This paper introduces a computational approach for routing thin artificial muscle actuators through hyperelastic soft robots, in order to achieve a desired deformation behavior. Provided with a robot design, and a set of example deformations, we continuously co-optimize the routing of actuators, and their actuation, to approximate example deformations as closely as possible. We introduce a data-driven model for McKibben muscles, modeling their contraction behavior when embedded in a silicone elastomer matrix. To enable the automated routing, a differentiable hyperelastic material simulation is presented. Because standard finite elements are not differentiable at element boundaries, we implement a Moving Least Squares formulation, making the deformation gradient twice-differentiable. Our robots are fabricated in a two-step molding process, with the complex mold design steps automated. While most soft robotic designs utilize bending, we study the use of our technique in approximating twisting deformations on a bar example. To demonstrate the efficacy of our technique in soft robotic design, we show a continuum robot, a tentacle, and a 4-legged walking robot.

中文翻译：

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用于软机器人的肌肉纤维的自动布线

本文介绍了一种通过超弹性软机器人路由薄人工肌肉执行器的计算方法，以实现所需的变形行为。提供机器人设计和一组示例变形，我们不断共同优化执行器的路由及其驱动，以尽可能接近示例变形。我们为 McKibben 肌肉引入了一个数据驱动模型，模拟它们嵌入有机硅弹性体矩阵时的收缩行为。为了实现自动布线，提出了可微的超弹性材料模拟。由于标准有限元在单元边界处不可微，我们实现了移动最小二乘公式，使变形梯度可微分。我们的机器人采用两步成型工艺制造，复杂的模具设计步骤自动化。虽然大多数软机器人设计都利用弯曲，但我们研究了使用我们的技术来近似棒材示例上的扭曲变形。为了证明我们的技术在软机器人设计中的有效性，我们展示了一个连续体机器人、一个触手和一个 4 腿步行机器人。