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Soft robots have immense potential given their safer contact with environments, 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 introduce a soft limb with swappable SMA muscles, and deploy the limb in a blocked-force test to quantify the maximum applied force at different temperatures over different cyclic fatigue lifetimes. Then, by applying an invariance-based control system to maintain temperatures under our proposed 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. 

Robots built from soft materials have the potential for intuitively-safer interactions with humans and the environment. However, soft robots’ embodiments have many sources of failure that could lead to unsafe conditions in closed-loop control, such as degradation of sensors or fracture of actuators. This letter proposes a fault detection system for sensors attached to artificial muscle actuators that satisfies a formal safety condition. Our approach combines redundant sensing, model-based state estimation, and Gaussian process regression to determine when one sensor’s reading statistically diverges from another, indicating a fault condition. We apply the approach to electrothermal shape memory alloy (SMA) artificial muscles, demonstrating that our method prevents the overheating and fire damage risk that could otherwise occur. Experiments show that when the muscle’s nominal sensor (temperature via a thermocouple) is fractured from the robot, the redundant sensor (electrical resistance) combined with our method prevents violation of state constraints. Deploying this system in real-world human-robot interaction could help make soft robots more robust and reliable.