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.

Optimization of the Robot's Position Base Point by Using the Proper Algorithm and Iterative Pseudo Inverse Jacobian Neural Network Matrix Method

2016 ◽  
Vol 859 ◽  
pp. 153-160
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai ◽  
Liviu Ciupitu
Keyword(s):  
Neural Network ◽  
Matrix Method ◽  
Jacobian Matrix ◽  
Optimal Solution ◽  
Parallel Robot ◽  
Base Point ◽  
Objective Functions ◽  
Optimal Position ◽  
Coupled Method ◽  
Pseudo Inverse

In the robotized production one of the more important think is to choose the optimal solution to use the robots with respect an objective function which represents, for example, minimum time of motion during a application, or minimum consumption of energy, or maximum precision, or combination of these. Some objective functions could results from the specificity of the application like is the case of casting of forging, where the minimum of the accumulation of heat could be one of the optimization criteria. In the controlling of the space movement of the end effecter and the robot’s joints of the all robots from the applications, one of the most important think is 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 Pseudo Inverse Jacobian Matrix Method. In this paper was 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) to establish the optimal position of the application point of the robot's base with respect simultaneously two objective functions: the extreme precision and the minimum of the movements time. The paper shown how can be changed the multi robots application in to one application with parallel robot structure with three independent robots, all of them with optimal location point with respect the obiective function. 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.

Proper Smart Method of the Inverse Kinematic Problem

2015 ◽  
Vol 772 ◽  
pp. 455-460 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai
Keyword(s):  
Inverse Kinematics ◽  
Jacobian Matrix ◽  
Inverse Kinematic ◽  
Iterative Approximation ◽  
Inverse Kinematic Problem ◽  
Space Curves ◽  
Inverse Kinematics Problem ◽  
Artificial Intelligence Algorithm ◽  
Pseudo Inverse

One of the most precise method solving the inverse kinematics problem in the redundant cases of the robots is the coupled method. The proposed method use the Iterative Pseudo Inverse Jacobian Matrix Method (IPIJMM) coupled with the proper Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN-TDRL). One precise solution of the inverse kinematics problem is very difficult to find, when the degree of freedom increase and in many cases this is impossible because the redundant solutions. In all these cases must be used the numerical iterative approximation, like the proposed method, with artificial intelligence algorithm. The paper describe all the steps in one case study to obtain the space circle curve in different planes by using one arm type robot and the proposed method. The errors of the space movement of the robot end-effecter, after applying the proposed method, was less than 0,01. The presented method is general and it can be used in all other robots types 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.

Increase the Space Trajectory Precision by Using the Proper Assisted Research Method for Inverse Kinematics Problem

2015 ◽  
Vol 789-790 ◽  
pp. 711-716 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai
Keyword(s):  
Inverse Kinematics ◽  
Numerical Approximation ◽  
Jacobian Matrix ◽  
Industrial Robot ◽  
Forward Kinematics ◽  
Hyperbolic Tangent ◽  
Space Curves ◽  
Inverse Kinematics Problem ◽  
Pseudo Inverse

Inverse kinematics model of the industrial robot is used in the control of the end-effecter trajectory. The solution of the inverse kinematics problem is very difficult to find, when the degree of freedom increase and in many cases this is impossible. In these cases is used the numerical approximation or other method with diffuse logic. The paper showed one new method for optimization of the inverse cinematic solution by applying the proper assisted Iterative Pseudo Inverse Jacobian Matrix Method coupled with proper Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links Method (IPIJMM-SBHTNN-TDRLM). In the paper was shown one case study to obtain one space circle curve by using one arm type robot and the proposed method. The errors of the space coordinates of the circle, after applying the proposed method, was less than 0.001. The study has contained the determining the internal coordinates corresponding to the external coordinates of the circle space curve, by solving the inverse kinematics with the proposed method and after that, by applying the forward kinematics to this coordinates, were obtained the external coordinates, what were compared with the theoretical one. The presented method is general and it can be used in all other robots types and for all other conventional and unconventional space curves.

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.

Proper LabVIEW Instrumentation of the Robot’s Kinematics and Dynamics Behavior

2015 ◽  
Vol 811 ◽  
pp. 291-299 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai ◽  
Tadeusz Mikolajczyk ◽  
Doru Bardac
Keyword(s):  
Matrix Method ◽  
Inverse Kinematics ◽  
Quaternion Algebra ◽  
Jacobian Matrix ◽  
Home Position ◽  
Space Curves ◽  
Working Space ◽  
Initial Settings ◽  
Pseudo Inverse

The most important in the study of the robots is the kinematic and dynamic analyze. Many researchers studied the kinematics or dynamics without simulation and assisted analyze that it is very heavy to understand the behavior and to show some characteristics. The paper shows one assisted method by using the virtual proper LabVIEW instrumentation (VI). For the forward kinematics (FK) and for direct dynamics (DD) was used one recurrent matrix method which was developed with quaternion algebra, that will be possible to use in many different other types of robots, only by initial settings of the type of joints, the movement axis, the home position, the dimension of each robot’s body, the application point in the working space of the manufacturing cells and the internal coordinates in each joint. For the inverse kinematics (IK) we used the Iterative Pseudo Inverse Jacobian Matrix Method (IPIJMM) coupled with the proper Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN-TDRL). The paper describe all steps in one case study to obtain the space curve in different Euller planes by using one arm type robot and the proposedVI-s. The presented method and the LabVIEWVI-s are generally and they can be used in all other robots types and for all other conventional and unconventional space curves.

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

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

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