A Smoothed Particle Hydrodynamics framework for fluid simulation in robotics
Simulation is a core component of robotics workflows that can shed light on the complex interplay between a physical body, the environment and sensory feedback mechanisms in silico. To this goal several simulation methods, originating in rigid body dynamics and in continuum mechanics have been employed, enabling the simulation of a plethora of phenomena such as rigid/soft body dynamics, fluid dynamics, muscle simulation as well as sensor and actuator dynamics. The physics engines commonly employed in robotics simulation focus on rigid body dynamics, whereas continuum mechanics methods excel on the simulation of phenomena where deformation plays a crucial role, keeping the two fields relatively separate. Here, we propose a shift of paradigm that allows for the accurate simulation of fluids in interaction with rigid bodies within the same robotics simulation framework, based on the continuum mechanics-based Smoothed Particle Hydrodynamics method. The proposed framework is useful for simulations such as swimming robots with complex geometries, robots manipulating fluids and even robots emitting highly viscous materials such as the ones used for 3D printing. Scenarios like swimming on the surface, air-water transitions, locomotion on granular media can be natively simulated within the proposed framework. Firstly, we present the overall architecture of our framework and give examples of a concrete software implementation. We then verify our approach by presenting one of the first of its kind simulation of self-propelled swimming robots with a smooth particle hydrodynamics method and compare our simulations with real experiments. Finally, we propose a new category of simulations that would benefit from this approach and discuss ways that the sim-to-real gap could be further reduced.
@article{AAB+24,
title = {A smoothed particle hydrodynamics framework for fluid simulation in robotics},
journal = {Robotics and Autonomous Systems},
volume = {185},
year = {2025},
issn = {0921-8890},
doi = {https://doi.org/10.1016/j.robot.2024.104885},
url = {https://www.sciencedirect.com/science/article/pii/S0921889024002690},
author = {Emmanouil Angelidis and Jonathan Arreguit and Jan Bender and Patrick Berggold and Ziyuan Liu and Alois Knoll and Alessandro Crespi and Auke J. Ijspeert}
}