Inverse kinematics learning by modular architecture neural networks

نویسندگان

  • Eimei Oyama
  • Susumu Tachi
چکیده

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not p a y enough attention to the discontinuity of the inverse kinematics system of typical robot arms with joint limits. The inverse kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multi-layer neuml network to approximate such a function, a correct inverse kinematics model for the end-effector’s overall position and orientation cannot be obtained b y the conventional methods. In order to overcome the drawbacks of the inverse kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the inverse kinematics model learning.

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تاریخ انتشار 1999