Discrete Cosserat Approach for Multi-Section Soft Robots Dynamics

In spite of recent progress, soft robotics still suffers from a lack of unified modeling framework. Nowadays, the most adopted model for the design and control of soft robots is the piece-wise constant curvature model, with its consolidated benefits and drawbacks. In this work, an alternative model for multisection soft robots dynamics is presented based on a discrete Cosserat approach, which, not only takes into account shear and torsional deformations, essentials to cope with out-of-plane external loads, but also inherits the geometrical and mechanical properties of the continuous Cosserat model, making it the natural soft robotics counterpart of the traditional rigid robotics dynamics model. The soundness of the model is demonstrated through extensive simulation and experimental results for both plane and out-of-plane motions.Comment: 13 pages, 9 figure

The Soft Robotics Buzz

Dr. Bergeles\u27 work has close ties to other soft robotics research around the world, including here at Purdue. “Soft Robotics\u27\u27 is a buzzword these days, and that has both positive and negative consequences on the field. On one hand, novel research in the area of surgical robotics is paving the way for things like tele-surgeries. On the other, it has become very competitive to get into the field and some may be mis-using the term “soft robotics” because its clout has outgrown its understanding in everyday society. This artifact is a Canva presentation offering background on the development, market, challenges, and future opportunities for soft robotics

Soft Robotics for Chemists

Soft robots: A methodology based on embedded pneumatic networks (PneuNets) is described that enables large-amplitude actuations in soft elastomers by pressurizing embedded channels. Examples include a structure that can change its curvature from convex to concave, and devices that act as compliant grippers for handling fragile objects (e.g., a chicken egg).Chemistry and Chemical Biolog

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video: https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page: https://github.com/yuanming-hu/ChainQuee

Toward a Common Framework and Database of Materials for Soft Robotics

To advance the field of soft robotics, a unified database of material constitutive models and experimental characterizations is of paramount importance. This will facilitate the use of finite element analysis to simulate their behavior and optimize the design of soft-bodied robots. Samples from seventeen elastomers, namely Body Double™ SILK, Dragon Skin™ 10 MEDIUM, Dragon Skin 20, Dragon Skin 30, Dragon Skin FX-Pro, Dragon Skin FX-Pro + Slacker, Ecoflex™ 00–10, Ecoflex 00–30, Ecoflex 00–50, Rebound™ 25, Mold Star™ 16 FAST, Mold Star 20T, SORTA-Clear™ 40, RTV615, PlatSil® Gel-10, Psycho Paint®, and SOLOPLAST 150318, were subjected to uniaxial tensile tests according to the ASTM D412 standard. Sample preparation and tensile test parameters are described in detail. The tensile test data are used to derive parameters for hyperelastic material models using nonlinear least-squares methods, which are provided to the reader. This article presents the mechanical characterization and the resulting material properties for a wide set of commercially available hyperelastic materials, many of which are recognized and commonly applied in the field of soft robotics, together with some that have never been characterized. The experimental raw data and the algorithms used to determine material parameters are shared on the Soft Robotics Materials Database GitHub repository to enable accessibility, as well as future contributions from the soft robotics community. The presented database is aimed at aiding soft roboticists in designing and modeling soft robots while providing a starting point for future material characterizations related to soft robotics research