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This article details the integration of Resistive Force Theory (RFT) into the MuJoCo physics engine to simulate robot locomotion on granular substrates like sand without modeling individual grains. The study validates that this approximation method accurately predicts walking distance and foot sinkage for a hexapod robot, offering a computationally efficient tool for designing robots capable of traversing soft terrain.
Simulating Robotic Locomotion in Sand: Resistive Force Theory in an Open-Source Physics Engine
arXiv:2606.19504v2 Announce Type: replace Abstract: Recent advancements in Resistive Force Theory (RFT) enable approximation of ground reaction forces for locomotion in sand without the computational expense of modeling interactions with individual grains. However, these tools have been absent in 3D physics engines commonly used for robot simulation. We explore if resistive force approximations are sufficient, when integrated with standard dynamics calculations, to provide a stable substrate for a freely walking robot. To determine this, we implement 3D Granular Resistive Force Theory (3D RFT) in a physics simulation engine, MuJoCo. We verify simulations in multiple scenarios to demonstrate that key trends due to end effector shape, speed, and loading are preserved. Our implementation predicts both walking distance and foot sinkage of a 12-Degree of Freedom hexapod robot within 7\% of experiments in sand. While RFT has inherent approximations, the open source tool described here has potential to help develop new and improved robot designs to traverse granular media substrates.
Abstract:Recent advancements in Resistive Force Theory (RFT) enable approximation of ground reaction forces for locomotion in sand without the computational expense of modeling interactions with individual grains. However, these tools have been absent in 3D physics engines commonly used for robot simulation. We explore if resistive force approximations are sufficient, when integrated with standard dynamics calculations, to provide a stable substrate for a freely walking robot. To determine this, we implement 3D Granular Resistive Force Theory (3D RFT) in a physics simulation engine, MuJoCo. We verify simulations in multiple scenarios to demonstrate that key trends due to end effector shape, speed, and loading are preserved. Our implementation predicts both walking distance and foot sinkage of a 12-Degree of Freedom hexapod robot within 7\% of experiments in sand. While RFT has inherent approximations, the open source tool described here has potential to help develop new and improved robot designs to traverse granular media substrates.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2606.19504 [cs.RO]
(or arXiv:2606.19504v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.19504 Focus to learn more arXiv-issued DOI via DataCite
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