Solving inverse kinematics problem of robot arm based on a-star algorithm

2016 ◽  
Author(s):  
Jirayus Chaichawananit ◽  
Saiyan Saiyod
Keyword(s):  
Inverse Kinematics ◽  
Robot Arm ◽  
Inverse Kinematics Problem

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.

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 Using proposed optimization algorithm for solving inverse kinematics of human upper limb applying in rehabilitation robotic

2021 ◽  
Author(s):  
Trung Nguyen ◽  
Tam Bui ◽  
Ha Pham
Keyword(s):  
Upper Limb ◽  
Inverse Kinematics ◽  
Search Space ◽  
Human Motion ◽  
Robot Arm ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Robots ◽  
Inverse Kinematics Problem ◽  
Limb Rehabilitation ◽  
Human Upper Limb

AbstractThe requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this problem are not suitable, it can cause undesirable damage to the patient when exercising. Normally, the problem of Inverse Kinematics in the robotics field, as well as the natural field, especially for redundant driven systems, often requires the application of a lot of techniques. The redundancy in Degree of Freedom (DoF), the nonlinearity of the system leads to solve inverse kinematics problem more challenge. In this study, we proposed to apply the self-adaptive control parameters in Differential Evolution with search space improvement (Pro-ISADE) to solve the problem for the human upper limb, which is a very typical redundancy model in nature. First of all, the angles of the joints were measured by a proposed Exoskeleton type Human Motion Capture System (E-HMCS) when the wearer performs some Activities of Daily Living (ADL) and athletic activities. The values of these measured angles joints then were put into the forward kinematics model to find the end effector trajectories. After having these orbits, they were re-fed into the proposed Pro-ISADE algorithm mentioned above to process the IK problem and obtain the predicted joints angular values. The experimental results showed that the predicted joints’ values closely follow the measured joints’ values. That demonstrates the ability to apply the Pro-ISADE algorithm to solve the problem of Inverse Kinetics of the human upper limb as well as the upper limb rehabilitation robot arm.

A new ABC variant for solving inverse kinematics problem in 5 DOF robot arm

2018 ◽  
Vol 73 ◽  
pp. 24-38 ◽  
Author(s):  
Ahmed El-Sherbiny ◽  
Mostafa A. Elhosseini ◽  
Amira Y. Haikal
Keyword(s):  
Inverse Kinematics ◽  
Robot Arm ◽  
Inverse Kinematics Problem
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