Soft robots have immense potential given their inherent safety and adaptability, but challenges in soft actuator forces and design constraints have limited scaling up soft robots to larger sizes. Electrothermal shape memory alloy (SMA) artificial muscles have the potential to create these large forces and high displacements, but consistently using these muscles under a well-defined model, in-situ in a soft robot, remains an open challenge. This article provides a system for maintaining the highest-possible consistent SMA forces, over long lifetimes, by combining a fatigue testing protocol with a supervisory control system for the muscles' internal temperature state. We propose a design of a soft limb with swap-able SMA muscles, and deploy the limb in a blocked-force test to quantify the relationship between the measured maximum force at different temperatures over different lifetimes. Then, by applying an invariance-based control system to maintain temperatures under our long-life limit, we demonstrate consistent high forces in a practical task over hundreds of cycles. The method we developed allows for practical implementation of SMAs in soft robots through characterizing and controlling their behavior in-situ, and provides a method to impose limits that maximize their consistent, repeatable behavior.

翻译：软体机器人因其固有的安全性和适应性而具有巨大潜力，但软体执行器的力输出不足及设计约束限制了其向更大尺寸的扩展。电热形状记忆合金人工肌肉能够产生高力输出和大位移，然而，如何在明确定义的模型下，于软体机器人原位环境中持续使用这些肌肉仍是一个开放挑战。本文提出一种系统，通过将疲劳测试协议与肌肉内部温度状态的监督控制系统相结合，在长寿命周期内维持尽可能高的一致性形状记忆合金力输出。我们设计了一种可更换形状记忆合金肌肉的软体肢体，并在阻塞力测试中部署该肢体，以量化不同温度下不同寿命周期内测得的最大力之间的关系。随后，通过应用基于不变量的控制系统将温度维持在长寿命极限以下，我们证明该方法可在数百个周期内实现实际任务中的一致性高力输出。所开发的方法通过原位表征和控制行为，为形状记忆合金在软体机器人中的实际应用提供了途径，并确立了一种施加极限以最大化其一致性与可重复行为的方法。