To maintain full autonomy, autonomous robotic systems must have the ability to self-repair. Self-repairing via compensatory mechanisms appears in nature: for example, some fish can lose even 76% of their propulsive surface without loss of thrust by altering stroke mechanics. However, direct transference of these alterations from an organism to a robotic flapping propulsor may not be optimal due to irrelevant evolutionary pressures. We instead seek to determine what alterations to stroke mechanics are optimal for a damaged robotic system via artificial evolution. To determine whether natural and machine-learned optima differ, we employ a cyber-physical system using a Covariance Matrix Adaptation Evolutionary Strategy to seek the most efficient trajectory for a given force. We implement an online optimization with hardware-in-the-loop, performing experimental function evaluations with an actuated flexible flat plate. To recoup thrust production following partial amputation, the most efficient learned strategy was to increase amplitude, increase frequency, increase the amplitude of angle of attack, and phase shift the angle of attack by approximately 110 degrees. In fish, only an amplitude increase is reported by majority in the literature. To recoup side-force production, a more challenging optimization landscape is encountered. Nesting of optimal angle of attack traces is found in the resultant-based reference frame, but no clear trend in amplitude or frequency are exhibited -- in contrast to the increase in frequency reported in insect literature. These results suggest that how mechanical flapping propulsors most efficiently adjust to damage of a flapping propulsor may not align with natural swimmers and flyers.

翻译：为保持完全自主性，自主机器人系统必须具备自我修复能力。自然界中存在通过补偿机制实现自我修复的范例：例如某些鱼类在损失高达76%的推进表面积后，仍能通过改变扑翼运动学特征维持推力输出。然而，由于生物体演化过程中存在非适应性进化压力，直接将生物体的运动调整策略移植至机器人扑翼推进器可能并非最优方案。本研究转而通过人工进化方法，确定受损机器人系统的最优扑翼运动学调整方案。为探究自然进化与机器学习所得最优策略的差异性，我们采用协方差矩阵自适应进化策略构建信息物理系统，在给定作用力条件下搜索最高效的运动轨迹。通过硬件在环在线优化方法，利用主动式柔性平板执行器完成实验性功能评估。研究发现，在部分截肢后恢复推力输出的过程中，最高效的学习策略包括：增大振幅、提升频率、增加攻角幅值，并将攻角相位偏移约110度。而鱼类文献中多数仅报道了振幅增大策略。在恢复侧向力输出的研究中，优化景观呈现更高复杂性。尽管在基于合成参考系中发现了最优攻角轨迹的嵌套模式，但振幅与频率均未呈现明显变化趋势——这与昆虫文献中频率增大的报道形成对比。研究结果表明，机械扑翼推进器针对损伤的最优效率调整策略，可能与自然水生/飞行生物的策略存在差异。