Design and Human-Robot Coupling Performance Analysis of Flexible Ankle Rehabilitation Robot

Zeng, Daxing; Liu, Ya; Qu, Chongrui; Cong, Jianhua; Hou, Yulei; Lu, Wenjuan

doi:10.1109/lra.2023.3330052

Cited by 5 publications

(1 citation statement)

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“…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 ). Furthermore, in these application scenarios, information on the external environment and the robot's status is acquired from various sensors, especially for the image capture of visual information (Jin 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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