Confidence-based roadmap using Gaussian process regression for a robot control
To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the st...
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Published in | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 661 - 666 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2014
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Abstract | To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the state space is constructed based on reachability between node pairs, and the motion is planned using the graph. However, it requires an explicit model of a controlled target. In this research, we propose a motion planning method in which a system model is estimated by using Gaussian process regression. We apply our method to the control of an actual robot. Experimental results show that the control of the robot can be achieved by the proposed motion planning method. |
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AbstractList | To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the state space is constructed based on reachability between node pairs, and the motion is planned using the graph. However, it requires an explicit model of a controlled target. In this research, we propose a motion planning method in which a system model is estimated by using Gaussian process regression. We apply our method to the control of an actual robot. Experimental results show that the control of the robot can be achieved by the proposed motion planning method. |
Author | Urai, Kenji Nakamura, Yutaka Nakata, Yoshihiro Okadome, Yuya Ishiguro, Hiroshi |
Author_xml | – sequence: 1 givenname: Yuya surname: Okadome fullname: Okadome, Yuya email: okadome.yuya@irl.sys.es.osaka-u.ac.jp organization: Dept. of Syst. Innovation, Osaka Univ., Toyonaka, Japan – sequence: 2 givenname: Yutaka surname: Nakamura fullname: Nakamura, Yutaka email: nakamaura@irl.sys.es.osaka-u.ac.jp organization: Dept. of Syst. Innovation, Osaka Univ., Toyonaka, Japan – sequence: 3 givenname: Kenji surname: Urai fullname: Urai, Kenji organization: Dept. of Syst. Innovation, Osaka Univ., Toyonaka, Japan – sequence: 4 givenname: Yoshihiro surname: Nakata fullname: Nakata, Yoshihiro organization: Dept. of Syst. Innovation, Osaka Univ., Toyonaka, Japan – sequence: 5 givenname: Hiroshi surname: Ishiguro fullname: Ishiguro, Hiroshi organization: Dept. of Syst. Innovation, Osaka Univ., Toyonaka, Japan |
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Snippet | To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion... |
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Title | Confidence-based roadmap using Gaussian process regression for a robot control |
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