用简化模型模拟机器人在沙地行走，精度达实验值的80%以上。
Simulating Robotic Locomotion in Sand: Resistive Force Theory in an Open-Source Physics Engine
- 将3D颗粒物阻力理论嵌入物理引擎MuJoCo，替代真实沙粒建模。
- 仿真结果与实验对比，足部下陷深度和行走距离误差小于20%。
- 适合机器人设计者快速测试沙地行进性能，无需复杂计算。
最近的阻力力理论（RFT）进展使得无需模拟单个沙粒即可估算沙地运动中的地面反作用力，从而大幅降低计算成本。然而，这类工具尚未集成到常用的3D机器人仿真物理引擎中。本文在物理引擎MuJoCo中实现了3D颗粒物阻力力理论（3D RFT），验证了其在多种场景下的有效性，表明末端执行器形状、速度和负载带来的关键趋势得以保留。该方法对12自由度六足机器人在沙地上的行走距离和足部下陷深度的预测值与实际实验结果相差不超过20%。尽管RFT存在固有近似，但本研究提供的开源工具可助力新型机器人在颗粒介质环境中的设计优化。
原文摘要 · Abstract (English)
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 walking distance and foot sinkage of a 12-Degree of Freedom hexapod robot within 20\% 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.
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