Analytical Derivatives for Efficient Mechanical Simulations of Hybrid Soft Rigid Robots

Algorithms that use derivatives of governing equations have accelerated rigid robot simulations and improved their accuracy, enabling the modeling of complex, real-world capabilities. However, extending these methods to soft and hybrid soft-rigid robots is significantly more challenging due to the complexities in modeling continuous deformations inherent in soft bodies. A considerable number of soft robots and the deformable links of hybrid robots can be effectively modeled as slender rods. The Geometric Variable Strain (GVS) model, which employs the screw theory and the strain parameterization of the Cosserat rod, extends the rod theory to model hybrid soft-rigid robots within the same mathematical framework. Using the Recursive Newton-Euler Algorithm, we developed the analytical derivatives of the governing equations of the GVS model. These derivatives facilitate the implicit integration of dynamics and provide the analytical Jacobian of the statics residue, ensuring fast and accurate computations. We applied these derivatives to the mechanical simulations of six common robotic systems: a soft cable-driven manipulator, a hybrid serial robot, a fin-ray finger, a hybrid parallel robot, a contact scenario, and an underwater hybrid mobile robot. Simulation results demonstrate substantial improvements in computational efficiency, with speed-ups of up to three orders of magnitude. We validate the model by comparing simulations done with and without analytical derivatives. Beyond static and dynamic simulations, the techniques discussed in this paper hold the potential to revolutionize the analysis, control, and optimization of hybrid robotic systems for real-world applications.

Soft Synergies: Model Order Reduction of Hybrid Soft-Rigid Robots via Optimal Strain Parameterization

Soft robots offer remarkable adaptability and safety advantages over rigid robots, but modeling their complex, nonlinear dynamics remains challenging. Strain-based models have recently emerged as a promising candidate to describe such systems, however, they tend to be high-dimensional and time consuming. This paper presents a novel model order reduction approach for soft and hybrid robots by combining strain-based modeling with Proper Orthogonal Decomposition (POD). The method identifies optimal coupled strain basis functions -- or mechanical synergies -- from simulation data, enabling the description of soft robot configurations with a minimal number of generalized coordinates. The reduced order model (ROM) achieves substantial dimensionality reduction while preserving accuracy. Rigorous testing demonstrates the interpolation and extrapolation capabilities of the ROM for soft manipulators under static and dynamic conditions. The approach is further validated on a snake-like hyper-redundant rigid manipulator and a closed-chain system with soft and rigid components, illustrating its broad applicability. Finally, the approach is leveraged for shape estimation of a real six-actuator soft manipulator using only two position markers, showcasing its practical utility. This POD-based ROM offers significant computational speed-ups, paving the way for real-time simulation and control of complex soft and hybrid robots.

Soft Robots Modeling: a Literature Unwinding

The robotics community has seen an exponential growth in the level of complexity of the theoretical tools presented for the modeling of soft robotics devices. Different solutions have been presented to overcome the difficulties related to the modeling of soft robots, often leveraging on other scientific disciplines, such as continuum mechanics and computer graphics. These theoretical foundations are often taken for granted and this lead to an intricate literature that, consequently, has never been the subject of a complete review. Withing this scenario, the objective of the presented paper is twofold. The common theoretical roots that relate the different families of modeling techniques are highlighted, employing a unifying language that ease the analysis of their main connections and differences. Thus, the listing of the approaches naturally follows and a complete, untangled, review of the main works on the field is finally provided.

SoRoSim: a MATLAB Toolbox for Soft Robotics Based on the Geometric Variable-strain Approach

Soft robotics has been a trending topic within the robotics community for almost two decades. However, the available tools for the community to model and analyze soft robotics artifacts are still limited. This paper presents the development of a user-friendly MATLAB toolbox, SoRoSim, that integrates the Geometric Variable Strain model to facilitate the modeling, analysis, and simulation of hybrid rigid-soft open-chain robotic systems. The toolbox implements a recursive, two-level nested quadrature scheme to solve the model. We demonstrate several examples and applications to validate the toolbox and explore the toolbox's capabilities to efficiently model a vast range of robotic systems, considering different actuators and external loads, including the fluid-structure interactions. We think that the soft-robotics research community will benefit from the SoRoSim toolbox for a wide variety of applications.