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

Ogawa, Takehiko; Kanada, Hajime

doi:10.1155/2010/870923

Cited by 15 publications

(8 citation statements)

References 11 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under this criterion, three cases should be considered in the manipulator decision-making: existence, uniqueness, and stability. 22 In Figure 4, an overview of how the robot operates to solve the ill-posed problem is shown. The first block (I) represents the human-machine interaction (HMI) and the variable declaration.…”

Section: Procedures For Optimizing the Test Trajectoriesmentioning

confidence: 99%

Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace

Funes-Lora

Portilla-Flores

Rivera-Blas

et al. 2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic endeffector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton's method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.

show abstract

Section: Procedures For Optimizing the Test Trajectoriesmentioning

confidence: 99%

Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace

Funes-Lora

Portilla-Flores

Rivera-Blas

et al. 2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…In this way, the inverse problem for estimating input x from output y is solved with a multilayer neural network by using the forward relationship inversely. Further, network inversion has been applied to image restoration (Valova et al, 1995) and the inverse kinematics of robot arms (Ogawa & Kanada, 2010).…”

Section: Complex-valued Network Inversionmentioning

confidence: 99%

Transformations of Image Filters for Machine Vision Using Complex-Valued Neural Networks

Ogawa¹

2012

Human-Centric Machine Vision

Self Cite

View full text Add to dashboard Cite

“…The inverse kinematics of three DOF robotic arm was solved by multilayer network inversion method; the joint angles were estimated for given end effector position in a simulation of three-link robotic arm. The results showed an approximation solution for inverse kinematics [1]. Singularities and uncertainties in arm configurations are the main complications in the kinematics of robot control, in order to have a realistic solution based on one of the heuristic methods; artificial neural network (ANN) was suggested for a nonsurgical robot.…”

Section: Introductionmentioning

confidence: 99%

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Almusawi

lger

Kapucu

2016

Computational Intelligence and Neuroscience

124

View full text Add to dashboard Cite

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

show abstract

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

Cited by 15 publications

References 11 publications

Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace

Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace

Transformations of Image Filters for Machine Vision Using Complex-Valued Neural Networks

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Contact Info

Product

Resources

About