Refined Self-Motion Scheme With Zero Initial Velocities and Time-Varying Physical Limits via Zhang Neurodynamics Equivalency

Tang, Zanyu; Zhang, Yunong

doi:10.3389/fnbot.2022.945346

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Robots can accurately perform complex tasks and have become a vital driving force in industrial production (Agarwal and Akella, 2024 ). Among industrial robots, redundant robots, equipped with multiple degrees of freedom (DOFs), have gained significant recognition and favor due to their exceptional flexibility and automation capabilities (Tang and Zhang, 2022 ; Zheng et al, 2024 ). Therefore, numerous control schemes are designed to extend the application range of redundant robots, such as medical services (Zeng et al, 2024 ) and visual navigation (Wang et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

A data-driven acceleration-level scheme for image-based visual servoing of manipulators with unknown structure

Wen,

Xie

2024

Front. Neurorobot.

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The research on acceleration-level visual servoing of manipulators is crucial yet insufficient, which restricts the potential application range of visual servoing. To address this issue, this paper proposes a quadratic programming-based acceleration-level image-based visual servoing (AIVS) scheme, which considers joint constraints. Besides, aiming to address the unknown problems in visual servoing systems, a data-driven learning algorithm is proposed to facilitate estimating structural information. Building upon this foundation, a data-driven acceleration-level image-based visual servoing (DAIVS) scheme is proposed, integrating learning and control capabilities. Subsequently, a recurrent neural network (RNN) is developed to tackle the DAIVS scheme, followed by theoretical analyses substantiating its stability. Afterwards, simulations and experiments on a Franka Emika Panda manipulator with eye-in-hand structure and comparisons among the existing methods are provided. The obtained results demonstrate the feasibility and practicality of the proposed schemes and highlight the superior learning and control ability of the proposed RNN. This method is particularly well-suited for visual servoing applications of manipulators with unknown structure.

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Section: Introductionmentioning

confidence: 99%

A data-driven acceleration-level scheme for image-based visual servoing of manipulators with unknown structure

Wen,

Xie

2024

Front. Neurorobot.

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Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix

Li,

Su,

et al. 2024

Front. Neurorobot.

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Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.

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Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency

Tang,

Mao,

Zhang

et al. 2024

Biomimetics

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Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming from angle-layer and velocity-layer constraints to acceleration-layer constraints, five theorems and three corollaries are theoretically derived and rigorously proved. Then, together with the unified acceleration-layer bound constraint, an enhanced acceleration-layer CA scheme specially considering three-layer time-variant physical limits is proposed, and a simplified acceleration-layer CA scheme considering three-layer time-invariant physical limits is also proposed. The proposed CA schemes are finally formulated in the form of standard quadratic programming and are solved by a projection neurodynamics solver. Moreover, comparative simulative experiments based on a four-link planar arm and a UR3 spatial arm are performed to verify the efficacy and superiority of the proposed CA schemes. At last, physical experiments are conducted on a real Kinova Jaco2 arm to substantiate the practicability of the proposed CA schemes.

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Refined Self-Motion Scheme With Zero Initial Velocities and Time-Varying Physical Limits via Zhang Neurodynamics Equivalency

Cited by 4 publications

References 27 publications

A data-driven acceleration-level scheme for image-based visual servoing of manipulators with unknown structure

A data-driven acceleration-level scheme for image-based visual servoing of manipulators with unknown structure

Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix

Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency

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