期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 38, 期 9, 页码 1063-1097出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364919859425
关键词
bipedal robots; machine learning; trajectory optimization; zero dynamics
类别
资金
- NSF [CPS-1239037, NRI-1525006, ECCS 1808051]
- Toyota Research Institute [TRI-N021515]
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.
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