Abstract
The artificial lateral line (ALL) is a bioinspired flow sensing system for underwater robots, comprising of distributed flow sensors. The ALL has been successfully applied to detect the undulatory flow fields generated by body undulation and tail-flapping of bioinspired robotic fish. However, its feasibility and performance in sensing the undulatory flow fields produced by human leg kicks during swimming has not been systematically tested and studied. This paper presents a novel sensing framework to investigate the undulatory flow field generated by swimmer's leg kicks, leveraging bioinspired ALL sensing. To evaluate the feasibility of using the ALL system for sensing the undulatory flow fields generated by swimmer leg kicks, this paper designs an experimental platform integrating an ALL system and a lab-fabricated human leg model. To enhance the accuracy of flow sensing, this paper proposes a feature extraction method that dynamically fuses time-domain and time-frequency characteristics. Specifically, time-domain features are extracted using one-dimensional convolutional neural networks and bidirectional long short-term memory networks (1DCNN-BiLSTM), while time-frequency features are extracted using short-term Fourier transform and two-dimensional convolutional neural networks (STFT-2DCNN). These features are then dynamically fused based on attention mechanisms to achieve accurate sensing of the undulatory flow field. Furthermore, extensive experiments are conducted to test various scenarios inspired by human swimming, such as leg kick pattern recognition and kicking leg localization, achieving satisfactory results.
Abstract (translated)
人工侧线系统（ALL）是一种仿生流体传感系统，用于水下机器人，由分布式的流动传感器组成。该系统已成功应用于检测由生物启发的机器鱼身体摆动和尾部拍打产生的波动水流场。然而，其在感测游泳时人类腿部踢水所产生的波动水流场方面的可行性和性能尚未经过系统的测试与研究。 本文提出了一种新的传感框架，利用仿生ALL传感技术来探究游泳者腿部踢水所生成的波动水流场。为了评估使用ALL系统感应由游泳者的腿部踢动产生的波动水流场的可能性，本文设计了一个实验平台，该平台集成了ALL系统和实验室制造的人类腿模型。 为提高流体感知的准确性，本研究提出了一种基于注意力机制动态融合时域与时频特征的提取方法。具体而言，时间领域的特性通过一维卷积神经网络（1DCNN）与双向长短时记忆网络（BiLSTM）进行抽取；而时间-频率特性则通过短时傅里叶变换（STFT）及二维卷积神经网络（2DCNN）来抽取。这些特征随后基于注意力机制被动态融合，以实现对波动水流场的准确感知。 此外，本文还进行了广泛的实验，测试了由人类游泳启发的各种场景下的性能，如踢腿模式识别和踢动腿部定位等任务，并取得了令人满意的结果。
URL
https://arxiv.org/abs/2503.07312
