Three Recurrent Neural Networks and Three Numerical Methods for Solving a Repetitive Motion Planning Scheme of Redundant Robot Manipulators

Zhang, Zhijun; Zheng, Lunan; Yu, Jin Hai; Li, Yuanqing; Yu, Zhuliang

doi:10.1109/tmech.2017.2683561

Cited by 145 publications

(44 citation statements)

References 29 publications

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“…The forward kinematic equation is r = f (θ ) [18], where θ(t) ∈ R 6 denotes the joint-angular vector, and r(t) ∈ R 3 denotes the end-effector path. Due to the redundancy and nonlinearity of a robot manipulator, it is difficult to straightly obtain its inverse kinematic solution θ(t) through known r(t) [45]- [47].…”

Section: A Robot Tracking Problemmentioning

confidence: 99%

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Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications

Zhang

Kong

Zheng

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Varying-parameter recurrent neural network, being a special kind of neural-dynamic methodology, has revealed powerful abilities to handle various time-varying problems, such as quadratic minimization (QM) and quadratic programming (QP) problems. In this paper, a novel power-type varying-parameter recurrent neural network (PT-VP-RNN) is proposed to solve the perturbed time-varying QM and QP problems. First, based on the generalization of time-varying QM and QP problems, the design process of the PT-VP-RNN is presented in detail. Second, the robustness performance of the proposed PT-VP-RNN is theoretically analyzed and proved. What is more, two numerical examples are simulated to illustrate the robustness convergence performance of PT-VP-RNN even in a large disturbance condition. Finally, two practical application examples (i.e., a robot tracking example and a venture investment example) further verify the effectiveness, accuracy, and widespread applicability of the proposed PT-VP-RNN.

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Section: A Robot Tracking Problemmentioning

confidence: 99%

“…To remedy this disadvantage, a zeroing neural network (ZNN) was proposed by Zhang et al [18], Jin et al [19], Xiao [34], and Li et al [35]. Since the derivatives of the error function is taken into consideration, ZNN contains a predictive ability, which can be applied to tracking theoretical solutions.…”

mentioning

confidence: 99%

Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications

Zhang

Kong

Zheng

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

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show abstract

“…It makes it feasible to obtain rapid tracking of reference signals. In, [144] three different RNNs and three different numerical methods was investigated, developed, and compared to solve a repetitive motion planning (RMP) scheme for remedying joint-drift problems of redundant robot manipulators. Three RNNs presented in [144] are recurrent and real time, and they do not need to be trained in advance.…”

Section: Rnn In Robot Autonomymentioning

confidence: 99%

“…In, [144] three different RNNs and three different numerical methods was investigated, developed, and compared to solve a repetitive motion planning (RMP) scheme for remedying joint-drift problems of redundant robot manipulators. Three RNNs presented in [144] are recurrent and real time, and they do not need to be trained in advance. Li et al [145] proposed a novel RNN to handle the redundancy of robots for efficient kinematics problem under the influence of polynomial-type noises.…”

Section: Rnn In Robot Autonomymentioning

confidence: 99%

A Review on Neural Dynamics for Robot Autonomy

Chen

2018

IJRC

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Exploiting neural networks to solve control problems of robots is becoming commonly and effectively in academia and engineering. Due to the remarkable features like distributed storage, parallelism, easy implementation by hardware, adaptive self-learning capability, and free of off-line training, the solutions of neural networks break the bottlenecks of serial-processing strategies and methods, and serve as significant alternatives for robotic engineers and researchers. Especially, various types and branches of recurrent neural networks (RNNs) have been sequentially developed since the seminal works by Hopfield and Tank. Successively, many classes and branches of RNNs such as primal-dual neural networks (PDNNs), zeroing neural networks (ZNNs) and gradient neural networks (GNNs) are proposed, investigated, developed and applied to the robot autonomy. The objective of this paper is to present a comprehensive review of the research on neural networks (especially RNNs) for control problems solving of different kinds of robots. Specifically, the state-of-the-art research of RNNs, PDNNs, ZNNs and GNNs in different robot control problems solving are detailedly revisited and reported. The readers can readily find many effective and valuable solutions on the basis of neural networks for the robot autonomy in this paper.

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“…Guo et al proposed a hybrid redundancy resolution with both weighted velocity and acceleration minimization as co-criteria for robot control [15]. Zhang et al compared three recurrent neural networks and three numerical methods for solving a repetitive motion planning scheme of redundant robot manipulator to remedy joint-drift problems [16]. These works have successfully and efficiently resolved inverse kinematics problems with various performance indices satisfied with efficient optimization formulated.…”

Section: Introductionmentioning

confidence: 99%

A Fault-Tolerant Method for Motion Planning of Industrial Redundant Manipulator

Zhan

et al. 2020

IEEE Trans. Ind. Inf.

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Nowadays industrial redundant manipulators have been playing important roles in manufacturing fields such as welding and assembling by performing repetitive and dull work. Such long-term industrial operations usually require redundant manipulators to keep good working conditions and maintain steadiness of joint actuation. However, some joints of redundant manipulators may fall into fault status after enduring longperiod heavy manipulations, causing that the desired industrial tasks can not be accomplished accurately. In this paper, we propose a novel fault-tolerant method with simultaneous faultdiagnose function for motion planning and control of industrial redundant manipulators. The proposed approach is able to adaptively localize which joints run away from the normal state to be fault, and it can guarantee to finish the desired path tracking control even when these fault joints lose their velocity to actuate. Simulation and experiment results on a Kuka LBR iiwa manipulator demonstrate the efficiency of the proposed faulttolerant method for motion control of the redundant manipulator.

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Three Recurrent Neural Networks and Three Numerical Methods for Solving a Repetitive Motion Planning Scheme of Redundant Robot Manipulators

Cited by 145 publications

References 29 publications

Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications

Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications

A Review on Neural Dynamics for Robot Autonomy

A Fault-Tolerant Method for Motion Planning of Industrial Redundant Manipulator

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