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Neural network-based construction of inverse kinematics model for serial redundant manipulators

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Abstract

Solving the inverse kinematics of redundant manipulators is difficult, because knowledge of the manipulators and their evaluation functions is required. To solve this problem, we propose a novel method of enabling a neural network model to learn the inverse kinematics. The method achieves learning independent of the structure of the evaluation function, by combining multiple neural network models. The method can obtain the neural network models of the inverse kinematics via an automatic calculation process using only training data, which consist of the postures, end-points, and evaluation values. In this paper, the algorithm used by the method and its background is explained, and the effectiveness of the method is validated by a numerical simulation.

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Authors and Affiliations

  1. Graduate School of Engineering, University of Hyogo, Himeji, Japan

    Hideaki Takatani, Nozomu Araki, Takao Sato & Yasuo Konishi

Authors
  1. Hideaki Takatani
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  2. Nozomu Araki
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  3. Takao Sato
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  4. Yasuo Konishi
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Correspondence to Hideaki Takatani.

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This work was presented in part at the 24th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 23–25, 2019.

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Takatani, H., Araki, N., Sato, T. et al. Neural network-based construction of inverse kinematics model for serial redundant manipulators. Artif Life Robotics 24, 487–493 (2019). https://doi.org/10.1007/s10015-019-00552-y

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  • DOI: https://doi.org/10.1007/s10015-019-00552-y

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