模拟鳟鱼游泳，揭示肌肉激活如何提升推进效率
How do trout regulate patterns of muscle contraction to optimize propulsive efficiency during steady swimming
- 用深度强化学习控制数字鳟鱼的肌肉激活时序
- 肌肉激活跨度超0.5个体长可稳定波形传播，降低能耗
- 适合研究仿生机器人与水下推进系统设计
理解高效鱼类运动对生物力学、流体动力学及工程应用具有重要意义。传统研究常忽视神经肌肉控制与整体运动的关联。为探究车鳍型游泳中的能量传递机制，我们构建了一个仿生数字鳟鱼模型，结合多体动力学、Hill型肌肉建模和高保真流固耦合算法，精确复现真实鳟鱼的形态与物理特性。通过深度强化学习，该模型实现了肌肉激活的分层时空控制。系统分析了不同激活策略对速度与能耗的影响。结果表明，轴向肌节耦合（激活跨度超过0.5个体长）对稳定身体波传播至关重要；适度的肌肉收缩时长（尾拍周期的[0.1,0.3]区间）使身体与流体形成被动阻尼系统，显著降低能量消耗。此外，肌节激活相位滞后会影响身体波形；若过大则引发拮抗收缩，阻碍推力生成。这些发现深化了对仿生运动的理解，有助于设计更节能的水下系统。
原文摘要 · Abstract (English)
Understanding efficient fish locomotion offers insights for biomechanics, fluid dynamics, and engineering. Traditional studies often miss the link between neuromuscular control and whole-body movement. To explore energy transfer in carangiform swimming, we created a bio-inspired digital trout. This model combined multibody dynamics, Hill-type muscle modeling, and a high-fidelity fluid-structure interaction algorithm, accurately replicating a real trout's form and properties. Using deep reinforcement learning, the trout's neural system achieved hierarchical spatiotemporal control of muscle activation. We systematically examined how activation strategies affect speed and energy use. Results show that axial myomere coupling-with activation spanning over 0.5 body lengths-is crucial for stable body wave propagation. Moderate muscle contraction duration ([0.1,0.3] of a tail-beat cycle) lets the body and fluid act as a passive damping system, cutting energy use. Additionally, the activation phase lag of myomeres shapes the body wave; if too large, it causes antagonistic contractions that hinder thrust. These findings advance bio-inspired locomotion understanding and aid energy-efficient underwater system design.
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