He Shan, Musgrave Patrick
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, USA.
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, USA.
Neural Netw. 2025 Jan;181:106766. doi: 10.1016/j.neunet.2024.106766. Epub 2024 Sep 26.
Bio-inspired Autonomous Underwater Vehicles with soft bodies provide significant performance benefits over conventional propeller-driven vehicles; however, it is difficult to control these vehicles due to their soft underactuated bodies. This study investigates the application of Physical Reservoir Computing (PRC) in the swimmer's flexible body to perform state estimation. This PRC informed state estimation has potential to be used in vehicle control. PRC is a type of recurrent neural network that leverages the nonlinear dynamics of a physical system to predict a nonlinear spatiotemporal input-output relationship. By embodying the neural network into the physical structure, PRC can process the response to an environment input with high computational efficiency. This study uses a soft bio-inspired propulsor embodied as a physical reservoir. We evaluate its ability to predict different state estimation tasks including hydrodynamic forces and benchmark computational tasks in response to the forcing applied to the artificial muscles during actuation. The propulsor's nonlinear fluid-structural dynamics act as the physical reservoir and the kinematic feedback serves as the reservoir readouts. We show that the bio-inspired underwater propulsor can predict the hydrodynamic thrust and benchmark tasks with high accuracy under specific input frequencies. By analyzing the frequency spectrum of the input, readouts, and target signals, we demonstrate that the system's dynamic response determines the frequency contents relevant to the task being predicted. The propulsor's ability to process information stems from its nonlinearity, as it is responsible to transform the input signal into a broader spectrum of frequency content at the readouts. This broad band of frequency content is necessary to recreate the target signal within the PRC algorithm, thereby improving the prediction performance. The spectral analysis provides a unique perspective to analyze the nonlinear dynamics of a physical reservoir and serves as a valuable tool for examining other types of vibratory systems for PRC. This work serves as a first step towards embodying computation into soft bio-inspired swimmers.
具有柔软身体的仿生自主水下航行器相较于传统螺旋桨驱动的航行器具有显著的性能优势；然而，由于其柔软的欠驱动身体，控制这些航行器较为困难。本研究探讨了物理水库计算（PRC）在游泳者柔性身体中的应用，以进行状态估计。这种基于PRC的状态估计有潜力用于航行器控制。PRC是一种递归神经网络，它利用物理系统的非线性动力学来预测非线性时空输入-输出关系。通过将神经网络体现在物理结构中，PRC能够以高计算效率处理对环境输入的响应。本研究使用一种作为物理水库的柔软仿生推进器。我们评估其预测不同状态估计任务的能力，包括水动力以及在驱动过程中响应施加于人工肌肉的力时的基准计算任务。推进器的非线性流固动力学充当物理水库，运动反馈作为水库读数。我们表明，这种仿生水下推进器在特定输入频率下能够高精度地预测水动力推力和基准任务。通过分析输入、读数和目标信号的频谱，我们证明系统的动态响应决定了与被预测任务相关的频率成分。推进器处理信息的能力源于其非线性，因为它负责将输入信号在读数处转换为更广泛的频率成分谱。这个宽频带的频率成分对于在PRC算法中重建目标信号是必要的，从而提高预测性能。频谱分析为分析物理水库的非线性动力学提供了独特视角，并作为检查用于PRC的其他类型振动系统的有价值工具。这项工作是将计算体现在柔软仿生游泳者中的第一步。
