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Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.

 

Modular soft robots combine the strengths of two traditionally separate areas of robotics. As modular robots, they can show robustness to individual failure and reconfigurability; as soft robots, they can deform and undergo large shape changes in order to adapt to their environment, and have inherent human safety. However, for sensing and communication these robots also combine the challenges of both: they require solutions that are scalable (low cost and complexity) and efficient (low power) to enable collectives of large numbers of robots, and these solutions must also be able to interface with the high extension ratio elastic bodies of soft robots. In this work, we seek to address these challenges using acoustic signals produced by piezoelectric surface transducers that are cheap, simple, and low power, and that not only integrate with but also leverage the elastic robot skins for signal transmission. Importantly, to further increase scalability, the transducers exhibit multi-functionality made possible by a relatively flat frequency response across the audible and ultrasonic ranges. With minimal hardware, they enable directional contact-based communication, audible-range communication at a distance, and exteroceptive sensing. We demonstrate a subset of the decentralized collective behaviors that these functions make possible with multi-robot hardware implementations. The use of acoustic waves in this domain is shown to provide distinct advantages over existing solutions. 

Continuum robots have high degrees of freedom and the ability to safely move in constrained environments. One class of soft continuum robot is the “vine” robot. This type of robot extends from its tip by everting or unfurling new material, driven by internal body pressure. Most vine robot examples store new body material in a reel at their base, passing it through the core of the robot to the tip, and like many continuum robots, steer by selectively lengthening or shortening one side of the body. While this approach to steering and material storage lends itself to a fully soft device, it has three key limitations: (i) internal friction of material passing through the core of the robot limits its length in tortuous paths, (ii) body buckling as the robot's body material is re-spooled at the base can prevent retraction, and (iii) constant curvature steering limits the robot's poses and object approach angles in a given workspace. This letter presents a hybrid soft-rigid robotic system comprising a soft vine robot body and a rigid, mobile, internal steering-reeling mechanism (SRM); this SRM is equipped with a reel for material storage, a bending actuator for steering, and is capable of actuating the robot at any point along its length. This hybrid configuration increases reach along tortuous paths, allows retraction, and increases the workspace. We describe the motivation for the device, generate its mathematical models, present its methods of operation, and verify experimentally the models we developed and the performance improvements over previous vine robots. 

Navigation and motion control of a robot to a destination are tasks that have historically been performed with the assumption that contact with the environment is harmful. This makes sense for rigid-bodied robots, where obstacle collisions are fundamentally dangerous. However, because many soft robots have bodies that are low-inertia and compliant, obstacle contact is inherently safe. As a result, constraining paths of the robot to not interact with the environment is not necessary and may be limiting. In this article, we mathematically formalize interactions of a soft growing robot with a planar environment in an empirical kinematic model. Using this interaction model, we develop a method to plan paths for the robot to a destination. Rather than avoiding contact with the environment, the planner exploits obstacle contact when beneficial for navigation. We find that a planner that takes into account and capitalizes on environmental contact produces paths that are more robust to uncertainty than a planner that avoids all obstacle contact.