最快者生存——光驱动水下微型机器人的算法引导进化
Survival of the fastest -- algorithm-guided evolution of light-powered underwater microrobots
AI总结：
本研究利用粒子群优化和遗传算法，以实测游泳速度为适应度函数，优化光驱动毫米级水下软体游泳机器人，发现意外基因组合可使速度提升八倍并发现新运动模式。
摘要：
软体机器人的运动模式取决于多个参数，难以进行数值建模。因此，提升其性能十分复杂，尤其是在低雷诺数且多种空气与流体动力学过程共同影响运动的小尺度系统中。本研究通过将实验结果（即测量的游泳速度）作为适应度函数应用于两种进化算法：粒子群优化和遗传算法，来优化光驱动的毫米级水下游泳机器人运动。由于这些具有不同特征（表型）的光驱动软体机器人能够快速制造，它们为优化实验提供了绝佳平台，可利用众多相互竞争的机器人在连续世代中提升游泳速度。有趣的是，正如自然进化中一样，意想不到的基因组合带来了惊人的结果，包括速度提升八倍，或发现了一种自振荡水下运动模式。
英文摘要：
Depending on multiple parameters, soft robots can exhibit different modes of locomotion that are difficult to model numerically. As a result, improving their performance is complex, especially in small-scale systems characterized by low Reynolds numbers, when multiple aero- and hydrodynamical processes influence their movement. In this work, we optimize light-powered millimetre-scale underwater swimmer locomotion by applying experimental results - measured swimming speed - as the fitness function in two evolutionary algorithms: particle swarm optimization and genetic algorithm. As these soft, light-powered robots with different characteristics (phenotypes) can be fabricated quickly, they provide a great platform for optimisation experiments, using many competing robots to improve swimming speed over consecutive generations. Interestingly, just like in natural evolution, unexpected gene combinations led to surprisingly good results, including eight-fold increase in speed or the discovery of a self-oscillating underwater locomotion mode.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
