Motion Imitation Robot Based on Artificial Neural Network with Minimization of Restrictions on Degrees of Freedom

초록

For a robot to imitate human motions, each human joint must be mapped onto the robot. In the mapping process of the NAO robot, there is a degrees-of-freedom mismatch problem between a human arm with six degrees of freedom and a robot arm with four degrees of freedom. During the collection of information on robot joint angles from human joint angles, some information on the six degrees of freedom is absent, resulting in inaccurate or erroneous movements of the robot, requiring additional calculations. In this paper, we propose a robot technology that imitates human movements by minimizing the degrees-of-freedom constraints without missing information using an artificial neural network. To verify the proposed approach, a manually measured answer dataset and an inverse kinematics answer dataset were created for each of the 919 motion frames of the human right-arm and upper-body motions. The robot imitation performance was stable through a 10-fold verification with the manually measured and inverse kinematics answer datasets for the right-arm motion imitations of 3.245(degrees) and 4.24(degrees) and the upper-body imitations of 5.10(degrees) and 4.82(degrees). In addition, as the trends of the robot prediction motion signal graph were similar to those of the answer motion signal graph, the proposed approach demonstrated a steady imitation performance.

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