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.

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 IKBT: Solving Symbolic Inverse Kinematics with Behavior Tree (Extended Abstract)

2020 ◽  
Author(s):  
Dianmu Zhang ◽  
Blake Hannaford
Keyword(s):  
Closed Form ◽  
Inverse Kinematics ◽  
Intelligent System ◽  
Logical Reasoning ◽  
Robot Arm ◽  
Kinematics Analysis ◽  
Closed Form Solutions ◽  
Behavior Tree ◽  
Behavior Trees ◽  
High Level

Inverse kinematics solves the problem of how to control robot arm joints to achieve desired end effector positions, which is critical to any robot arm design and implementations of control algorithms. It is a common misunderstanding that closed-form inverse kinematics analysis is solved. Popular software and algorithms, such as gradient descent or any multi-variant equations solving algorithm, claims solving inverse kinematics but only on the numerical level. While the numerical inverse kinematics solutions are relatively straightforward to obtain, these methods often fail, even when the inverse kinematics solutions exist. Therefore, closed-form inverse kinematics analysis is superior, but there is no generalized automated algorithm. Up till now, the high-level logical reasoning involved in solving closed-form inverse kinematics made it hard to automate, so it's handled by human experts. We developed IKBT, a knowledge-based intelligent system that can mimic human experts' behaviors in solving closed-from inverse kinematics using Behavior Tree. Knowledge and rules used by engineers when solving closed-from inverse kinematics are encoded as actions in Behavior Tree. The order of applying these rules is governed by higher level composite nodes, which resembles the logical reasoning process of engineers. It is also the first time that the dependency of joint variables, an important issue in inverse kinematics analysis, is automatically tracked in graph form. Besides generating closed-form solutions, IKBT also explains its solving strategies in human (engineers) interpretable form. This is a proof-of-concept of using Behavior Trees to solve high-cognitive problems.

scholarly journals Investigation of Cyclicity of Kinematic Resolution Methods for Serial and Parallel Planar Manipulators

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Maurizio Ruggiu ◽  
Andreas Müller
Keyword(s):  
Objective Function ◽  
Inverse Kinematics ◽  
Joint Space ◽  
Point Of View ◽  
Initial Configuration ◽  
Local Optimum ◽  
Closed Path ◽  
Redundancy Resolution ◽  
Projected Gradient Method ◽  
Inverse Kinematics Problem

Kinematic redundancy of manipulators is a well-understood topic, and various methods were developed for the redundancy resolution in order to solve the inverse kinematics problem, at least for serial manipulators. An important question, with high practical relevance, is whether the inverse kinematics solution is cyclic, i.e., whether the redundancy solution leads to a closed path in joint space as a solution of a closed path in task space. This paper investigates the cyclicity property of two widely used redundancy resolution methods, namely the projected gradient method (PGM) and the augmented Jacobian method (AJM), by means of examples. Both methods determine solutions that minimize an objective function, and from an application point of view, the sensitivity of the methods on the initial configuration is crucial. Numerical results are reported for redundant serial robotic arms and for redundant parallel kinematic manipulators. While the AJM is known to be cyclic, it turns out that also the PGM exhibits cyclicity. However, only the PGM converges to the local optimum of the objective function when starting from an initial configuration of the cyclic trajectory.

scholarly journals Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Aryslan Malik ◽  
Troy Henderson ◽  
Richard Prazenica
Keyword(s):  
Swarm Intelligence ◽  
Inverse Kinematics ◽  
A Priori ◽  
Trajectory Generation ◽  
Robotic Manipulator ◽  
Degree Of Freedom ◽  
Control Effort ◽  
End Effector ◽  
Multi Objective ◽  
Inverse Kinematics Problem

This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared.

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.

Application of Dual-Number Matrices to the Inverse Kinematics Problem of Robot Manipulators

1985 ◽  
Vol 107 (2) ◽  
pp. 201-208 ◽  
Author(s):  
G. R. Pennock ◽  
A. T. Yang
Keyword(s):  
Closed Form ◽  
Inverse Kinematics ◽  
Robot Manipulators ◽  
Closed Form Solutions ◽  
Dual Number ◽  
Special Geometry ◽  
Inverse Kinematics Problem ◽  
Concise Representation ◽  
Joint Parameters ◽  
General Geometry

This paper presents the application of dual-number matrices to the formulation of displacement equations of robot manipulators with completely general geometry. Dual-number matrices make possible a concise representation of link proportions and joint parameters; together with the orthogonality properties of the matrices we are able to derive, in a systematic manner, closed-form solutions for the joint displacements of robot manipulators with special geometry as illustrated by three examples. It is hoped that the method presented here will provide a meaningful alternative to existing methods for formulating the inverse kinematics problem of robot manipulators.

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