寻找高性能软体机器人设计的“MAP”：使用MAP-elites和拓扑优化遍历复杂设计空间
A 'MAP' to find high-performing soft robot designs: Traversing complex design spaces using MAP-elites and Topology Optimization
AI总结：
针对软体机器人缺乏高效设计工具的问题，提出结合拓扑优化与质量多样性优化的分层方法，通过演化设计域内可变空隙生成多样高性能设计，在基准与抓手任务中验证有效，提供新的软硬部件设计框架。
摘要：
软体机器人技术已成为抓取可变形物体的标准解决方案，且在极端环境下的移动机器人探索中被证明极具价值。然而，尽管该领域发展迅速，目前仍没有被广泛采用的计算设计工具，能够生成优质、可制造的设计。为突破启发式生物仿生灵感带来的收益递减瓶颈，该领域需要高效工具来探索软体机器人领域中复杂的非线性设计空间，并找到新颖的高性能设计。在本研究中，我们探究了一种分层设计优化方法，该方法结合拓扑优化与质量多样性优化的优势，通过演化设计域来生成多样化的高性能软体机器人。该方法在设计域内嵌入可变尺寸的空隙区域，并演化其尺寸与位置，以促进对设计空间更丰富的探索，找到一组多样化的高性能软体机器人。我们在基准拓扑优化问题和软体机器人设计问题上验证了该方法的有效性，并表明该方法应用于软体抓手时可提升抓取性能。我们的方法为在复杂设计域中设计软体和刚性部件提供了新框架。
英文摘要：
Soft robotics has emerged as the standard solution for grasping deformable objects, and has proven invaluable for mobile robotic exploration in extreme environments. However, despite this growth, there are no widely adopted computational design tools that produce quality, manufacturable designs. To advance beyond the diminishing returns of heuristic bio-inspiration, the field needs efficient tools to explore the complex, non-linear design spaces present in soft robotics, and find novel high-performing designs. In this work, we investigate a hierarchical design optimization methodology which combines the strengths of topology optimization and quality diversity optimization to generate diverse and high-performance soft robots by evolving the design domain. The method embeds variably sized void regions within the design domain and evolves their size and position, to facilitating a richer exploration of the design space and find a diverse set of high-performing soft robots. We demonstrate its efficacy on both benchmark topology optimization problems and soft robotic design problems, and show the method enhances grasp performance when applied to soft grippers. Our method provides a new framework to design parts in complex design domains, both soft and rigid.
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。
