工业机器人在进行工件抓取过程中，往往存在夹持力过大使工件破损、夹持力过小导致工件滑落的矛盾。为此，提出一种接触滑动的快速检测方法，采用聚偏二氟乙烯(polyvinylidene fluoride, PVDF)压电传感器作为滑觉感知元件。首先，利用阿基米德优化算法(Archimedes Optimization Algorithm, AOA)优化变分模态分解(Variational Mode Decomposition, VMD)对传感器信号进行分解与重构，降低噪声干扰；然后，提取信号的时频域特征，构建信号特征集；最后，使用蜣螂优化算法(Dung Beetle Optimization, DBO)优化选取长短期记忆网络(Long Short-term Memory Networks, LSTM)参数，将DBO优化选取后的参数和信号特征集用于构建滑动检测识别模型。将所提滑动检测方法应用于电动夹爪抓取实验，结果表明，该方法实现了接触状态的精准快速识别，准确率达到100%，识别时间在20ms以内，根据识别结果可实时调整电动夹爪夹持力大小。
Abstract
In the process of grasping workpieces, industrial robots often face the contradiction of excessive gripping force causing damage to the workpiece, and too little gripping force leading to slippage. To address this issue, a rapid sliding detection method is proposed using polyvinylidene fluoride (PVDF) piezoelectric sensors as tactile perception elements. First, the sensor signal is decomposed and reconstructed using the Variational Mode Decomposition (VMD) optimized by the Archimedes Optimization Algorithm (AOA) to reduce noise interference. Next, extract the time-frequency domain features of the signal to construct the signal feature set. Finally, use the Dung Beetle Optimization (DBO) algorithm to optimize the selection of parameters for Long Short-term Memory Networks (LSTM). Apply the optimized parameters obtained from DBO along with the signal feature set to construct the sliding detection recognition model. The proposed sliding detection method was applied to an experiment involving electric gripper grasping. The results demonstrate precise and rapid recognition of contact status, achieving 100% accuracy with recognition times under 20ms. Based on the recognition results, the gripping force of the electric gripper can be adjusted in real-time.
关键词
滑动检测 /
聚偏二氟乙烯 /
阿基米德优化算法 /
变分模态分解 /
长短期记忆网络
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Key words
sliding detection, polyvinylidene fluoride, Archimedes optimization algorithm, variational mode decomposition, long short-term memory network /
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