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- W2535000673 abstract "This paper deals with trajectory planning and motion learning for a redundant manipulator. We have proposed a hierarchical trajectory planning method by a virus-evolutionary genetic algorithm. Furthermore, we have applied a neural network for the motion learning of trajectories generated by the hierarchical trajectory planning method. This paper proposes a primitive motion planning method by using outputs of the learned neural network. The simulation results show that the primitive motion planning method can reduce computational cost and quickly obtain collision-free trajectories of a redundant manipulator." @default.
- W2535000673 created "2016-10-28" @default.
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- W2535000673 date "2002-11-27" @default.
- W2535000673 modified "2023-09-26" @default.
- W2535000673 title "Motion learning for redundant manipulator with structured intelligence" @default.
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- W2535000673 doi "https://doi.org/10.1109/iecon.1998.723953" @default.
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