precise method to solve the inverse kinematics problem in the robots with redundant chain is the complex coupled method of the neural network with Iterative Jacobian Pseudo Inverse method. In this paper was proposed and used the proper coupled method Iterative Pseudo Inverse Jacobian Matrix Method (IPIJMM) with Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN-TDRL). The paper contents the mathematical matrix model of the forward kinematics of multiple robots applications, mathematical model of the proper iterative algorithm and all proper virtual LabVIEW instrumentation, to obtain the space conventional and unconventional curves in different Euller planes for one case study of three simultaneously robots movement with extreme precision of the end-effecter less than 0.001mm. The paper shown how can be changed the multi robots application in to one application with parallel robot structure with three independent robots. The presented method and the virtual instrumentations (VI) are generally and they can be used in all other robots application and for all other conventional and unconventional space curves.

Assisted Research and Optimization of the Proper Neural Network Solving the Inverse Kinematics Problem

2012 ◽  
Vol 463-464 ◽  
pp. 827-832
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Dan Paune ◽  
Oprean Aurel
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Inverse Kinematics ◽  
Hyperbolic Tangent ◽  
Variable Parameters ◽  
The Neural Network ◽  
Variable Step ◽  
Inverse Kinematics Problem ◽  
Sensitive Function ◽  
Target Data

Finding the better solution of the neural network design to solve the inverse kinematics problem with the minimum of the trajectory errors is very difficult, because there are many variable parameters and many redundant solutions. The presented paper show the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network model solving the inverse kinematics problem. We were been studied the number of neurons in each layers, the sensitive function for the first and second layer, the magnifier coefficient of the trajectory error, the variable step of the time delay and the position of this block, the different cases of target data and the case when the hidden target data were adjusted. All obtained results were been verified by applying the proper direct kinematics virtual LabVIEW instrumentation. Finally we were obtained one optimal Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN(TDRL)) type, what can be used to solve the inverse kinematics problem with maximum 4% of trajectory errors.

Optimize the Satellite Orientation by Using the Inertial Pulse Method, Intelligent Damper, Dynamics, Kinematics and Proper Neural Network

2012 ◽  
Vol 232 ◽  
pp. 665-673
Author(s):  
Adrian Olaru
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Inverse Dynamics ◽  
Pulse Method ◽  
Good Precision ◽  
Small Satellite ◽  
The Neural Network ◽  
Control Orientation ◽  
Inverse Kinematics Problem ◽  
Electrical Motors

In the paper are shown one assisted method of the control orientation inertial mechanism of the small satellite. It is presented some of the general components of the small satellite and the possibility to orientation them between three axes using the inertial pulse of the electrical motors. The angular space position was determines by command of the final point and using the proper neural network to solve the inverse kinematics problem with the minimum of the errors. The precision of the space orientation position was increased by using in the proper controlling schema a direct kinematics module, direct and inverse dynamics, intelligent damper and one multiple inertial pulse method what was described in the paper. Finally was obtained one very good precision, before 2%.The neural network, the inertial pulse method, the results and the virtual LabVIEW instrumentation could be used in many other researches on the field.

Sign in / Sign up

Export Citation Format

Share Document