SIA OpenIR  > 空间自动化技术研究室
Robot Obstacle Avoidance Learning Based on Mixture Models
Zhang HW(张会文); Han XN(韩小宁); Fu ML(付明亮); Zhou WJ(周维佳)
Department空间自动化技术研究室
Source PublicationJournal of Robotics
ISSN1687-9600
2016
Volume2016Pages:1-14
Indexed ByEI
EI Accession number20164002870742
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (Grant no. 51505470) and the Dr. Startup Fund in Liaoning province (20141152).
AbstractWe 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.
Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/19199
Collection空间自动化技术研究室
Corresponding AuthorZhang HW(张会文)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Zhang HW,Han XN,Fu ML,et al. Robot Obstacle Avoidance Learning Based on Mixture Models[J]. Journal of Robotics,2016,2016:1-14.
APA Zhang HW,Han XN,Fu ML,&Zhou WJ.(2016).Robot Obstacle Avoidance Learning Based on Mixture Models.Journal of Robotics,2016,1-14.
MLA Zhang HW,et al."Robot Obstacle Avoidance Learning Based on Mixture Models".Journal of Robotics 2016(2016):1-14.
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