N珠泳者中分散决策的神经进化带来可扩展且鲁棒的集体运动
Neuroevolution of Decentralized Decision-Making in N-Bead Swimmers Leads to Scalable and Robust Collective Locomotion
AI总结：
该研究利用神经进化优化N珠泳者各身体部位的分散决策，揭示可扩展、抗形态缺陷且可直接用于人工微型泳者的高效集体运动策略。
摘要：
许多微生物通过由分子马达在局部发起的较大非互易形状变形来游动。然而，分散式形状控制如何决定整个生物体的运动仍不清楚。在此，我们利用神经进化技术，研究高效运动如何从身体各部分协调但简单且分散的决策中涌现。我们的方法使我们能够研究日益增大的微型泳者身体的最优运动策略，并涌现出长波长的身体形状变形，对应于令人惊讶的高效游动步态。所获得的分散式策略对形态变化或缺陷具有鲁棒性和容忍度，并且可以“开箱即用”地应用于人工微型泳者，以进行货物运输或药物递送应用，而无需进一步优化。我们的工作与理解和开发生物及人工微型泳者的鲁棒导航策略相关；在更广泛的背景下，也与理解人工生命中个体性的涌现层次和集体智能的作用相关。
英文摘要：
Many microorganisms swim by performing larger non-reciprocal shape deformations that are initiated locally by molecular motors. However, it remains unclear how decentralized shape control determines the movement of the entire organism. Here, we investigate how efficient locomotion emerges from coordinated yet simple and decentralized decision-making of the body parts using neuroevolution techniques. Our approach allows us to investigate optimal locomotion policies for increasingly large microswimmer bodies, with emerging long-wavelength body shape deformations corresponding to surprisingly efficient swimming gaits. The obtained decentralized policies are robust and tolerant concerning morphological changes or defects and can be applied to artificial microswimmers for cargo transport or drug delivery applications without further optimization "out of the box". Our work is of relevance to understanding and developing robust navigation strategies of biological and artificial microswimmers and, in a broader context, for understanding emergent levels of individuality and the role of collective intelligence in Artificial Life.
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