Bodaghi Dariush, Wang Yuxing, Liu Geng, Liu Dongfang, Xue Qian, Zheng Xudong
Department of Mechanical Engineering, University of Maine, Orono, ME, United States.
Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States.
Front Robot AI. 2023 Aug 3;10:1231715. doi: 10.3389/frobt.2023.1231715. eCollection 2023.
This study presents a novel method that combines a computational fluid-structure interaction model with an interpretable deep-learning model to explore the fundamental mechanisms of seal whisker sensing. By establishing connections between crucial signal patterns, flow characteristics, and attributes of upstream obstacles, the method has the potential to enhance our understanding of the intricate sensing mechanisms. The effectiveness of the method is demonstrated through its accurate prediction of the location and orientation of a circular plate placed in front of seal whisker arrays. The model also generates temporal and spatial importance values of the signals, enabling the identification of significant temporal-spatial signal patterns crucial for the network's predictions. These signal patterns are further correlated with flow structures, allowing for the identification of important flow features relevant for accurate prediction. The study provides insights into seal whiskers' perception of complex underwater environments, inspiring advancements in underwater sensing technologies.
本研究提出了一种新颖的方法，该方法将计算流体-结构相互作用模型与可解释的深度学习模型相结合，以探索海豹胡须传感的基本机制。通过在关键信号模式、流动特性和上游障碍物属性之间建立联系，该方法有可能加深我们对复杂传感机制的理解。通过准确预测放置在海豹胡须阵列前方的圆形板的位置和方向，证明了该方法的有效性。该模型还生成信号的时间和空间重要性值，从而能够识别对网络预测至关重要的显著时空信号模式。这些信号模式进一步与流动结构相关联，从而能够识别与准确预测相关的重要流动特征。该研究为海豹胡须对复杂水下环境的感知提供了见解，激发了水下传感技术的进步。
