Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

TL;DR

This paper introduces a collision-aware simulation method for soft robots that uses geometric optimization to efficiently handle collision detection and response, incorporating hyperelastic material properties for realistic behavior.

Contribution

The paper presents a novel geometric optimization-based simulator for soft robots that improves efficiency and realism over traditional FEA-based methods, including adaptive remeshing for large volume changes.

Findings

Lower computational cost compared to FEA-based methods

Effective collision detection and response in soft robot simulation

Improved convergence with adaptive remeshing

Abstract

Soft robots can safely interact with environments because of their mechanical compliance. Self-collision is also employed in the modern design of soft robots to enhance their performance during different tasks. However, developing an efficient and reliable simulator that can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometry-based objectives. The collision-free body of a soft robot can be obtained by minimizing the geometry-based objective function. Unlike the FEA-based physical simulation, the proposed pipeline performs a much lower computational cost. Moreover, adaptive remeshing is applied to achieve the improvement of the convergence when dealing with soft robots that have large volume variations. Experimental tests are conducted on different soft robots to verify the performance of our approach.