期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 5, 期 1, 页码 119-126出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2947001
关键词
Humanoid robots; Optimal control; Dynamics; Optimization; Task analysis; Computational modeling; Optimization and Optimal Control; Humanoid Robots; Motion Control
类别
资金
- JSPS KAKENHI [JP16H06565, JP19K21540, JP18H06472]
- JSPS KAKENHI under Grant MIC-SCOPE [182107105]
- NEDO, JST-Mirai Program, Japan [JPMJMI18B8]
In this letter, we develop an optimal control framework that takes the full-body dynamics of a humanoid robot into account. Employing full-body dynamics has been explored in, especially, an online optimal control approach known as model predictive control (MPC). However, whole-body motions cannot be updated in a short period of time due to MPC's large computational burden. Thus, MPC has generally been evaluated with a physical humanoid robot in a limited range of tasks where high-speed motion executions are unnecessary. To cope with this problem, our multi-timescale control framework drives whole-body motions with a computationally efficient hierarchical MPC. Meanwhile, a biologically inspired controller maintains the robot's posture for a very short control period. We evaluated our framework in skating tasks with simulated and real lower-body humanoids that have rollers on the feet. Our simulated robot generated various agile motions such as jumping over a bump and flipping down from a cliff in real time. Our real lower-body humanoid also successfully generated a movement down a slope.
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