Toward a Data-Driven Template Model for Quadrupedal Locomotion
Toward a Data-Driven Template Model for Quadrupedal Locomotion
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DOI：
10.1109/lra.2022.3184007
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发表时间：
2022-07
影响因子：
5.2
通讯作者：
Randall T. Fawcett;K. Afsari;A. Ames;K. Hamed
中科院分区：
文献类型：
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作者：
Randall T. Fawcett;K. Afsari;A. Ames;K. Hamed
研究了一种数据驱动的四足机器人轨迹规划模板模型。许多国家的最先进的方法涉及使用降阶模型，主要是由于计算的易处理性。在这项工作中的轨迹规划方法的精神借鉴了行为系统理论领域的最新进展。在这里，我们的目标是利用知名的模板模型的知识来构建一个数据驱动的模型，使我们能够获得一个信息丰富的降阶模型。特别是，这项工作考虑了类似于单刚体模型的输入输出状态，并继续开发系统的数据驱动表示，然后在预测控制框架中使用该表示来规划四足动物的轨迹。最佳轨迹被传递到一个低级别和非线性的基于模型的控制器进行跟踪。初步的实验结果，建立这种分层控制方法的有效性，小跑和行走步态的高维四足机器人在未知的地形和干扰的存在下。
This work investigates a data-driven template model for trajectory planning of dynamic quadrupedal robots. Many state-of-the-art approaches involve using a reduced-order model, primarily due to computational tractability. The spirit of the trajectory planning approach in this work draws on recent advancements in the area of behavioral systems theory. Here, we aim to capitalize on the knowledge of well-known template models to construct a data-driven model, enabling us to obtain an information rich reduced-order model. In particular, this work considers input-output states similar to that of the single rigid body model and proceeds to develop a data-driven representation of the system, which is then used in a predictive control framework to plan a trajectory for quadrupeds. The optimal trajectory is passed to a low-level and nonlinear model-based controller to be tracked. Preliminary experimental results are provided to establish the efficacy of this hierarchical control approach for trotting and walking gaits of a high-dimensional quadrupedal robot on unknown terrains and in the presence of disturbances.