Leveraging the elastic bodies of soft robots promises to enable the execution of dynamic motions as well as compliant and safe interaction with an unstructured environment. However, the exploitation of these abilities is constrained by the lack of appropriate control strategies. This work tackles for the first time the development of closed-loop dynamic controllers for a continuous soft robot. We present two architectures designed for dynamic trajectory tracking and surface following, respectively. Both controllers are designed to preserve the natural softness of the robot and adapt to interactions with an unstructured environment. The validity of the controllers is proven analytically within the hypotheses of the model. The controllers are evaluated through an extensive series of simulations, and through experiments on a physical soft robot capable of planar motions.
Despite the emergence of many soft-bodied robotic systems, model-based feedback control for soft robots has remained an open challenge. This is largely due to the intrinsic difficulties in designing controllers for systems with infinite dimensions. This work extends our previously proposed formulation for the dynamics of a soft robot from two to three dimensions. The formulation connects the soft robot's dynamic behavior to a rigid-bodied robot with parallel elastic actuation. The matching between the two systems is exact under the hypothesis of Piecewise Constant Curvature. Based on this connection, we introduce a control architecture with the aim of achieving accurate curvature and bending control. This controller accounts for the natural softness of the system moving in three dimensions, and for the dynamic forces acting on the system. The controller is validated in a realistic simulation, together with a kinematic inversion algorithm. The paper also introduces a soft robot capable of three-dimensional motion, that we use to experimentally validate our control strategy.
In recent years, a clear trend toward simplification emerged in the development of robotic hands. The use of soft robotic approaches has been a useful tool in this prospective, enabling complexity reduction by embodying part of grasping intelligence in the hand mechanical structure. Several hand prototypes designed according to such principles have accomplished good results in terms of grasping simplicity, robustness, and reliability. Among them, the Pisa/IIT SoftHand demonstrated the feasibility of a large variety of grasping tasks, by means of only one actuator and an opportunely designed tendon-driven differential mechanism. However, the use of a single degree of actuation prevents the execution of more complex tasks, like fine preshaping of fingers and in-hand manipulation. While possible in theory, simply doubling the Pisa/IIT SoftHand actuation system has several disadvantages, e.g., in terms of space and mechanical complexity. To overcome these limitations, we propose a novel design framework for tendon-driven mechanisms, in which the main idea is to turn transmission friction from a disturbance into a design tool. In this way, the degrees of actuation (DoAs) can be doubled with little additional complexity. By leveraging on this idea, we design a novel robotic hand, the Pisa/IIT SoftHand 2. We present here its design, modeling, control, and experimental validation. The hand demonstrates that by opportunely combining only two DoAs with hand softness, a large variety of grasping and manipulation tasks can be performed, only relying on the intelligence embodied in the mechanism. Examples include rotating objects with different shapes, opening a jar, and pouring coffee from a glass.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.