Repetitive motion planning of PA10 robot arm subject to joint physical limits and using LVI-based primal–dual neural network

Zhang, Yunong; Lv, Xudong; Li, Zhonghua; Yang, Zhi; Chen, Ke

doi:10.1016/j.mechatronics.2008.04.005

Cited by 77 publications

(52 citation statements)

References 17 publications

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“…In addition, to simplify the QP solver, we use an appropriate treatment to cancel the dual decision variable for bound constraint (35). This means that we only need to define the dual decision vector y ∈ R 2m for equality constraint (34 [30]. That is, to find a primal-dual equilibrium vector…”

Section: B Recurrent Neural Network Solvermentioning

confidence: 99%

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Zhang

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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126

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We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

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Section: B Recurrent Neural Network Solvermentioning

confidence: 99%

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Zhang

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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126

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“…(24) and (25)). In addition, to reduce the QP-solver complexity, we can use an elegant treatment to cancel the dual decision vector for the bound constraint (25) (Zhang, Lv, Li, Yang, & Chen, 2008). That is, we only need to define the corresponding dual decision variable vector y ∈ R m for the equality constraint (24).…”

Section: 'Bridge' Theorems and Numerical Algorithmmentioning

confidence: 99%

“…It can be generalised from Zhang et al (2008), and references therein, by using the Lagrangian multipliers.…”

Section: 'Bridge' Theorems and Numerical Algorithmmentioning

confidence: 99%

Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

Xiao

Zhang

2014

International Journal of Systems Science

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For avoiding obstacles and joint physical constraints of robot manipulators, this paper proposes and investigates a novel obstacle avoidance scheme (termed the acceleration-level obstacle-avoidance scheme). The scheme is based on a new obstacle-avoidance criterion that is designed by using the gradient neural network approach for the first time. In addition, joint physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are incorporated into such a scheme, which is further reformulated as a quadratic programming (QP). Two important 'bridge' theorems are established so that such a QP can be converted equivalently to a linear variational inequality and then equivalently to a piecewise-linear projection equation (PLPE). A numerical algorithm based on a PLPE is thus developed and applied for an online solution of the resultant QP. Four path-tracking tasks based on the PA10 robot in the presence of point and windowshaped obstacles demonstrate and verify the effectiveness and accuracy of the acceleration-level obstacle-avoidance scheme. Besides, the comparisons between the non-obstacle-avoidance and obstacle-avoidance results further validate the superiority of the proposed scheme.

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“…Numerous applications in mathematics and engineering fields are closely related to online solution of the Moore-Penrose inverse (also termed pseudo inverse), e.g., signal processing [1], robotics [2][3][4][5][6][7][8], and control theory [9,10]. In mathematics, given any matrix A ∈ R m × n , the Moore-Penrose inverse A + is unique, and can be derived from the following four matrix equations [11]:…”

Section: Introductionmentioning

confidence: 99%

Improved recurrent neural networks for online solution of Moore‐Penrose inverse applied to redundant manipulator kinematic control

Tan

Chen

et al. 2018

Asian Journal of Control

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In this paper, two novel neural networks (NNNs), namely NNN-L and NNN-R neural models, are proposed to online left and right Moore-Penrose inversion. As compared to GNN (gradient neural network) and the recently proposed ZNN (Zhang neural network) for the left or right Moore-Penrose inverse solving, our models are theoretically proven to possess superior global convergence performance. More importantly, the proposed NNN-R model is successfully applied to path-tracking control of a three-link planar robot manipulator. Illustrative examples well validate the theoretical analyses as well as demonstrate the feasibility of the proposed models, which are adopted and verified their effectiveness in kinematic control of a redundant manipulator, for real-time Moore-Penrose inverse solving.

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Repetitive motion planning of PA10 robot arm subject to joint physical limits and using LVI-based primal–dual neural network

Cited by 77 publications

References 17 publications

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

Improved recurrent neural networks for online solution of Moore‐Penrose inverse applied to redundant manipulator kinematic control

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