scholarly journals Solving forward and inverse kinematics problem for a robot arm (2DOF) using Fuzzy Neural Petri Net (FNPN)

2021 ◽  
Vol 1773 (1) ◽  
pp. 012009
Author(s):  
Wael Hussein Zayer ◽  
Zahraa Ali Maeedi ◽  
Abdul Jabber Fathel Ali
Keyword(s):  
Petri Net ◽  
Inverse Kinematics ◽  
Robot Arm ◽  
Fuzzy Neural ◽  
Inverse Kinematics Problem ◽  
Forward And Inverse Kinematics

Improved Artificial Potential Field Method Manipulator Inverse Kinematics

2013 ◽  
Vol 273 ◽  
pp. 119-123
Author(s):  
Ding Jin Huang ◽  
Teng Liu
Keyword(s):  
Potential Field ◽  
Inverse Kinematics ◽  
Search Space ◽  
Field Method ◽  
Robotic Arm ◽  
Artificial Potential Field ◽  
Robot Arm ◽  
Potential Field Method ◽  
Inverse Kinematics Problem ◽  
Artificial Potential Field Method

The use of traditional analytical method for manipulator inverse kinematics is able to get a display solution with the limitations of the application, only when the robotic arm has a specific structure. In view of the insufficient, this paper presents an improved artificial potential field method to solve the inverse kinematics problem of the manipulator which does not have a special structure. Firstly, establish the standard DH model for the robot arm. Then the strategy that improves search space of artificial potential field method and motion control standard is presented by combining artificial potential field method with the manipulator. Finally, the simulation results show that the proposed method is effective.

MODELING AND SIMULATION OF A 5-AXIS RV-2AJ ROBOT USING SIMMECHANICS

2015 ◽  
Vol 76 (4) ◽  
Author(s):  
Mohammad Afif Ayob ◽  
Wan Nurshazwani Wan Zakaria ◽  
Jamaludin Jalani ◽  
Mohd Razali Md Tomari
Keyword(s):  
Modeling And Simulation ◽  
Inverse Kinematics ◽  
Cad Model ◽  
Robot Arm ◽  
Simulation Environment ◽  
Kinematics Simulation ◽  
Real Robot ◽  
Forward And Inverse Kinematics ◽  
Simulation Results ◽  
Multi Body

This paper presents the reliability and accuracy of the developed model of 5-axis Mitsubishi RV-2AJ robot arm. The CAD model of the robot was developed by using SolidWorks while the multi-body simulation environment was demonstrated by using SimMechanics toolbox in MATLAB. The forward and inverse kinematics simulation results proposed that the established model resembles the real robot with accuracy of 98.99%. 

Analysis of the Kinematics of an Eight-Wheeled Mobile Platform

2013 ◽  
Vol 198 ◽  
pp. 67-72
Author(s):  
Marek Stania
Keyword(s):  
Inverse Kinematics ◽  
Object Motion ◽  
Forward Kinematics ◽  
Contact Phenomenon ◽  
Kinematics Model ◽  
Inverse Kinematics Problem ◽  
Transport Vehicle ◽  
Forward And Inverse Kinematics ◽  
Motion Parameters ◽  
Simulation Results

This paper presents the modeling problem connected with the autonomous transport vehicle designed at Hochschule Ravensburg-Weingarten. The forward and inverse kinematics problem of eight-wheeled autonomous transport vehicle have been formulated and solved, additionally examples of simulation results representing the changes of individual motion parameters have been presented. Contact phenomenon between foundation and drive wheel has been taken into account in the kinematics model. Motion trajectory and velocity of the selected point belonging to the platform have been intended while the inverse kinematics problem has been solved. The forward kinematics problem has been worked out in order to verify correctness of the studied kinematics model. The presented simulation results point out compatibility of the worked out kinematics model of investigated object. The worked out models allow carrying out analysis of object motion through simulation investigations on the basis of proposed computational model.

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.

scholarly journals Hybrid Taguchi DNA Swarm Intelligence for Optimal Inverse Kinematics Redundancy Resolution of Six-DOF Humanoid Robot Arms

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hsu-Chih Huang ◽  
Sendren Sheng-Dong Xu ◽  
Huan-Shiuan Hsu
Keyword(s):  
Swarm Intelligence ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Robot Arm ◽  
Redundancy Resolution ◽  
Closed Form Solutions ◽  
Robot Arms ◽  
Inverse Kinematics Problem ◽  
Six Dof ◽  
Six Degree Of Freedom

This paper presents a hybrid Taguchi deoxyribonucleic acid (DNA) swarm intelligence for solving the inverse kinematics redundancy problem of six degree-of-freedom (DOF) humanoid robot arms. The inverse kinematics problem of the multi-DOF humanoid robot arm is redundant and has no general closed-form solutions or analytical solutions. The optimal joint configurations are obtained by minimizing the predefined performance index in DNA algorithm for real-world humanoid robotics application. The Taguchi method is employed to determine the DNA parameters to search for the joint solutions of the six-DOF robot arms more efficiently. This approach circumvents the disadvantage of time-consuming tuning procedure in conventional DNA computing. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods. This Taguchi-based DNA (TDNA) solver outperforms the conventional solvers, such as geometric solver, Jacobian-based solver, genetic algorithm (GA) solver and ant, colony optimization (ACO) solver.

scholarly journals Digital Hardware Realization of Forward and Inverse Kinematics for a Five-Axis Articulated Robot Arm

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Bui Thi Hai Linh ◽  
Ying-Shieh Kung
Keyword(s):  
Integrated Circuits ◽  
Inverse Kinematics ◽  
High Speed ◽  
Robot Arm ◽  
Hardware Realization ◽  
Finite State ◽  
Motion Speed ◽  
Forward And Inverse Kinematics ◽  
Articulated Robot ◽  
Five Axis

When robot arm performs a motion control, it needs to calculate a complicated algorithm of forward and inverse kinematics which consumes much CPU time and certainty slows down the motion speed of robot arm. Therefore, to solve this issue, the development of a hardware realization of forward and inverse kinematics for an articulated robot arm is investigated. In this paper, the formulation of the forward and inverse kinematics for a five-axis articulated robot arm is derived firstly. Then, the computations algorithm and its hardware implementation are described. Further, very high speed integrated circuits hardware description language (VHDL) is applied to describe the overall hardware behavior of forward and inverse kinematics. Additionally, finite state machine (FSM) is applied for reducing the hardware resource usage. Finally, for verifying the correctness of forward and inverse kinematics for the five-axis articulated robot arm, a cosimulation work is constructed by ModelSim and Simulink. The hardware of the forward and inverse kinematics is run by ModelSim and a test bench which generates stimulus to ModelSim and displays the output response is taken in Simulink. Under this design, the forward and inverse kinematics algorithms can be completed within one microsecond.

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