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Open AccessArticle

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

by Shaokun Jin 1,2 and Yongsheng Ou 1,3,4,*
1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 100049, China
3
Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
4
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5279; https://doi.org/10.3390/app9245279
Received: 23 October 2019 / Revised: 27 November 2019 / Accepted: 29 November 2019 / Published: 4 December 2019
In order to enable robots to be more intelligent and flexible, one way is to let robots learn human control strategy from demonstrations. It is a useful methodology, in contrast to traditional preprograming methods, in which robots are required to show generalizing capacity in similar scenarios. In this study, we apply learning from demonstrations on a wheeled, inverted pendulum, which realizes the balance controlling and trajectory following simultaneously. The learning model is able to map the robot position and pose to the wheel speeds, such that the robot regulated by the learned model can move in a desired trajectory and finally stop at a target position. Experiments were undertaken to validate the proposed method by testing its capacity of path following and balance guaranteeing. View Full-Text
Keywords: learning from demonstration; robot control; wheeled inverted pendulum; stability analysis; path following; external perturbations learning from demonstration; robot control; wheeled inverted pendulum; stability analysis; path following; external perturbations
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Jin, S.; Ou, Y. A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations. Appl. Sci. 2019, 9, 5279.

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