Data-Driven Modeling and High-Precision Tracking Control of a Soft Continuum Manipulator: Enabling Robotic Sorting of Multiwire Cables

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-09-09 DOI:10.1002/aisy.202300827
Yuan Gao, Zhi Chen, Fangxun Zhong, Xiang Li, Yun-Hui Liu
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Abstract

As a new class of robots, soft continuum manipulators have attracted attention due to their flexibility and compliance. However, these characteristics create challenges for precise modeling and control. This study proposes a hybrid offline and online data-driven scheme to achieve high-precision tracking control of a soft continuum manipulator. First, a novel multiscale deep neural network learns the manipulator model offline. Specifically, the feature fusion module extracts highly discriminative features and captures long-term dependencies from the temporal trajectory data. The self-attention module strengthens the ability to represent fusion features and enhances the model prediction accuracy. Then, the learnt model is updated using multisensor data online, and the proposed controller further compensates for the updated model and enhances the tracking accuracy in the movement stage. Finally, the experimental results demonstrate a significant improvement in motion accuracy under different trajectory-tracking scenarios (i.e., deviations of <1 mm in position and <0.8° in orientation). The example of the multiwire cable sorting proves the feasibility of the proposed scheme in high-precision industrial applications.

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软连续机械手的数据驱动建模和高精度跟踪控制:实现多线电缆的机器人分类
作为一类新型机器人,软连续机械手因其灵活性和顺应性而备受关注。然而,这些特性给精确建模和控制带来了挑战。本研究提出了一种离线和在线数据驱动的混合方案,以实现对软连续机械手的高精度跟踪控制。首先,新型多尺度深度神经网络离线学习机械手模型。具体来说,特征融合模块从时序轨迹数据中提取高区分度特征并捕捉长期依赖关系。自我关注模块加强了表示融合特征的能力,提高了模型预测的准确性。然后,利用多传感器数据对学习到的模型进行在线更新,提议的控制器进一步对更新后的模型进行补偿,并提高运动阶段的跟踪精度。最后,实验结果表明,在不同的轨迹跟踪情况下(即位置偏差为 1 毫米,方向偏差为 0.8°),运动精度都有显著提高。多线电缆分拣的实例证明了所提方案在高精度工业应用中的可行性。
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CiteScore
1.30
自引率
0.00%
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0
审稿时长
4 weeks
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