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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