Robot Obstacle Avoidance Learning Based on Mixture Models
Joint Authors
Zhou, Weijia
Zhang, Huiwen
Han, Xiaoning
Fu, Mingliang
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-07
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
We briefly surveyed the existing obstacle avoidance algorithms; then a new obstacle avoidance learning framework based on learning from demonstration (LfD) is proposed.
The main idea is to imitate the obstacle avoidance mechanism of human beings, in which humans learn to make a decision based on the sensor information obtained by interacting with environment.
Firstly, we endow robots with obstacle avoidance experience by teaching them to avoid obstacles in different situations.
In this process, a lot of data are collected as a training set; then, to encode the training set data, which is equivalent to extracting the constraints of the task, Gaussian mixture model (GMM) is used.
Secondly, a smooth obstacle-free path is generated by Gaussian mixture regression (GMR).
Thirdly, a metric of imitation performance is constructed to derive a proper control policy.
The proposed framework shows excellent generalization performance, which means that the robots can fulfill obstacle avoidance task efficiently in a dynamic environment.
More importantly, the framework allows learning a wide variety of skills, such as grasp and manipulation work, which makes it possible to build a robot with versatile functions.
Finally, simulation experiments are conducted on a Turtlebot robot to verify the validity of our algorithms.
American Psychological Association (APA)
Zhang, Huiwen& Han, Xiaoning& Fu, Mingliang& Zhou, Weijia. 2016. Robot Obstacle Avoidance Learning Based on Mixture Models. Journal of Robotics،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1110286
Modern Language Association (MLA)
Zhang, Huiwen…[et al.]. Robot Obstacle Avoidance Learning Based on Mixture Models. Journal of Robotics No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1110286
American Medical Association (AMA)
Zhang, Huiwen& Han, Xiaoning& Fu, Mingliang& Zhou, Weijia. Robot Obstacle Avoidance Learning Based on Mixture Models. Journal of Robotics. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1110286
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110286