A method for solving inverse kinematics of PUMA560 manipulator based on PSO-RBF network

Li, Zhe Ming; Li, Chun Gui; Lv, Shao Jiao

doi:10.1109/icnc.2012.6234507

Cited by 10 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, several researchers have proposed solving inverse kinemat- † Koji Kinoshita is the presenter of this paper. ics problems by the neural network based on PSO [12][13][14][15]. However, Kennedy has pointed out that the particles tend to converge quickly [16].…”

Section: Introductionmentioning

confidence: 98%

Estimation of inverse model based on ANN and PSO with adaptively varying acceleration coefficients

Kinoshita

Watanabe

Isshiki

2014

2014 Proceedings of the SICE Annual Conference (SICE)

View full text Add to dashboard Cite

We consider estimation of an inverse model for the uncertain systems. Multi-layered neural network (NN) can approximate any continuous nonlinear mapping, then we use NN in order to represent the inverse model. The the back-propagation rule, which is commonly used for updating the weights, requires the sensitivity function of the system. However, we can not calculate this function because of uncertainty of the systems. Hence, we apply particle swarm optimization (PSO) to update the weights. PSO is suitable for learning NN, because it dose not require the derivative of the objective function. This paper introduces a novel parameter automation strategy in order to overcome the premature convergence. The acceleration coefficients are adaptively varies with the distance between the particle and the gbest which is the best solution ever found by the swarm. We can maintain the diversity of the swarm and search a better solution around the gbest efficiently. We compare the proposed method with the basic PSO and TVAC PSO, which is proposed by Ratnaweera, through the simulation of the inverse kinematics problem. The proposed method can obtain the accurate inverse model than the basic PSO and TVAC PSO.

show abstract

Section: Introductionmentioning

confidence: 98%

Estimation of inverse model based on ANN and PSO with adaptively varying acceleration coefficients

Kinoshita

Watanabe

Isshiki

2014

2014 Proceedings of the SICE Annual Conference (SICE)

View full text Add to dashboard Cite

show abstract

“…LM algorithm is more suitable for dealing with huge sample data [7] and RBF network has good local approximation ability [8]. Therefore, a parallel neural network method based on BP and RBF neural network for solving inverse kinematics of robot is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Solving Inverse Kinematics of Robot Based on BP and RBF Neural Network

Yuan

Feng

2014

2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control

View full text Add to dashboard Cite

A parallel neural network algorithm based on BP and RBF neural network for solving inverse kinematics of robot is proposed in this paper. Concrete steps of this method and related matters that should be noticed are presented. BP network is trained by LM algorithm and RBF network increases radial basis neurons automatically. The simulation results of PUMA560 show that this algorithm is simple and reliable, making the error of the whole system become smaller. In addition, the algorithm effectively solves the problem of inverse kinematics and overcomes the defects of traditional methods for solving inverse kinematics, such as large amount of calculation, slow convergence rate and low accuracy.

show abstract

“…For example, the particle swarm optimization (PSO) has been used by different authors to solve the inverse kinematics for robot manipulators, including the redundant robots. [27][28][29][30] Some authors presented modifications to the PSO to overcome problems such as premature convergence. Ren et al 11 introduced a hybrid approach for solving the inverse kinematics based on differential evolution (DE) and biogeography-based optimization (BBO); they called this method hybrid BBO (HBBO).…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Inverse kinematics of mobile manipulators based on differential evolution

López-Franco

Hernández-Barragán

Alanis

et al. 2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

The solution of the inverse kinematics of mobile manipulators is a fundamental capability to solve problems such as path planning, visual-guided motion, object grasping, and so on. In this article, we present a metaheuristic approach to solve the inverse kinematic problem of mobile manipulators. In this approach, we represent the robot kinematics using the Denavit-Hartenberg model. The algorithm is able to solve the inverse kinematic problem taking into account the mobile platform. The proposed approach is able to avoid singularities configurations, since it does not require the inversion of a Jacobian matrix. Those are two of the main drawbacks to solve inverse kinematics through traditional approaches. Applicability of the proposed approach is illustrated using simulation results as well as experimental ones using an omnidirectional mobile manipulator.

show abstract

A method for solving inverse kinematics of PUMA560 manipulator based on PSO-RBF network

Cited by 10 publications

References 2 publications

Estimation of inverse model based on ANN and PSO with adaptively varying acceleration coefficients

Estimation of inverse model based on ANN and PSO with adaptively varying acceleration coefficients

A New Algorithm for Solving Inverse Kinematics of Robot Based on BP and RBF Neural Network

Inverse kinematics of mobile manipulators based on differential evolution

Contact Info

Product

Resources

About