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题名: Robot Obstacle Avoidance Learning Based on Mixture Models
作者: Zhang HW(张会文); Han XN(韩小宁); Fu ML(付明亮); Zhou WJ(周维佳)
作者部门: 空间自动化技术研究室
刊名: Journal of Robotics
出版日期: 2016
卷号: 2016, 页码:1-14
收录类别: EI
产权排序: 1
项目资助者: National Natural Science Foundation of China (Grant no. 51505470) and the Dr. Startup Fund in Liaoning province (20141152).
摘要: 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.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/19199
Appears in Collections:空间自动化技术研究室_期刊论文

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Recommended Citation:
Zhang HW,Han XN,Fu ML,et al. Robot Obstacle Avoidance Learning Based on Mixture Models[J]. Journal of Robotics,2016,2016:1-14.
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