耗散介质中通过神经力学调节实现鲁棒波动运动
Robust undulatory locomotion via neuromechanical adjustments in a dissipative medium
AI总结：
本文以秀丽隐杆线虫为灵感，建立具有节律神经模式发生器的细长弹性运动体模型，通过庞加莱截面分析证明局部神经力学调节可在耗散介质中产生鲁棒波动运动，并实现前进、后退、转向及复杂行为，为解读动物适应性行为和设计自适应机器人提供方法。
摘要：
耗散环境在自然界中无处不在，从低雷诺数流体中的微观游动者到摩擦介质中的宏观动物。在本研究中，受秀丽隐杆线虫（Caenorhabditis elegans）在游泳和爬行运动中的各种行为启发，我们考虑了一个具有内部节律神经模式发生器的细长弹性运动体数学模型。通过使用庞加莱截面分析该模型的动力系统，我们发现对环境的局部神经力学调节可以产生鲁棒的波动运动。这种渐进行为在广泛的参数区域内表现为全局稳定周期轨道。此外，通过控制机械感知，我们能够设计动力系统以实现前进、后退和转向运动，以及实验观察到的秀丽隐杆线虫（C. elegans）中看似随机、复杂的行为。本研究中发现的机制，连同我们从动力系统视角出发的方法论，有助于解读复杂的动物适应性行为，也有助于为广泛的耗散介质设计自适应机器人。
英文摘要：
Dissipative environments are ubiquitous in nature, from microscopic swimmers in low-Reynolds-number fluids to macroscopic animals in frictional media. In this study, motivated by various behaviours of {\it Caenorhabditis elegans} during swimming and crawling locomotion, we consider a mathematical model of a slender elastic locomotor with an internal rhythmic neural pattern generator. By analysing the dynamical systems of the model using a Poincaré section, we found that local neuromechanical adjustments to the environment can create robust undulatory locomotion. This progressive behaviour emerges as a global stable periodic orbit in a broad range of parameter regions. Further, by controlling the mechanosensation, we were able to design the dynamical systems to manoeuvre with progressive, reverse, and turning motions as well as apparently random, complex behaviours, as experimentally observed in {\it C. elegans}. The mechanisms found in this study, together with our methodologies with the dynamical systems viewpoint, are useful for deciphering complex animal adaptive behaviours and also designing adaptive robots for a wide range of dissipative environments.
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