While undulatory swimming of elongate limbless robots has been extensively studied in open hydrodynamic environments, less research has been focused on limbless locomotion in complex, cluttered aquatic environments. Motivated by the concept of mechanical intelligence, where controls for obstacle navigation can be offloaded to passive body mechanics in terrestrial limbless locomotion, we hypothesize that principles of mechanical intelligence can be extended to cluttered hydrodynamic regimes. To test this, we developed an untethered limbless robot capable of undulatory swimming on water surfaces, utilizing a bilateral cable-driven mechanism inspired by organismal muscle actuation morphology to achieve programmable anisotropic body compliance. We demonstrated through robophysical experiments that, similar to terrestrial locomotion, an appropriate level of body compliance can facilitate emergent swim through complex hydrodynamic environments under pure open-loop control. Moreover, we found that swimming performance depends on undulation frequency, with effective locomotion achieved only within a specific frequency range. This contrasts with highly damped terrestrial regimes, where inertial effects can often be neglected. Further, to enhance performance and address the challenges posed by nondeterministic obstacle distributions, we incorporated computational intelligence by developing a real-time body compliance tuning controller based on cable tension feedback. This controller improves the robot's robustness and overall speed in heterogeneous hydrodynamic environments.

翻译：尽管细长无肢机器人的波动游动在开放流体环境中已被广泛研究，但针对复杂、杂乱水生环境中无肢运动的研究相对较少。受机械智能概念的启发——在陆地无肢运动中，避障控制可卸载至被动体态力学——我们假设机械智能的原理可扩展至杂乱的流体动力学领域。为验证此假设，我们开发了一种无缆无肢机器人，能在水面进行波动游动。该机器人采用受生物肌肉驱动形态启发的双边缆绳驱动机制，实现了可编程的各向异性身体柔顺性。通过机器人物理实验，我们证明与陆地运动类似，适当的身体柔顺性可在纯开环控制下，促进机器人在复杂流体环境中实现涌现式游动。此外，我们发现游动性能取决于波动频率，有效的运动仅在一个特定频率范围内实现。这与高阻尼的陆地环境形成对比，后者通常可忽略惯性效应。为进一步提升性能并应对非确定性障碍物分布带来的挑战，我们引入了计算智能，开发了一种基于缆绳张力反馈的实时身体柔顺性调节控制器。该控制器增强了机器人在异质流体环境中的鲁棒性和整体速度。