Combining trajectory optimization, supervised machine learning, and model structure for mitigating the curse of dimensionality in the control of bipedal robots
Combining trajectory optimization, supervised machine learning, and model structure for mitigating the curse of dimensionality in the control of bipedal robots
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DOI：
10.1177/0278364919859425
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发表时间：
2019-07-08
影响因子：
9.2
通讯作者：
Grizzle, Jessy
中科院分区：
文献类型：
--
作者：
Da, Xingye;Grizzle, Jessy
为了克服高维双足模型所施加的障碍，我们嵌入一个稳定的行走运动在一个有吸引力的低维表面的系统的状态空间。该过程从轨迹优化开始，以设计高维模型的开环周期性行走运动，然后向该解决方案添加一组精心选择的模型的附加开环轨迹，这些轨迹转向标称运动。轨迹的一个缺点是，它们提供的关于如何响应干扰的信息很少。为了解决这个缺点，监督机器学习被用来提取开环轨迹的低维状态变量实现。周期轨道现在是低维状态变量模型的吸引子，但在全阶系统中不具有吸引力。然后，我们使用与双足机器人相关的机械模型的特殊结构，以这种方式将低维模型嵌入到原始模型中，使得所需的步行运动局部指数稳定。设计程序首先开发的常微分方程，并说明了一个简单的模型。该方法随后扩展到一类混合模型，然后实现实验上的Atrias系列3D双足机器人。
To overcome the obstructions imposed by high-dimensional bipedal models, we embed a stable walking motion in an attractive low-dimensional surface of the system's state space. The process begins with trajectory optimization to design an open-loop periodic walking motion of the high-dimensional model and then adding to this solution a carefully selected set of additional open-loop trajectories of the model that steer toward the nominal motion. A drawback of trajectories is that they provide little information on how to respond to a disturbance. To address this shortcoming, supervised machine learning is used to extract a low-dimensional state-variable realization of the open-loop trajectories. The periodic orbit is now an attractor of the low-dimensional state-variable model but is not attractive in the full-order system. We then use the special structure of mechanical models associated with bipedal robots to embed the low-dimensional model in the original model in such a manner that the desired walking motions are locally exponentially stable. The design procedure is first developed for ordinary differential equations and illustrated on a simple model. The methods are subsequently extended to a class of hybrid models and then realized experimentally on an Atrias-series 3D bipedal robot.