A Self-Learning Algorithm for Primitive Actions’ Affordances of Nuclear Power Plant Maintenance Robot

Chun Yang Liu; Jing Wei Zhang; Xue Feng Zheng; Xu Yan Tu

doi:10.4028/www.scientific.net/AMM.701-702.219

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 701-702A Self-Learning Algorithm for Primitive Actions’...

A Self-Learning Algorithm for Primitive Actions’ Affordances of Nuclear Power Plant Maintenance Robot

Abstract:

In this paper, an algorithm concerning the primitive action affordances of learning nuclear power plant maintenance robot is presented. The algorithm generates a random matching data set through a new matching method, which is utilized for the selection of object operation, with the matching rate improved by trial and error, and then the attempt number for a successful operation is reduced. In the end, simulation is conducted to verify the feasibility and correctness of the proposed algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 701-702)

Pages:

219-222

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.701-702.219

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Online since:

December 2014

Authors:

Chun Yang Liu*, Jing Wei Zhang, Xue Feng Zheng, Xu Yan Tu

Keywords:

Affordance, Matching Rate, Primitive Actions, Robot, Self-Learning Method

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* - Corresponding Author

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