Project Overview:

This project addresses the challenge of efficiently simulating soft and hybrid rigid-soft robots for design and control applications. Inspired by biological systems like cheetahs, which use their flexible spine for enhanced locomotion, the simulator enables exploration of bio-inspired designs that incorporate soft, deformable components alongside traditional rigid elements.

Key Features:

• FEM-based deformation model for accurate simulation of soft materials with varying properties
• GPU-accelerated parallel solver achieving real-time performance for complex models
• Unified treatment of rigid and soft body dynamics within a single simulation framework
• Contact model specifically designed for soft-rigid interactions
• Open-source implementation with Python and C++ APIs

Technical Implementation:

The core of the simulator is built on a finite element method (FEM) approach using a corotational formulation for large deformations. To achieve real-time performance, the computational bottleneck of solving large sparse linear systems is addressed through a custom GPU-accelerated preconditioned conjugate gradient solver.

The system incorporates a unified constraint-based formulation that handles both soft body deformation and rigid body dynamics, allowing for seamless simulation of hybrid systems. A specialized contact model accounts for the unique challenges of soft material contact, including friction and self-collision.

Applications:

The simulator has been applied to several bio-inspired robotic designs, including:

1. Cheetah-inspired robot: A quadrupedal robot with a flexible spine, demonstrating how controlled spinal flexibility can enhance running efficiency and maneuverability.

2. Soft grippers: Simulation of pneumatically actuated soft grippers for delicate object manipulation.

3. Tensegrity robots: Robots that use a combination of rigid struts and flexible tensile elements for locomotion and adaptation.

Impact:

The simulator enables researchers to explore the design space of soft and hybrid robots more efficiently, testing concepts virtually before physical prototyping. The ability to run simulations in real-time facilitates the development of control algorithms and the application of learning-based approaches to this challenging domain.