A Data-driven Approach for Fast Simulation of Robot Locomotion on Granular Media
A Data-driven Approach for Fast Simulation of Robot Locomotion on Granular Media
复制标题
颗粒介质上机器人运动快速仿真的数据驱动方法
DOI：
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发表时间：
2019
期刊：
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通讯作者：
Kris K. Hauser
中科院分区：
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作者：
Yifan Zhu;Laith Abdulmajeid;Kris K. Hauser
在本文中，我们提出了一种模拟机器人在颗粒介质上运动的半经验方法。我们首先开发一个基于刚性物体和颗粒之间的粘滑行为的接触模型，然后通过进行大量的实验来学习该模型。接触模型代表颗粒基底可以作为凸体积提供的所有可能的接触扳手，我们的方法将其表示为基于优化的接触力求解器中的约束。在模拟过程中，颗粒状基材被视为允许穿透的刚性物体，接触求解器求解最大化摩擦耗散的扳手。我们表明，我们的方法能够以交互速率模拟与多种颗粒媒体的合理交互响应。
In this paper, we propose a semi-empirical approach for simulating robot locomotion on granular media. We first develop a contact model based on the stick-slip behavior between rigid objects and granular grains, which is then learned through running extensive experiments. The contact model represents all possible contact wrenches that the granular substrate can provide as a convex volume, which our method formulates as constraints in an optimization-based contact force solver. During simulation, granular substrates are treated as rigid objects that allow penetration and the contact solver solves for wrenches that maximize frictional dissipation. We show that our method is able to simulate plausible interaction response with several granular media at interactive rates.