A Massively-Parallel 3D Simulator for Soft and Hybrid Robots

TL;DR

This paper introduces Cronos, an open-source, massively parallel 3D simulator capable of real-time soft and hybrid robot simulation, leveraging GPU acceleration for high performance and versatility in modeling complex nonlinear materials.

Contribution

The work presents a novel GPU-based parallel simulation engine for soft and hybrid robots, enabling accurate, real-time simulation of complex nonlinear materials and heterogeneous structures.

Findings

Achieves over 1 billion elements per second on consumer GPUs.

Validates physical accuracy against theoretical and empirical data.

Supports diverse material configurations within a single simulation framework.

Abstract

Simulation is an important step in robotics for creating control policies and testing various physical parameters. Soft robotics is a field that presents unique physical challenges for simulating its subjects due to the nonlinearity of deformable material components along with other innovative, and often complex, physical properties. Because of the computational cost of simulating soft and heterogeneous objects with traditional techniques, rigid robotics simulators are not well suited to simulating soft robots. Thus, many engineers must build their own one-off simulators tailored to their system, or use existing simulators with reduced performance. In order to facilitate the development of this exciting technology, this work presents an interactive-speed, accurate, and versatile simulator for a variety of types of soft robots. Cronos, our open-source 3D simulation engine, parallelizes a mass-spring model for ultra-fast performance on both deformable and rigid objects. Our approach is applicable to a wide array of nonlinear material configurations, including high deformability, volumetric actuation, or heterogenous stiffness. This versatility provides the ability to mix materials and geometric components freely within a single robot simulation. By exploiting the flexibility and scalability of nonlinear Hookean mass-spring systems, this framework simulates soft and rigid objects via a highly parallel model for near real-time speed. We describe an efficient GPU CUDA implementation, which we demonstrate to achieve computation of over 1 billion elements per second on consumer-grade GPU cards. Dynamic physical accuracy of the system is validated by comparing results to Euler-Bernoulli beam theory, natural frequency predictions, and empirical data of a soft structure under large deformation.