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 exploitatio...

https://journals.sagepub.com/doi/pdf/10.1177/0278364919897292

Model-based dynamic feedback control of a planar soft robot: trajectory tracking and interaction with the environment:

Obtaining an accurate mechanical model of a soft deformable robot compatible with the computation time imposed by robotic applications is often considered an unattainable goal. This paper should invert this idea. The proposed methodology offers the possibility to dramatically reduce the size and the online computation time of a finite element model (FEM) of a soft robot. After a set of expensive offline simulations based on the whole model, we apply snapshot-proper orthogonal decomposition to sharply reduce the number of state variables of the soft-robot model. To keep the computational efficiency, hyperreduction is used to perform the integration on a reduced domain. The method allows to tune the error during the two main steps of complexity reduction. The method handles external loads (contact, friction, gravity, etc.) with precision as long as they are tested during the offline simulations. The method is validated on two very different examples of FEMs of soft robots and on one real soft robot. It enables acceleration factors of more than 100, while saving accuracy, in particular compared to coarsely meshed FEMs and provides a generic way to control soft robots.

/pdf/fast-generic-and-reliable-control-and-simulation-of-soft-4690ejwgpu.pdf

Fast, Generic, and Reliable Control and Simulation of Soft Robots Using Model Order Reduction

Inspired by nature, soft robots promise disruptive advances in robotics. Soft robots are naturally compliant and exhibit nonlinear behavior, which makes their study challenging. No unified framework exists to control these robots, especially when considering their dynamics. This letter proposes a methodology to study this type of robots around a stable equilibrium point. It can make the robot converge faster and with reduced oscillations to a desired equilibrium state. Using computational mechanics, a large-scale dynamic model of the robot is obtained and model reduction algorithms enable the design of a low-order controller and observer. A real robot is used to demonstrate the interest of the results.

/pdf/control-design-for-soft-robots-based-on-reduced-order-model-4yc4q7b3hu.pdf

Control Design for Soft Robots Based on Reduced-Order Model

Soft robots are primarily composed of soft materials that can allow for mechanically robust maneuvers that are not typically possible with conventional rigid robotic systems. However, owing to the current limitations in simulation, design and control of soft robots often involve a painstaking trial. With the ultimate goal of a computational framework for soft robotic engineering, here we introduce a numerical simulation tool for limbed soft robots that draws inspiration from discrete differential geometry based simulation of slender structures. The simulation incorporates an implicit treatment of the elasticity of the limbs, inelastic collision between a soft body and rigid surface, and unilateral contact and Coulombic friction with an uneven surface. The computational efficiency of the numerical method enables it to run faster than real-time on a desktop processor. Our experiments and simulations show quantitative agreement and indicate the potential role of predictive simulations for soft robot design.

/pdf/dynamic-simulation-of-articulated-soft-robots-1v264omawk.pdf

Dynamic simulation of articulated soft robots.

This paper presents a computationally efficient method to model and simulate soft robots. Finite element methods enable us to simulate and control soft robots, but require us to work with a large dimensional system. This limits their use in real-time simulation and makes those methods less suitable for control design tools. Using model order reduction, it is possible to create a reduced order system for building controllers and observers. Model reduction errors are taken into account in the design of the low-order feedback, and it is then applied to the large dimensional, unreduced model. The control architecture is based on a linearized model of the robot and enables the control of the robot around this equilibrium point. To show the performance of this control method, pose-to-pose and trajectory tracking experiments are conducted on a pneumatically actuated soft arm. The soft arm has 12 independent interior cavities that can be pressurized and cause the arm to move in three dimensions. The arm is made of a rubber material and is casted through a lost-wax fabrication technique.

https://hal.archives-ouvertes.fr/hal-02078809/document

Dynamically Closed-Loop Controlled Soft Robotic Arm using a Reduced Order Finite Element Model with State Observer

We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent real-time constraints imposed by control algorithms, a reduced-order modeling strategy is proposed that allows to minimize the amount of online CPU cost. Instead, an offline training procedure is proposed that allows to determine a sort of response surface that characterizes the response of the robot. Contrarily to existing strategies, the proposed methodology allows for a fully non-linear modeling of the soft material in a hyperelastic setting as well as a fully non-linear kinematic description of the movement without any restriction nor simplifying assumption. Examples of different configurations of the robot were analyzed that show the appeal of the method.

/pdf/reduced-order-modeling-of-soft-robots-3a22i05gpp.pdf

J. Vicente Aguado

Papers

Reduced-order modeling of soft robots