The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2017
Session ID : 2P1-G03
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Robot Path Training and Planning Usign LSTM Network
Masaya INOUE, Takahiro YAMASHITA, Takeshi NISHIDA
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Abstract

We propose a novel robot path planning method combining RRT and LSTM network. In this method, numerous and good paths are generated in the robot configuration space by RRT method, a small size LSTM network is trained by them. By the proposed method, the difficulty of general methods with LSTM network, i.e. “the acquisition of a large amount of training data” is overcame. Moreover, the difficulty of general random based methods, i.e. “the reproducible path generation” is enabled with high-speed.

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© 2017 The Japan Society of Mechanical Engineers
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