多形状机器人板材变刚度纤维花纹的演化

Atoosa Parsa, M. Goyal, Maggy Lambo, Bilige Yang, J. Bongard, Rebecca Kramer‐Bottiglio
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引用次数: 0

摘要

薄的平面薄片可以通过编程通过拉伸和面外弯曲变成复杂的形状,适用于变形的软机器人。制造变形片的一种方法是使用可变刚度纤维,这种纤维可以调节附着在体积膨胀片表面的拉伸刚度。在板材膨胀过程中,通过拉伸纤维干扰调节局部刚度,可以控制局部形状张量。然而,找到光纤的位置和干扰策略以实现一组所需的形状是一个非平凡的反设计问题。我们提出了一种使用进化算法的加性逆设计框架,以找到匹配多个目标形状的最佳干扰光纤模式。我们用两种曲率对来演示优化管道的效用:1)圆柱曲率和球面曲率,2)简单鞍形曲率和猴形曲率。我们的方法能够在这两种情况下找到一组不同的充分解。通过将硬件约束纳入我们的优化管道,我们进一步探索从模拟到现实的进化解决方案的转移。
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Evolving variable stiffness fiber patterns for multi-shape robotic sheets
Thin, planar sheets can be programmed to morph into complex shapes through stretching and out-of-plane bending, with applicability to shape-shifting soft robots. One way to make a morphing sheet is to use variable stiffness fibers that can modulate their tensile stiffness attached to the surface of a volumetrically expanding sheet. Adjusting local stiffnesses via tensile fiber jamming during sheet expansion allows control of the local shape tensor. However, finding the fiber placements and jamming policies to achieve a set of desired shapes is a non-trivial inverse design problem. We present an additive inverse design framework using an evolutionary algorithm to find optimal jamming fiber patterns to match multiple target shapes. We demonstrate the utility of our optimization pipeline with two input curvature pairs: 1) cylinder and sphere curvatures and 2) simple saddle and monkey saddle curvatures. Our method is able to find a diverse set of sufficient solutions in both cases. By incorporating hardware constraints into our optimization pipeline, we further explore the transfer of evolved solutions from simulation to reality.
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