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Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast realtime trajectory generation approach for soft robot manipulators, which creates dynamically-feasible motions for arbitrary kinematically-feasible paths of the robot’s end effector. Our insight is that piecewise constant curvature (PCC) dynamics models of soft robots can be differentially flat, therefore control inputs can be calculated algebraically rather than through a nonlinear differential equation. We prove this flatness under certain conditions, with the curvatures of the robot as the flat outputs. Our two-step trajectory generation approach uses an inverse kinematics procedure to calculate a motion plan of robot curvatures per end-effector position, then, our flatness diffeomorphism generates corresponding control inputs that respect velocity. We validate our approach through simulations of our representative soft robot manipulator along three different trajectories, demonstrating a margin of 23x faster than realtime at a frequency of 100 Hz. This approach could allow fast verifiable replanning of soft robots’ motions in safety-critical physical environments, crucial for deployment in the real world. 

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. 

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.