Climate change, invasive species and human activities are currently damaging the world's coral reefs at unprecedented rates, threatening their vast biodiversity and fisheries, and reducing coastal protection. Solving this vast challenge requires scalable coral regeneration technologies that can breed climate-resilient species and accelerate the natural regrowth processes; actions that are impeded by the absence of safe and robust tools to handle the fragile coral. We investigate ReefFlex, a generative soft finger design methodology that explores a diverse space of soft fingers to produce a set of candidates capable of safely grasping fragile and geometrically heterogeneous coral in a cluttered environment. Our key insight is encoding heterogeneous grasping into a reduced set of motion primitives, creating a simplified, tractable multi-objective optimisation problem. To evaluate the method, we design a soft robot for reef rehabilitation, which grows and manipulates coral in onshore aquaculture facilities for future reef out-planting. We demonstrate ReefFlex increases both grasp success and grasp quality (disturbance resistance, positioning accuracy) and reduces in adverse events encountered during coral manipulation compared to reference designs. ReefFlex, offers a generalisable method to design soft end-effectors for complex handling and paves a pathway towards automation in previously unachievable domains like coral handling for restoration.

翻译：气候变化、入侵物种及人类活动正以前所未有的速度破坏全球珊瑚礁，威胁其巨大的生物多样性与渔业资源，并削弱海岸防护能力。应对这一重大挑战需要可扩展的珊瑚再生技术，以培育气候适应型物种并加速自然再生过程；然而，由于缺乏安全可靠的工具来处理脆弱的珊瑚，这些行动受到阻碍。本研究提出ReefFlex——一种生成式软体手指设计方法，该方法通过探索多样化的软体手指设计空间，生成一组能够在杂乱环境中安全抓取几何异构脆弱珊瑚的候选方案。我们的核心思路是将异构抓取任务编码为精简的运动基元集合，从而构建简化的、可处理的多目标优化问题。为验证该方法，我们设计了一款用于珊瑚礁修复的软体机器人，该机器人在陆基水产养殖设施中培育并操控珊瑚，为后续的珊瑚礁移植做准备。实验表明，相较于基准设计，ReefFlex在珊瑚操控过程中不仅提升了抓取成功率与抓取质量（抗干扰能力、定位精度），同时减少了不良事件的发生。ReefFlex为复杂操控任务中的软体末端执行器设计提供了通用方法，并为珊瑚修复等以往难以实现自动化操作的领域开辟了技术路径。